From 2af5358f4fc08940252ac8a4e3dc6a620cfc3833 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Thu, 28 May 2026 18:09:37 +0100 Subject: [PATCH 1/9] Add Bayesian experimentation triptych --- .../assurance_planning_simulation.ipynb | 1387 ++++++++++++++ .../assurance_planning_simulation.myst.md | 530 +++++ .../multiple_experiments_meta_analysis.ipynb | 1702 +++++++++++++++++ ...multiple_experiments_meta_analysis.myst.md | 601 ++++++ .../sensitivity_unmeasured_confounding.ipynb | 1149 +++++++++++ ...sensitivity_unmeasured_confounding.myst.md | 437 +++++ examples/references.bib | 89 + 7 files changed, 5895 insertions(+) create mode 100644 examples/causal_inference/assurance_planning_simulation.ipynb create mode 100644 examples/causal_inference/assurance_planning_simulation.myst.md create mode 100644 examples/causal_inference/multiple_experiments_meta_analysis.ipynb create mode 100644 examples/causal_inference/multiple_experiments_meta_analysis.myst.md create mode 100644 examples/causal_inference/sensitivity_unmeasured_confounding.ipynb create mode 100644 examples/causal_inference/sensitivity_unmeasured_confounding.myst.md diff --git a/examples/causal_inference/assurance_planning_simulation.ipynb b/examples/causal_inference/assurance_planning_simulation.ipynb new file mode 100644 index 000000000..f56a8e713 --- /dev/null +++ b/examples/causal_inference/assurance_planning_simulation.ipynb @@ -0,0 +1,1387 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0e1fc447", + "metadata": {}, + "source": [ + "(assurance_planning)=\n", + "# Assurance Planning via Simulation\n", + "\n", + ":::{post} May 2026\n", + ":tags: experimentation, decision analysis, power analysis, assurance, simulation\n", + ":category: intermediate, reference\n", + ":author: Nathaniel Forde\n", + ":::\n", + "\n", + "## The question planners are actually asking\n", + "\n", + "When a team approves an experiment, the question they are silently asking is whether the experiment will be useful. The frequentist quantity called \"power\" answers a different question entirely: given some specific effect size we have not observed, what is the probability the experiment crosses a fixed threshold? The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold.\n", + "\n", + "Assurance is the Bayesian alternative, and its construction is mechanical: draw an effect from the prior, generate a dataset, fit the model, apply the decision rule, repeat. The result is a single number with a clear meaning: the probability that the experiment is about to conclude what its stakeholders need it to. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the conclusion is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart.\n", + "\n", + ":::{admonition} Where this lands in regulatory practice\n", + ":class: note\n", + "\n", + "The assurance framing here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\\Pr(\\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference, which is exactly the split the generative and inference models below are built on. The setting differs from product experimentation; the machinery is identical.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1d31774d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:26.957214Z", + "iopub.status.busy": "2026-05-28T16:43:26.957092Z", + "iopub.status.idle": "2026-05-28T16:43:30.823177Z", + "shell.execute_reply": "2026-05-28T16:43:30.822559Z" + } + }, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "from dataclasses import dataclass\n", + "\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pymc as pm\n", + "import seaborn as sns\n", + "\n", + "from scipy import stats\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7b97cc23", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:30.824907Z", + "iopub.status.busy": "2026-05-28T16:43:30.824712Z", + "iopub.status.idle": "2026-05-28T16:43:30.828472Z", + "shell.execute_reply": "2026-05-28T16:43:30.827952Z" + } + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "az.style.use(\"arviz-darkgrid\")\n", + "rng = np.random.default_rng(42)\n", + "RANDOM_SEED = 42" + ] + }, + { + "cell_type": "markdown", + "id": "565c8ac0", + "metadata": {}, + "source": [ + "### The scenario\n", + "\n", + "The running scenario across this notebook and its two successors is a product-experimentation question: an e-commerce team is evaluating a redesigned checkout flow. The continuous outcome is revenue per visitor; the binary outcome is conversion. We will not commit to a particular vertical or product; the structure is generic to any A/B test with one of these outcome types. What matters is that the same prior beliefs and the same decision rule will be asked of two different likelihoods. The Gaussian case will carry the argument; the Bernoulli case will confirm it travels." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "85d2d6a4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:30.829790Z", + "iopub.status.busy": "2026-05-28T16:43:30.829698Z", + "iopub.status.idle": "2026-05-28T16:43:30.832014Z", + "shell.execute_reply": "2026-05-28T16:43:30.831593Z" + } + }, + "outputs": [], + "source": [ + "@dataclass\n", + "class EffectPrior:\n", + " \"\"\"Prior beliefs about a treatment effect, on the scale of the outcome.\"\"\"\n", + "\n", + " mu: float\n", + " sigma: float\n", + "\n", + " def sample(self, rng, size=1):\n", + " return rng.normal(self.mu, self.sigma, size=size)" + ] + }, + { + "cell_type": "markdown", + "id": "541c0d41", + "metadata": {}, + "source": [ + "## Power and assurance: two questions in the same wardrobe\n", + "\n", + "Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null:\n", + "\n", + "$$\n", + "\\text{Power}(N) = P\\!\\left(\\text{reject } H_0 \\mid \\theta = \\theta_0,\\; N\\right).\n", + "$$\n", + "\n", + "The conditioning bar is the load-bearing part. The planner who has confidently named $\\theta_0$ has implicitly used up the uncertainty that motivated the experiment in the first place. Assurance refuses the substitution. Instead it integrates over the planner's prior beliefs about the effect:\n", + "\n", + "$$\n", + "\\text{Assurance}(N) = \\int P\\!\\left(\\text{decision} \\mid \\theta,\\; N\\right)\\, \\pi(\\theta)\\, d\\theta.\n", + "$$\n", + "\n", + "This is the marginal probability of a successful decision, where \"successful\" is whatever decision rule the team has agreed in advance and $\\pi(\\theta)$ is the team's prior over the effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in.\n", + "\n", + "A planning question is not a hypothesis test. When stakeholders ask \"will the experiment work?\", they are not committing to a specific effect size and asking after its detectability; they are asking after the experiment in the world they actually inhabit, with all of its uncertainty intact. The frequentist answer requires them to invent a specific world before the calculation will proceed. The Bayesian answer integrates over the worlds they already hold open.\n", + "\n", + "## The simulation engine\n", + "\n", + "Assurance is an expectation over the *prior predictive distribution*: the joint distribution of effect and data that the model generates before any real data arrive. As a Monte Carlo estimator the integral becomes a procedure in three steps, repeated for each candidate sample size:\n", + "\n", + "1. **Sample the prior predictive** of an unconditioned generative model. Each draw is a complete synthetic experiment: an effect drawn from the prior, and a dataset of size $N$ generated under it.\n", + "2. **Fit the analysis model** to each synthetic dataset and apply the decision rule.\n", + "3. **Record** the fraction of synthetic experiments in which the decision succeeded.\n", + "\n", + "The first step is the one worth dwelling on. Drawing a hypothetical truth and then generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8f2c40f5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:30.833154Z", + "iopub.status.busy": "2026-05-28T16:43:30.833072Z", + "iopub.status.idle": "2026-05-28T16:43:30.835317Z", + "shell.execute_reply": "2026-05-28T16:43:30.834629Z" + } + }, + "outputs": [], + "source": [ + "def assurance_curve(assurance_fn, N_grid, n_sims, rng, **kwargs):\n", + " \"\"\"Estimate the assurance curve by evaluating assurance at each sample size.\"\"\"\n", + " records = []\n", + " for N in N_grid:\n", + " a = assurance_fn(N=int(N), n_sims=n_sims, rng=rng, **kwargs)\n", + " records.append({\"N\": int(N), \"assurance\": a})\n", + " return pd.DataFrame(records)" + ] + }, + { + "cell_type": "markdown", + "id": "3f49b072", + "metadata": {}, + "source": [ + "## Gaussian outcome: revenue per visitor\n", + "\n", + "The Gaussian case is the cleanest place to make the mechanics explicit. Each arm produces a revenue-per-visitor outcome with known noise scale $\\sigma_{\\text{obs}}$ (we treat this as known here; in practice it is estimated from a pilot or historical data). The team is interested in the treatment effect $\\delta = \\mu_B - \\mu_A$, and holds a prior over $\\delta$ informed by past experiments in similar product surfaces. That prior is not conjured from nothing: {ref}`meta_analysis_experiments` shows how a meta-analysis of past experiments produces exactly this kind of prior, so the synthesis at the end of one experiment's lifecycle is the planning input at the start of the next.\n", + "\n", + "Two models do the work. The *generative model* is unconditioned: it places the planning prior on $\\delta$ and generates synthetic datasets, and it is the object we hand to `pm.sample_prior_predictive`. The *inference model* conditions on a dataset and returns the posterior on $\\delta$; the decision rule asks whether the posterior probability of a positive effect exceeds a threshold (0.95 below). For the assurance loop the conjugate Normal–Normal posterior on $\\delta$ admits a closed form, which lets us evaluate the decision on each synthetic experiment without running MCMC every time. We verify that closed form against the inference model's MCMC posterior at a single sample size before relying on it." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4f485b3b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:30.837015Z", + "iopub.status.busy": "2026-05-28T16:43:30.836894Z", + "iopub.status.idle": "2026-05-28T16:43:30.840087Z", + "shell.execute_reply": "2026-05-28T16:43:30.839672Z" + } + }, + "outputs": [], + "source": [ + "SIGMA_OBS = 4.0\n", + "DECISION_THRESHOLD = 0.95\n", + "BASELINE_REVENUE = 10.0\n", + "EFFECT_PRIOR = EffectPrior(mu=0.4, sigma=0.5)\n", + "\n", + "\n", + "def gaussian_generative_model(N, prior):\n", + " \"\"\"Unconditioned model: planning prior on the effect plus the data-generating likelihood.\"\"\"\n", + " with pm.Model() as model:\n", + " delta = pm.Normal(\"delta\", mu=prior.mu, sigma=prior.sigma)\n", + " pm.Normal(\"y_A\", mu=BASELINE_REVENUE, sigma=SIGMA_OBS, shape=N)\n", + " pm.Normal(\"y_B\", mu=BASELINE_REVENUE + delta, sigma=SIGMA_OBS, shape=N)\n", + " return model\n", + "\n", + "\n", + "def gaussian_two_arm_model(y_A, y_B, prior):\n", + " \"\"\"Inference model: conditions on an observed dataset and returns the posterior on the effect.\"\"\"\n", + " with pm.Model() as model:\n", + " mu_A = pm.Normal(\"mu_A\", mu=BASELINE_REVENUE, sigma=5.0)\n", + " delta = pm.Normal(\"delta\", mu=prior.mu, sigma=prior.sigma)\n", + " mu_B = pm.Deterministic(\"mu_B\", mu_A + delta)\n", + " pm.Normal(\"obs_A\", mu=mu_A, sigma=SIGMA_OBS, observed=y_A)\n", + " pm.Normal(\"obs_B\", mu=mu_B, sigma=SIGMA_OBS, observed=y_B)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "id": "be971533", + "metadata": {}, + "source": [ + "A single synthetic experiment makes the inference model concrete. We use the do-operator to fix the generative effect at a chosen value, draw one dataset from the prior predictive, and condition the inference model on it." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0a894159", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:30.841402Z", + "iopub.status.busy": "2026-05-28T16:43:30.841299Z", + "iopub.status.idle": "2026-05-28T16:43:32.422867Z", + "shell.execute_reply": "2026-05-28T16:43:32.422377Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [mu_A, delta]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 0 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + } + ], + "source": [ + "demo_true_delta = 0.6\n", + "demo_model = pm.do(gaussian_generative_model(N=200, prior=EFFECT_PRIOR), {\"delta\": demo_true_delta})\n", + "demo_draw = pm.sample_prior_predictive(draws=1, model=demo_model, random_seed=RANDOM_SEED)\n", + "y_A_demo = demo_draw.prior[\"y_A\"].values.reshape(-1)\n", + "y_B_demo = demo_draw.prior[\"y_B\"].values.reshape(-1)\n", + "\n", + "with gaussian_two_arm_model(y_A_demo, y_B_demo, EFFECT_PRIOR):\n", + " idata_gauss_demo = pm.sample(\n", + " draws=1000,\n", + " tune=1000,\n", + " chains=2,\n", + " target_accept=0.95,\n", + " random_seed=RANDOM_SEED,\n", + " progressbar=False,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0b654f32", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:32.424324Z", + "iopub.status.busy": "2026-05-28T16:43:32.424204Z", + "iopub.status.idle": "2026-05-28T16:43:33.188552Z", + "shell.execute_reply": "2026-05-28T16:43:33.188066Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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4B8qZIF2/exP5mbGwyQKFSM9V8H0SWmVs62ND161qK9ESua7p+N5J1DonYruZyt8b0dhmm22qLM/M8r5jDKuMo/DhUfZhiFQVYh/Q008/vdqGwcpqjzjiiMoSR/vQ21lkG/9rM0yFfuBsZ2/DDTf0UveaXH/99VV2MOw27cDaUm2rZAhtCG47JnYW3A6+raQyfLhIKNvIBIcqWImqpeuh/2Y73dGeJQ8m6Oeff77eeeedKtfbgamtr5W/r7322t519pzYwaedURw5cqTXp8n88ccf3lliS/lj6aFy3nnnVT5H9rxbiar93zaWxtJ8qxaJR8+ORL3uyX6cqfB4bYckuHNuQ4fs/qzHiVVrGQukrDG13f7y5csr/+6RRx7RgAEDvPtsLPZ+t89m6JAOO6A85ZRTvOfGwhr7TNkBoIUE4Q267fk888wzvVL04OvXGKzvQp8+fbzXyUr97SA+9IvdDoJsOzFixAh98803Vb5U7Xm0x5OobUhD1NY83w7G9thjj8qzufbetO2jVTUkst+YfZbszHKi2PvfXvdksfdU+I5yXesT+h4y6667bsz3u84661R73u27sabgJRn3WV/23V7bcLhU36YnYruezH2RaDTWPl08nXvuuZUnim0bbwd9duBl+8P2/Nr20Vo7WAgWZMPQ7TUaNGiQLrroIq8Cwx6LBaH2PrHvHwsR7H1kw46tajA0ILUwwXr12ZCrTJBO372J/MzY47Z92dDtiX1HWyWp/dh3rT1P9u8Wmj733HPeLNO2jhbM11V9HE/JWtfGeu805j5bY77fE7HdbOzPQCL2l8NHN3zRiCfg04Hjhs7pneUuvvhir8y7Pj0J7MNr/YBCp0C1HYLwnXxjFTyhqax96dvwBCutrIntCFh/hdDqNNshfOWVVyo/dKGsyWCvXr0qp2y3HRN7LNYEMBr297YRsgPDWMZC2/CJWIdqPP7447rlllsql21dr7rqqjqnmLf1s6Q+tPLmwgsv9J7fmkQ6ILDn38481jV2u6H9JxLxujfG46yvZD9e+4K5+uqra30f2f1ZFUToF6d92Vg/sMZiO/b2hR5kX2yPPfZYxKoTOwi137cv0XC2Q2BfpvFmB7S2YxZtPyL7sreJBEIrQuw1mz59euVBa2NvQ+rLekKGhy620xK6o2ChlJ2hDLLtvJXm18T6U4QGZLbTZH0lGiK8X05js8dw0003KRkiPVbbwbTvhNrYwUbo968N2bBAJBZ2kGLbg1D2Pg4PsJJ5n/VlB2qTJk2qXD7rrLO8oCtVt+nJ2K4ncl8kVonYp4tHz7Hgwad9FmxbWRMLAEKH2dqJua5du+qjjz7yDpYfffTRGidVsNfV3nOhJzasP6R9D6VSz7FYfz8dv3sT+ZmxisTQsMJGPTz44IPaZZddavwbe4/ZNirSoW5j7mskel0T+d6J1z5bItY5EdvNRH4GGmt/2arrLCwMHWr56aefxv0ET7pgWGWcenlYKWzoTrKJ1FTX0ujwckXbQa1t59LYwZodUIe+Ua2MM9gsOpztNIRuYPfdd9+oNwbGZmSrKxiLBzv4Dx/PbWO269qoGGtmaH8b+pzY8xHr2Hlr7tjYB56Jet2T/ThT6fFao9+63kd2f+EHiI1Zfm8VSOHBmAVfNQ3HsooyOxC0YSbhGms9Bw8eHFOjbjsbesYZZ3iBSuhrZiFTqguvGrODuvADMqtGCK3isbODNqtSJHYW2PrtxLtyLFtYtU/4ECl77k877bRa/85ej/Dv39CzwdGKNMS8pn5GybjPhggfBmkBRLpt0xtzu54K+yK1SZd9OmOf4dqCseDJaPt+C7LJgiwYs22whQi1zTZqFdPh24kZM2bUuF1OF+n23ZvIz4wdqIdX8Vx33XW1hk3GqrTsOUqkZKxrur13ErXOjb3dTPXvjWiFh/bFxcVVgv1sQzjWQNY02EKH8OZ11ssj0qxm4Weq7ODYkuNodO/evdqO6JgxY6oNuzLhwx7iPaNOvFhlXeiBgJ39s6bx0dpqq62qnKm3lN9KYaNlsyTZELXGlqjXPdmPM1Ue78Ybb+yduYvGsGHDqnw52bj/8N5s8WBfNLaDFHpQbF+MNfUlDGWVS+E9Aay02qYETxXh1VHp0NAzfAikVS6E97iwM2g2Y1Fo2b0N4YnEyvHD+00wU2V0bAfWKnvCG/FbGGI7qLWJ1Oi4Pv1DIgVVNW13knGfDRG+oxtrZVqyt+mNvV1P9r5IXdJln84qyeqa0ddYpUekg07722iG/FrlV+gwZtvuBmfKzDbJ+u5N5Gdm1KhR1V7/aKt0bUbT+vSDrK90Wtd03G+LZZ0be7uZ6t8b0bJ9l/BjkbmEY4jE3qT2xg//sZ1EG1ZjpY82Bav1QAhPt6+99tqIPUPs7FYom30ulh1qK6kMbeBoH/zwXmcm9IycCZ0qPpWEDvMIPr5YhU/VXtOUt5FYj4topgFvqES97sl+nKnyeA866KAqf18bG87RpUuXRm+KbRVgoQfUNszHDvaiFanqz7ZRqcLO9IeWtqdDz4La+o2Fsh2eaGatDA/bLICoT9PzcFY2bwd+ifpJxpDKu+++2+u5EsoOnqM50A727QhVUyPf2kTaRtVUjZKM+6wvW9elS5dWO2GSTtv0xt6uJ3tfpC7psk9n1S/R6tGjR4P+3kLXRMz0muqS9d2byM9MeGAQPhS9Nradsm1HoqTTuqbjflss69zY281U/96IRfg+wfwUOr5ItOwcTBolq8yoq89JpA3bFVdcEXFIpe2YWel4qH322Sem27czCjZMJ7QpoZXw7rzzztV2OmwnMlhOapUOd911lzc8JRVmpwz2+wkdgmThjZ1hiZWVpoaKZYc7Eb2NEvm61yRRPZxS5fHG+j6y+/vxxx8rl+uakTBWFppYX4QgC85j7UMVqbFmTVODx5uVedvrapVqdpbM7jd8SJkJrdSw5t3WIyLSDGOpItrm+RbS2GOzIfTBPmR2ObwfQ7RhG6r3WrGhVKEs2LDhCdGEIZGC/7qmhY8k0nCGmk4qJOM+6yvSMM1YZilOhW16Y27XU2FfpC7psE9nbIKEaHXo0KFaoBnLCaPwv4/393YqSNXv3kR+Zn777bcq2x9r1m4N3GNhJ7isf2tjS6V1TdX3TiLXuTG3m+nwvRGL8H2ClY3Q3iFdEI7FkX0orPdY+KwPQeEl37YjUJ/yWTtbFrqDGamU3KoVbGz1lClTKq+zg4/Ro0d7KbXNLmXDtBpztru6WA+T0LHgLVu29NYvVuFT+YY2N6yLzYzS2BL5uifzcabS4421n07458D6GMRTeH8dqziNpmFuKPt8hIu2iqI+bAfPGqZa42x7HYLBULTss209pGy68lRkQ7rCZxusKcyys4920BesErbnxqbfDm/m3xgzVWY6qxa78sorq1Xc2ZTo0b53IlVp16dvR6SKrZpmjUzGfdZXpCq3WA4MUmGb3pjb9VTYF6lLOuzTxTpja/i6WtgVy3da+OckUw7m0uG7N5GfmdCZSYMnTmLt72gz1dr7pbFPKCZ7XdPhvZPIdW7M7WY6fG80JBwrSvMejg1BOFafJy0vzzuwtQMmK++0McNWKWb9amoTPqwh1h29msZMh99ukM16YiWkoeOGbcYoGxYanC7WQhPbMFiwZ2dww0tQG1N4/w9bjrVSL5Kano9IEvEFkOjXPZJEftGlwuONFCTVJnTa6EhfVg1hZ73eeuutKtfV1aw4kkgzGNX3ua2L9QO69dZbq1WLxMoOVlI1HLOd2NDX2c461tbrxiplQofQ29DK0HDMXp/wHWPCsdrNmjVL5557bpUzw9b3woKx8KqQunbq7DMcejv16dsVaWewpp3mZNxnfUWacSrS2fhU3qY35nY9FfZFopHq+3SxvkbhlR6xnjBqzO/tZEmX795EfmbCn4tYAtjQ95p9pzT20Ntkrmu6vHcSvc6Ntd1Ml++NaIWHknlZOlOlyd5HHoX6TPVcm/CS7/ruAIfvfNT0QVprrbW84Sp2Vj68l0vwg2CVDvbz3HPPef1SLEU/9thjtd1226mxNdYGIJYDlEScZU306x5JIs8mp8LjbcyKqljZl3Loc2I79Ntuu23MtxPeHNN2oGJtqh0Nmyrb+j/FQyofrIRXeXXq1KnW96oNdbBeksGQ0sKx//3vf5X/blVo4ZULDKus2WeffebNKBhabWUHx4899litM9XVtr0I3cGuz6Qakc7Y1nawn4z7rI9IlQuxnBVOhW16Y27XU2FfJBqpvk/X0Ncolb63kyGdvnsT+ZmxqqCGhKiNtV2NJFnrmk7vnUSvc2NtN9Ple6O+99sszhXs6YRwLInitSNQ2+1Yg7377rvPq2gYO3as1/cofChRaL8UO+Czn/79+3sNxOu7YY9GffqzZIJEvO6pJNseb7jwPlRWMl+fnbTwz63dTn0agNfmo48+qrazYsOvrGrK+tZZZdXaa6/trb/1Vwi//z333DNtZriJdQikBZHWGyLY0NV6Ynz11VeVTaXDX2c7ixmvyj7rqWF9zhLF3lvhQ0bjyZ4rm5UrdNiIVWI9/PDD1Zpsx7LOFrgF/fXXXzHfhs1oGMpmyawtCErGfdaH3Z59VkO/cxsydDzTtunptC+Syvt0yJ7v3mR+Zuq73YhUfZ8J65pu751krHNjbDfT6XsjGuH7BK1SdNRHIhCOJVB4mWZ9d07Dz0xEU/5pfdAuvfRS78dmoLB+OdYM95NPPvEOVMI3xDYDh53RfvLJJ6uVrsdL+AfPSlnDezNlgmS+7smQbY+3LnPmzKlWoVQf9plt7EkWrFFpKNtBsf4M0YY86dTzpT7N8234fOhsR7bzFAzHwsO2eFaN1WdymIYYMmRIo4VjNlTkuOOOq1KNZDu+NgN0Q6pbwoOq2bNnN/izGj7bYSrcZ30P0GyYzp9//lklcI1Wpm/T03FfJBX36TJdY1bUpNt3byI/M+EnE8O3I9GKdy/ZVFnXdHvvJHOd47ndTMfvjZrY4w6fnXK9Rmrbkg5SdxqxDGRNbEPVN6kP35mONd21M9PWiNCGBL388suaOXOmdzn8g/DBBx94CXtjsUaJoepzYJEOUuV1T5Rse7x1Ca8mqe9QyHfeeafKcrzDC+vBYDsJQbYjYGfaov2CtNL0+u4IJpqd8QudZSja/mDhsxCHluiHh230G4v8mbahCosWLaryPrvtttu8YQwNET7LnVX1xSp8lqi6hncm4z7rK7yBvlU+RivTt+npvi+SKvt0qSy8v1ksPfcaezbMdPzuTeRnJnz7U58KXQs2Y9nmpcu6puN7J1XWuaHbzXT/3ghl+2ThlXAdw3qEZhPCsQQKnzHQepXU58MUWrlgamsiHY127drpyCOP9BLyLbfcssq/TZgwQY0lvLLCdpwT8eWVaKn6ujeWbHu8dQn/wqnPWXs7uxVaAm5nJ3fffXfF048//ljlzLhNNBJLlZtVTtXngCMZrHopfGbBaCq9bNKVzp07V7kdm3a8vpVo2cQqlY455pgqFUtW0XTdddd5s7c2VHhYbP2//vjjj5j6bYRPqGBnglPtPusrfBbtWJo9Z/o2PdP2RZK1T5fKwocqxzoToPUXaqwKm3T87k3kZyZ8O2HfubH2Zfrtt98afabKZKxrOr53UnWdY91uZtL3RvjJ4vXWWy9lTl4lA+FYAtlwCZuJK5QNy4mFffjCh+/YDBvxYM33zjzzzGobscaaLcjOZIefzX7ttdeUaVL9dY+3bHu8dbE+CqHqc8bLZu8LNXjw4Lg3ywyt5qnPLEvhM3Km8oxj4e8tO4NoPSmiYY35w9/bFvjY2dDGqhyzIbQ//PBDwn5uuukmxdPixYu9oZThgYqdpR06dGhc7sP6wYXPcGlTw0fr9ddfrxJkW5+TXXfdNeXus77Ce7nZ6xytTN+mZ+q+SH326TJV+HC3WKsfrUdSY0nH795EfmZs+HrogboFI2+//XZMtzFt2jQlQqLXNRnvnYbusyXr/R7v7WYyvjcaa385fH+gx3/tQrIV4ViC9erVq8ryyJEjY2rqZzNphI6Jtp4dW2+9ddzWL7yMsq6zF+Fn42Id07/ffvtVWbaZytKpb1GmvO7xlm2PtzbhfXXCz9DUZdasWVV2lqw/0xFHHBG39Qu93foOIbHPvb3G9dHQbUgimvHXNbQy/PasubztJCPwep5wwgn6+eefqzwd5557btzfx+GvjU0THz49eU1Gjx5dZXmXXXaJKoBOxn3Wx/bbb1+lObTtDMeyTc70bXqm7ovEuk+XqewAPHTWVvt+i+W7uL7fb5n83Zuoz4xtt8KH3b/wwgtR/71VidtshYmQ6HVNxnunoe+bZL3fG2O7mejvjcbaX/7mm2+q7S9kM8KxBAs/GLChUo888khUf2sHYM8++2yV64YPH+4diIWLduc8XHCIUOgUuLUJr7aIZaiGsWqC0IMBG5Zy8cUXJ2VWmUx43VNFtj3e2oSXZ9vji3ZIklUkXXjhhVWus55NoUP74iW8F5o1KI32i/eaa66pdjYwWg3dhtRHQ4ZA2gF96HbR+kyFn8m0oRU0vZaKiop0yimnVBtOd/LJJ3s/8Xb00UdX2fG2aqXwqstIJk+eXG3CCwv0UvU+68Pes6Hvc9vZj6VHWqZv01N9XyRR+3SZyrbH4SdBLMiOtsIzvOdnPKXrd28iPzMHH3xwleUPP/xQEydOjOpvbTsVOhlJY0vkuibjvdPQ900i17mxt5uJ/t5orP1l66sWqncDe8CmO8KxJBwo77bbblWuu/fee6udQQ5nJZ0nnnhilQ+6lYnbjnkkt99+u8477zx98cUXMQ19CZ9BpK4Z8cJ3NsaNGxfTmUn7oIcfJNmOyKmnnuqtTyxnW8aPH+/NslbfHYVMeN1TRbY93lgqNuxL0xqQRxOM2cGyzZQTtMEGG+i0005rlPW0z3LodNV29stmSKytbNteJ/sd++w15H4bsg2JlT3/4X2eYgnH7LW0acRDby+8WSv9xgK99s4666xqw5Gsp4dVjTUGG+IwbNiwKtdZo9/ahrV8+eWX3g53eJVUtDNnJuM+62uPPfaosvzee+9F/beZvk1P9X2RRO3TZbLwKo/nn3++zuGS06dP10UXXdSo65Wu372J/MzYtnGHHXaoNiy/rm2YBaC2PU6kRK5rMt47DX3fJHKdG3u7mejvjcbYX7YTXf/880+ViYbWDxsumm3ykr0C2eiGG27QwIEDvaa2wYOryy+/XG+++aZ34GAH09anyK63VNg+MDY9bHgDadtQrL322jVuSKypoP3Ym3yfffbRtttu6x20WY+U4Mw9Nh7eGgjbjrxNVRs6laudaTvssMNqfSyWLtttBTdq1kiyX79+Xl8ea7BoPVTCNzDhU9XbhsXOLE+ZMqXyOqvEsNs48MADvR162zEPLSe1Bpf23NhB7rvvvuvtwKT6EIhEvO6pJNseb23l2fb5sy/M0L4E1ojcdrrDS8zNq6++6j1/oZ9HmwXp4YcfrjI0JJ7y8vK8z1voVNR2ttMajNpn1Hb2gpUeFtjZZ85KyO0zb6yazT6DoWFeNOKxDYmFnZUNP0sZa38wW7dRo0ZVLocPL2OmSun666/33iOh7LvHXrsRI0bE/LrZ9t+2J3WxQM6GIQffh8XFxTr99NN1+OGHe99nFjBbwGmziNnBiH3vhe5c2llgO/Mbi2TcZ30MGDBA999/f+WyrbOtZ7QyfZueyvsiidqny2SDBg3yAt1g30/bbltwaz92UBrsfWTvV6tmseFt1kPQ3s/2XWSva3hvyWz/7k3kZ8b2mWz7YxXJxv5vlTtWhWrVWvZZsMdjr6vNhmhDuYP7XTaM24KM4HPW2BK1rsl47zT0fZPIdU7EdjORn4HG2F8OnXXdDIxiPyvTEY4lge0U2g6qJcuhY63tw2Q/thNtG0f7oNRUEmo7tP3794/q/mwIlw3zCA71sNu31N4+8HYfNfUNOfvss+tsymcbF2sOHlo5YQm0nZGL5MYbb6z2QbX1ufnmm73/hzYztHV7+umnvR9jG0v7qW2dU1miX/dky7bHWxtr7jljxozKHSVjw4zs/W5ns4NnaaxJsD034cMuLRh78MEHG72PlQ2Bsy/K0GbFNvTLfuwL2V4v+1K3g/9Qtj25++6761XVFo9tSCzC+4NZFUusU1bbjIK2o1PTzgyVY5HL/W3n9+qrr1Z92OxJ0ey02UGFHQDbgUgwgLLtS/C7xMIbey+HfhaD7Dvxlltu0UYbbRTTuiXjPuvDth89e/bU559/XtlnxIaBRvv+z/RterrsizTmPl0ms+/RSy65RJdeemnldfZ9ds8993g/duLJTlaF90Cy7wj7fDdW1XY6f/cm8jNjJxms6v6cc86p3L5YSGAnquzH3v/2fNjrFzqszdbt2muv9R5XosKxRK5rot878XjfJOP93ljbzUR+Bhpjfzk01LPHMJBwjGGVyWJlt/ZmjvSmtQ2lTRsdaefSDsjsjISdqa5N6DCuSLdvZ87s7G+kD6idxb7yyiuj7gljZ47DZ3CLle2U2AbPdlxCy21D2YFFTescZM9neHKeTa97qsm2x1uTbt266dZbb608SxVkZ77sjKF9gdnPM888Uy0Y23TTTb1qk0TM7GYz0ll1mgUR4WzHzj5/4Tsr1ovBzrTZetZXPLYhDek3Vtv2MhILO8IblAfZjlb4dO5ILOsLZzuk4b1NglUhkUIqm2HsgQce8M4sp8t91schhxxSZTnaXjjZsk1P1X2RRO7TZTIbAl3Te9AOxsODMQuErcKlId9vmf7dm8jPjG0r7b7CZx8NVv/Y9ic0bLKw0ype+/btq0RL1Lom473T0PdNotY5UdvNRH4G4rm/bEMqQ3uP9unTp9oM3NmInmNJPmC2Uk/rPVLXh902CkcddZSXtFtZbl3OP/98Pf74495QB7vtaJpD206Anfm25DuW0nvbgNgOvu0wW9NeO0iwM+n1abZ7zDHHeGegLanv2rVrVBs+e3y23tb7xIajhc/mkU2veyrKtsdbk3333dc7Y2Wlz9Fo166d90VrQzsSOf7fPnd2Vspeh9pmzrOz8Mcff7w37CR80oFYxXMbEmvlWH13DmsKNFI9oM8W9p60pvdW5dS2bdsaf892ZO170r47bMcw3e4zVgcccECV5sK2fYl1Ovhs2Kan2r5IIvfpMp1VL9pBdm3VIPadZO9be5937949IeuV7t+9ifrM2AzB9thtKGxNz5OdiLThbPZ8Dh06VMmSqHVN9HsnHu+bRKxzorebifgMxPMzG9oeJDjhFyTHTZWpeOCNcbZGvTbbhaXL9gGwHWwbbmEfnlirG0JZGacNc7GqFLt9O0MWLCm1nVerdLCD9obcR2Ow/g6Watv/rbmhnZG258XOuNu4c3tuakrp00Vjvu6pKNsebzg7G2a9fqZOner1G7D3tn0+bQfAvuC22mor7b777l7z68bqLxbLun722WfezD12Rt124uy1si99O2BgRkakAztLb0MIf/jhB6/Zre322HeINZ61z5tVAmbCfUbr0UcfrTIpiM2Q1pDZqbJhm55q+yLpuk+XaqwPoPV8sufQhkTbcC57La1SO5knOTLhuzcRnxl739tskDZk3+7D9plsmLi9frWdoEiGRK1rOr53ErXOid5uptr3Rvhzbt/7wYkD7Hl+6aWXkrIuqYZwDAAAIEvYAYFVNNjBgbEGyNYDEQAAZD6rPAudNdsmPbAT82BYJQAAQNawigUb+hlk1QyxTHUPAADSk1WwBScmCPYSJRjz0XMMAAAgixx88MFVZsi02foAAEBms15vNlO1sSGsF198cbJXKaUQjgEAAGQRmxnNZr0KmjlzpmbNmpXUdQIAAI3bVuHee++tXLaJcmqbnCQb5SV7BQAAAJBYO++8s66++mr9+++/3rI10wcAAJnJJiM46KCDKpdtxktURUN+AAAAAAAAZC2GVQIAAAAAACBrEY4BAAAAAAAgaxGOAQAAAAAAIGsRjgEAAAAAACBrEY4BAAAAAAAgaxGOAQAAAAAAIGsRjgEAAAAAACBrEY4BAAAAAAAga+XF+wYXL14c75sEMo7jOGrdurV3ecmSJXJdN9mrBCDB2A4AYFsAgO0AUD9t2rRRPFE5BgAAAAAAgKxFOAYAAAAAAICsRTgGAAAAAACArEU4BgAAAAAAgKxFOAYAAAAAAICsRTgGAAAAAACArEU4BgAAAAAAgKxFOAYAAAAAAICsRTgGAAAAAACArEU4BgAAAAAAgKxFOAYAAAAAAICsRTgGAAAAAACArEU4BgAAAAAAgKxFOAYAAAAAAICsRTgGAAAAAACArEU4BgAAAAAAgKxFOAYAAAAAAICsRTgGAAAAAACArEU4BgAAAAAAgKxFOAYAAAAAAICsRTgGAAAAAACArEU4BgAAAAAAgKxFOAYAAAAAAICsRTgGAAAAAACArJWX7BUAAACIp8WLXf3wozT3L2nePFfLl0urVklNm0pt2kjrrOOo8wbSRl2kFi0cnnwAAIAsRzgGAADSWlmZq8+/kKa/4+qjj6Q5c+v6C9f7b06OtEk3V9tvJ/Xu5ahbV8lxCMsAAACyDeEYAABIS4sWuRo/URo/0dWCBbH/fUWF9N33gZ9nnnO13rpSv77S/n2l9u0JyQAAALIF4RgAAEi7YZPPjXD18jippCTy71hV2DrrSB3WkVq3lpo1CwytXLRImjNH+jdCmGbDMB97wtUTT0l79HF1xGGOum5MSAYAAJDpCMcAAEBaKC11NXqM9NQzrlavrv7vnTtLvXaTttvW0WabWo+xmoOtZctdffGF9NHHrmbMVJXKM6som/am/bjaa09XZ57mqF07QjIAAIBM5biuG2i8ESeLFy+O580BGcl62rS2UgZJS5YsUZw/hgDSANuB2Hz5laubbnH15+yq1xfkS3vvLQ0b4miTbvULsCoqXH31tfTKa64XihUVVf13qzo7/lhHw4ZIeXmEZIgvtgUA2A4AsWtjsyzFEeEYkAR8AQJgOxCdkhJXjz3pasRIKfQ8Qm6uNKC/dNThTlz7gy1f7mrcBGn0GFfh5/tsdsvzz3XUozsBGeKHbQEAtgNA7AjHgAzAFyAAtgN1W7zE1aX/C1R1hbLZJc8509EGGzReSLVqlasnnw4M4ywvr9rL7KgjpGOOcqgiQ1ywLQDAdgBIfjiWE9dbAwAAiIPffnd10qlVg7EWLaQrLnN0x62NG4yZZs0cnX5qjp58zFHPrar2I3vqGen0s1zN/Ysh8QAAAJmAcAwAAKSUDz9ydcrprv7+279uqy2lZ590tO8+jneGPVG6bOjo3rsc/e9SRy2a+9d/86107AmuprxOQAYAAJDuCMcAAEDKmDjJ1QUXuVq50r9u/37SXbc7Wmut5PT6sjCu776Onnrc0ZY9/OtXrZKuvcHV3fdWqKyMkAwAACBdEY4BAICUCcZuvs1VeUVg2QrETj3Z0SUXOsrPT34T/HXWcXTPnY5OOM5Rbsge1IsvSRde4nrN/AEAAJB+CMcAAEDSvfqaq1tu98OlJk2k669xdPihiR1GWZe8PMdrxn/fPY7WXNO//sOPpJNPc/XnbAIyAACAdEM4BgAAkur1qa5uuNmV+1+uVFgo3XqTo167p04oFq5Hd0ePPeRo00386/6cLW8Sgc8+JyADAABIJ4RjAAAgad5829V1N/jBWEGBdPMNjrbumbrBWJD1QLvvbkd77eFft2KFdN4Frqa/Q0AGAACQLgjHAABAUliF1TXXuar4r8dYfr50w7WOtts29YOxoCZNHF11RaAPWVBJqXT5la7XQw0AAACpj3AMAAAk3Ow5ri67wlVZWWA5L0+67mpHO+2YPsFYkPVEsz5kl13iN+q3wM8mF3j6WauKIyQDAABIZYRjAAAgoZYtc3XBxa6WLfOvu/RiR7vukn7BWKh++zm66QbH65kW9Ojjru6+16rjCMgAAABSFeEYAABImNLSQMXYnDn+dccd42jfvdM7GAvaeSdHd9/hqGVL/7oxY6Wrr3O9xw4AAIDUQzgGAAASwoYX3nanzeboX7fP3tKxR2fWC9B9C0f33+NorXb+ddPelC6+zFVxMQEZAABAqiEcAwAACTF2nDT5FX+5R3fp4gscr2dXpumyoaMH73fUaX3/ug8+lC68xNXq1QRkAAAAqYRwDAAANLqvv3F17/1+KNRhncDMlIWFmReMBa2ztqMH7nW06Sb+dZ98Kp1/katVqwjIAAAAUgXhGAAAaFSLl7i64ip/ZkprWH/DdY7atMncYCyodWtHd93uqPsW/nVffCmde4GrFSsIyAAAAFIB4RgAAGjUPmM33+pq/r/+deef66jrxpkfjAW1aOHojlsd9dzKv+7rb6RzzrMZOwnIAAAAko1wDAAANJoJk6SZ7/rLA/pL/fbLnmAsqFkzR7fe5Gjbbfzrvv9BOuv/XK+yDgAAAMlDOAYAABrFn7Or9hnboJN09hnZF4wFNW3q6JYbHe24g3/dz79IZ53jatEiAjIAAIBkIRwDAABxV1bm6prrXBUVBZbz8qQr/ueoSZPsDceMTUBw43WOdtvVv+6336Uzz3G1YCEBGQAAQDIQjgEAgLh7/CnXGzYYdOLxjjbplt3BWFBBgaPrrnbUp7d/3R9//heQLSAgAwAASDTCMQAAEFdffOnquef95a17SoccxJMcKi/P0VWXO9prT/+62bOlM85xNX8+ARkAAEAiEY4BAIC4KS52dePNrtz/8p0WLaT/XeooN5eqsUgB2eWXOtpnb/+6OXMCFWR//01ABgAAkCiEYwAAIG6eesbVnLn+8nn/52jt9gRjtQVk/7vE0X77+tfN/Us65QxXv/xKQAYAAJAIhGMAACAufvrZ1Qsj/WVrOr93yLBBRGZVdZde5OiA/f3rFi6UTj/L1ZdfEZABAAA0NsIxAADQYBUVrm693VV5eWC5WTPp3LMdOQ5VY9EGZBed7+jgkN5sK1ZI/3e+q1nvEZABAAA0JsIxAADQYJNekb79zl8+5SRH7RlOGdtOWY6jM051dPKJfqBYXCxdcpmriZMIyAAAABoL4RgAAGiQJUtcPfSIH95stqk0aABPan1Ypd2RhweqyHL+20srr5Buvs3VPfdVqLyckAwAACDeCMcAAECDPPyoq2XLApdtFOV55zA7ZUMN6O/o2qsdFRT4140eI110qasVKwjIAAAA4olwDAAA1NvX37iaONlfHjRQ2nRT+ozFQ+/dHd13t6M11/Sve/8D6ZTTXc2dS0AGAAAQL4RjAACgXmyI3x13+SFN69bSSScQjMXT5ps5evRBR926+df9/od00qmuPvucgAwAACAeCMcAAEC9jBsv/fiTv3zayY7WaEk4Fm82scH9dzvq09u/buky6ZzzXI0b78p1CckAAAAagnAMAADEbOlSV48+4YcyPbpLfffjiWwsTZs6uuZKR8cc5V9XXi7ddqera693tWoVARkAAEB9EY4BAICYPfWMNYb/b2ciRzr3HJtdkaqxxmTP7wnH5eiqy6s26n99amCY5W+/E5ABAADUB+EYAACIyew5rsaO85f7HyB13ZhgLFH23svRg/c5Wnfdqn3ITjzF1SuvMswSAAAgVoRjAAAgJg894npD+kzTptLxxxCMJdom3Rw98YijXrv71xUVSTfc7OqS/7lauJAqMgAAgGgRjgEAgKh98aWr6TP85cMPdbTmmoRjydCihaPrr3F01umOcnP962e+Kx15rKvXp1JFBgAAEA3CMQAAEJWKClf3PehXJLVrJx1yEE9eMjmOo4OGO3rgXked1vevX7ZMuua6QBWZDYMFAABAzQjHAABAVN58S/ruO3/5pOMdNWlC1Vgq2GJzR08+5nhhpeNUrSI74ihXt91ZwVBLAACAGhCOAQCAOpWUuHr4Ub8CqevG0n778sSlksJCR2eclqP773HUsaN/fXmFNG68dPDhrh59vEJLl1JJBgAAEIpwDAAA1GniZOnvf/zl00+1PldUjaWiLXs4euoxRycc56hZs6oN+59+Vhp2sKu77q3Q338TkgEAABjCMQAAUKviYlfPPu8HKTtsL223LcFYKrPhrscc5WjUC46GD5Py8qqGZGNekg453NXV11Xo629o3A8AALIb4RgAAKjV+InSggX+slUkIT20ae3o7DNz9PwzjvrtpyqzWtpwyzemSqec7ur4k1yNftHVgoVUkwEAgOzjuK4b172gxYsXx/PmgIydXax169be5SVLlijOH0MAaSBdtgNFRa4OOtTVov++3nfZWbrlRs6tpat58129OMb1As/Vq6v/e06OtHVPaZ+9HO26i9SmDUFoY0uXbQGAxsN2AIhdmzZtFE8hRfYAAABVjR2nymDMHH8sYUk6W7u9Ne13dPSRrsZNkMZPcPXPPP/fKyqkTz61n0BA062rq+22k3bYzlGP7oGm/wAAAJmGyjEgGR88zhIDWS8dtgOrVgWqxpYsDSzvvpt043VUjWWS8nJXH3woTXnd1cxZ1l+u5t8tLJS22lLadhtH3beQNt2EsCxbtgUAGhfbASB2VI4BAICEeOllVQZj5rhjqBrKNDbjqA2V3WVnxwtDZ74rvTHN1UcfS2VlVX/XgrMPP7KfQHhjTf436eaqe3epxxaO9/92a/IeAQAA6YdhlQAAoJqVK12NGOVXsPTpLXXdmOAjkzVr5mjffaR99wkEZV98GQjCPvpE+v336r9v4dk33wZ+RinwXunQwVWPLaQePQLVZV02DARwAAAAqYxwDAAAVPPiS9KyZYHLjkPVWDYGZTvvJO28UyDYmj/f1cefSB9/4uqrr6W//4n8d3//Hfh5fWogLGvWTNp8M9frV9aju6PNN5NatCAsAwAAqYVwDAAAVLF8uauRo/2qsb32tAogAo1s1r69o/37Sfv3C7wPFiwIhGRffxP4/48/VR+GaVatskAt8CO5XtDaZcNAWNa9u+NVma27bqDfDgAAQLIQjgEAgCpGj3G1YkXgck6OdOxRBBeoql07R3v0kfboE3hvFBe7+v4HeUHZV1+7+vprael/lYehrNf8L78GfsZNCASwbdtIW/d01Wv3QLWaVa0BAAAkEuEYAACotGyZq9Fj/OV99pY22ICwArUrLHS8mSztR3K8GRdnz5EXkllY9tU3kfuWmUWLpWlv2Y+rgnxpu+1c9enlqHcvqXlz3nsAAKDxEY4BAIAqVWMrVwYu5+ZIx1A1hnqwYZKd1pf3ExyKuWy56zXv/+orV19/I337nVRUVPXvSkqlWe/Zj6vb75L69HLVr6+jbba2KkaCMgAA0DgIxwAAQOUMlWPG+k/GfvtK63ckkEB8rNHS0c47SjvvGHhPlZW5+vkXebNivjPT1ZdfSRUV/u8XF0tT3rAfV2uvLQ0fJg0eKDVpwnsSAADEF+EYAADwjJugyl5j1h/9iMMJIdB48vIcbbqJvJ+DhztavNjVzHelt6YHZsYMDcrmzZPue8DV8yOkww4JhGRNm/L+BAAA8ZETp9sBAABpzBqqjwqZodKarXdan/ABidOmjaMB/R3dcWuOxo52dNopjjp3rvo7ixdL9z/o6qDDXI160VVJif+eBQAAqC/CMQAAoMmvBhqjBx15GMEYkjsb5mGHOHr2SUcPP+Bo992qh2T33u/q8KNcvTHNVUUFIRkAAKg/hlUCAJDlrPfTCyP8cGHnnaSuXQnHssH06dP1+uuv67vvvtPChQtVWFio9u3ba6eddtKAAQO0wQYbxO2+Bg8erH/++adef/u///1Pxx59gJ582tU7M/3r586ZrMsvu16Xx3BbDz30kHr27Fmv9QAAAJmJcAwAgCz3xjTpn3n+8pH0Gst4FoRdddVV+uijj6pcX1xcrGXLlunnn3/WqFGjdOKJJ+qoo45Ssq2zzjrq1tXRjdc5+vEnVw8/6uqDD5O9VgAAIFMQjgEAkMVsONpzz/tVY1ttKW3Zg6qxTLZy5Ur93//9n3788UdvOTc3V7vttpu6du2q1atX6/3339cvv/yi0tJSPfDAA6qoqNAxxxzT4Pu127D7jsZbb72lr7/+2ru87rrratttt638NwvJbr/F0Ucfu7rxJumvOYHrnZzt5eTsqPx8qdfu0ibdbGKJ6u9luz0AAIBQhGMAAGQxG6L2x5/+MlVjme++++6rDMbWXHNN3XHHHdpkk00q//3MM8/UM8884wVj5uGHH/aGWW666aYNHlYZDQvjRo8eXbk8cODAiCHX9ts5OvZoR9df/98VTg/l5B2mcld6a4bk5Ernn+tojZaEvQAAoHY05AcAIEu5rqvnQnqNdesq7bhDUlcJjezPP//UhAkTKpevuOKKKsFYkA2l3HfffSvfJxaoJYpVrs2bN6+yqq1///41/m5oZtaju5Qbsmf75lvSMce5+vQzmvUDAIDaUTkGAECW+vIr6bvv/OXDD3O8Ch2rEgr2eRo3bpx3ecaMGZo0aZJXcbR48WK1bdtWm2++uY488shqFUWLFi3y/s6avf/9998qKSnReuutpz322EOHH364mjZtGtX6lZWVaerUqXrnnXe8hvF2vxbUtGnTRj169NA+++yj3Xffvc7bWbVqld577z198skn+uGHHzR37lytWLFCBQUFatWqlTbbbDP16tVLe++9t/Lyat81evTRR/X4449XNom34Gbp0qUaP368NxTQbtv6dtnzs8022+iQQw7xhiumiilTpqi8vNy7bM/hjjvuWOPvHnfccV6zfvPxxx97zfTtPdHY7LkMsuGe7dq1i+rvrJLsrLMcXXu9q7l/Ba6b/6909rmujjnK9arMcnKoIgMAANURjgEAkKVGjvYrajqsI/WOkDMVFRXp2muv1bRp06pcb6GX/bz55pu6+OKLNWjQIO/6d999V1deeaUXPoWyHlb288Ybb3jD9Ww4X22s39TVV1+t2bNnV/u34H1bcLP11lvrhhtu8AKzSOw+LeSxwCpS+GbBWfBxPP3007r55pvVqVMnRevzzz/X5Zdfrn///bfaOk6ePFmvvvqqLrzwwqiHFDY2CyyD9tprr1p/t3Pnztpoo4285zD4twcffHCjTxQwc6Y/HWXwfRWt7ls4evIx6e77XE1+JXCd60pPPi398IOr/10mhlkCAIBqCMcAAMhCc+a4mvmuvzz8QEd5edWraix4smBsjTXW8Kq0Onbs6DVttxDMQhOr5LrpppvUpUsXryLpoosu8kInq0qy4KpZs2beUD67DQuo/vjjDy9su+uuu2odVme3Ewy0rHJohx128KrPrLJtzpw5XoBisyp+9tlnOuWUU/TEE0+oefPm1W7L1tVux/5u44039qq4rPqpSZMm3r/9/vvv+uCDD7yQ7LffftPpp5/u9duqKWwLZY/f+nXZ39rtbrfddmrdurUX8FilnVVaWf+sW265xbvv7t27K5msgs8eY9BWW21V59/07NmzMhz7/vvv1disOjFY2bb22mtXVjFGwyoMZ82a5b0/7LUtLGypouJ1VOFuqZzcfpr1fjedeLKr66+VNt6ICjIAAOAjHAMAIAuNHuN6FTWmRXOp//7Vf8fCHfvp3bu3Vx3VokWLyn+zQMqqrGwInAVk999/v/e7hYWFXhi0yy67VLktG0558sknexVlFn5988032mKLLardp1VcWR8sC7Ss39Rpp53mVSuFD3e0WQ+vu+46byijBW533nmnN8wxnAVmtq42/LGm4XnLly/3HosN4bQKMGtAb9VwdRkxYoQXslnYZ0M8Q1nIdumll3ohogVkFt5ZkFZTIGSPJZ7sOQ5nz1MweDLrr79+nbdjYWjQr7/+qsZk76OJEydWLg8YMEA5OdG3xw1OMhBUVrZYkv18p/LyUXJy+mjO3It18mlr6H+XSHv0ISADAAABNOQHACDLLFvm6pXX/OWBA6VmzSIHBd26ddP1119fJRgzFlqcc845ldVaNrzQwjEL0cKDMWPD8w477LDKZQu1aurpZRVh5qyzzvJCtUh9wOx+LZQK9juz4Yt2/+E23HBDHXPMMbX2rWrZsqU3hDPYG8xuyyqPonHJJZdUC8aMhYQW1tn/TbA6LZmsoi3IKvrCX9NI2rdvX6WXXGOyvmZW9RV8f1k4Fi17P9j7zt4vp556qo4//nhvQoHQCkC34m2VlxyvoqJFuuJqV2PH0agfAAAEUDkGAECWGTfBeokFLufmSgcOqbmCxkKGmprUW2P9bbfd1htCGAzS+vTpU+NtWXP1Rx55JGKVj7HG9sEG8Db0cfjw4bU+Dlsva3h/1VVXeRVRVqU1bNgw1YdVqVlD/p9++smrWrMJAKyhfm0s8Ntvv/1q/HcLZuw2bDIAW7+ff/5ZW265ZbXfs4kNzjzzTDW20HDOKt6iETp5glXrJaoRvw2ntGGVddlggw0qKxUjvU/ttXzhhRf02GOPBarm3LmqKL1CTsF9uuMuV4sXuzrumMBEFAAAIHsRjgEAkEVKSly9NNavmNl7T6sOcmoMjKzXV22sD1hQXf2hQn83tIopyPqHWb8yY/cbzZC6TTbZpPKyBVp1DduzoYU2PNCq0ywssuuCQgM7+726wrFIFXKRwhsLx2p6zMb6tdlPYwudlCA/Pz+qv7EZPSP9fbxZMBoMWWNpxG+97WpjlXvHHnusF1RabzzjVnyqivJ3lZO7q9eof9EiV+eeY+93AjIAALIV4RgAAFlk2pvSwpDRcQcfVHMg0KpVqyqVQ5HY8Lwgq/aK9ncjDVu0qq2gCRMmeD+xWLzY+ktVZyHYc8895/WzCp9VsibWh6wudT1eEzpJQLRDNRtLcIinKS0tjbqJf6S/j7dXXnml8r5sCOyuu+4a19u32UJt9tCvvvrKW87LmaoKBe5j/ERp6TJXV/7PQkMCMgAAshHhGAAAWcKqpEaO9iulttla6tbViapqKBp1/X7o0DVrUh+peqghIoVP8+fP1xlnnOHNmBmLaKqkYg2LIj3mRAoNJ4uC42rrEPp7kWYDjZfQIPSAAw6ocShvQ+y1116V4Vib1l8rr4k0b17g396ebq+5q+uutteVgAwAgGxDOAYAQJb4+BPpl5AJBw+ppWosGUJnUrTKobqGNdbWPD7I+pEFg7G11lpLQ4cO1dZbb611111Xa6yxhhfoBYdvNsaskdGwYZ7BoZfxYo3pw6255ppVquls5tC6mvJbuBjUtm1bNYYvvvhCv/32W2WAOtBmiGgEobNzLl26UC8+5ujcC1z99nvguvfely68xNUtNxKQAQCQbQjHAADIEiNG+VVjG3SSdtpRKcXCqtAgI1LAEwvrIfbpp59WDoF8+umnvaGiDRlK2Ri+/fZb3XvvvXG9zUjPnfU/sz5ywRBy9uzZ2myzzWq9neDskcEJCBq7Ef92221XpTddY7EQbq21HN17l3Tuha6C7eY++VT635WubriWIZYAAGSTujvdAgCAtPfrr64+/Khqr7GcnNSqHOvcuXPl5S+//LLBtxccQmcGDBhQazBmfvnlF2Uyq5LbcMMNY3qOP//884iTH8SLzYD55ptvVukN1lhCgz5r0G9at3Z0zx2Oum/h/55VkF19nauyMj9MBgAAmY3KMQAAssDIF/0D/datpf32UcqxGSqtosd6o9nMk9agv2vXrvW+vdBKsNAhhTX11po5c6aSoX///t5PIvTu3Vs///yzd3natGk6+OCDa/xdG44aDAztdbG/jbfXXnutsq+ZBVa9evVSY5k6dWrl5e7du1debtHC0W03S2ed61eQWQ+ym5u4uuQipVyIDAAA4o/KMQAAMtzixa7e8HMBDR3spGTTcQuw9txzT++yBWQ333xzldkS62J/U9MwTRtiWZvHHntMS5YsUabr27evN7QyWDn20Uch5YRhHn/88crndNttt41qds6GDKncf//9lZ+fr8ZgM5WGVhJac/5QFpDdcYujDf3iRb06RbrzHrfa+woAAGQewjEAADLc+IlSaWngckG+NGSQUtapp55a2ST+66+/1plnnlnrTJPWVH7y5Mk66qijqs12aY33Q8ORd955p9rfl5aW6uGHH9Zzzz1XZTbNTGW93AYN8t8AV199tVehF+7ZZ5/VlClTvMv2vJx++uk13qZNZLDTTjtV/kTr+++/rxJaxtqI36rOrr32Wu92amLh6jPPPKObbrqp8roePXpErIKzIZZ33u6oY0jLs5fHSU88RTgGAECmY1glAAAZzPomvTzeP7jfe28bvpa6IVDHjh11/fXX66KLLvKG29lMhocccoi23HJLbb755mrdurWKi4u1ePFiL9SxYMQCrkisv5aFINOnT/d+54ILLlDPnj2922nZsqXmzZund999V//++6+aNWumIUOG6Pnnn1ems6DLqqjs+VuwYIGOPfZY7b777tp44421evVqffDBB1UCs5NPPrnOxv0NrRqzINMmDIhFWVmZF4zaj1W12etq75/mzZt7odjcuXO9x2LvlaAOHTroxhtvrPE2263p6K47pNPPcjVvXuC6J5+2mU5dDeyfup8bAADQMIRjAABkMOudtHChv3zg0NQ/wN9xxx29IX1W1WSVRRUVFV5j+NDm8JEqoiINybv88su1aNGiyiF1kW7HhnNec801+vvvv5UNLDy66667dOWVV+rjjz/2Qqa33nrL+wllz+eJJ57oVeXFm4Vwr7/+euVyaDVbffzzzz/eT21sKKUFpBaw1madtR3deZt06umuli4LXHfbHa7WbCvtukvqf34AAEDsCMcAAMhgY8b6VWNb9pC6dU2Pg/uNNtrIGw73/vvve5Vf1h/LqpxsGKXNumjN263SyIbI2VA+qxqKxIZoPvjgg151kYUxVhG1atUqrx/Zeuut5zWAt+F8NpOlDQ/MFhYI3nfffXr77be958UmQLAQ0Z7b9u3be8+pPS+xVnPF0hzfZqo09lrsscceMd+GhV22rt98842+/fZb/fXXX1q2bJk3vNaGgtrtWmhq75F+/fpVmQ21Lp3Wd3TzjdLZ57oqLpYqKqQrrnZ1z53SFpunx2cIAABEz3Hj3GU0tHQdQA0fPMepPHNtDaBp9gtkn0RsB7773tWJp/i3e81Vjvbsw4E9EK2Zs1xd+j/XC8dMqzWkhx5wtH7H+H2O2CcAwHYAiJ2dKI0nGvIDAJAFVWPt15J67ZbU1QHSzm67ODrv//wgzIZZXnKZq1WraNIPAEAmIRwDACADLVrk6s2QFlJDBjvKy6NqDIjVoAGOjj7SX/79D+na662ajIAMAIBMQTgGAEAGmjBJCk7iWJAvDTgg2WsEpK/jj3XUu5e//M670lPPJHONAABAPBGOAQCQYUpLXb083q9q2WcfqXVrqsaA+srJcXTZxY66bOhf98RTrt6ZSfUYAACZgHAMAIAMM2OmtHChv3zgEIIxoKGaNXN043WOWrb0r7vmele//U5ABgBAuiMcAwAgw4yf4B+s9+gude1KOAbEw3rrObr6Ckc5/+1Br14tXXq5q5UrCcgAAEhnhGMAAGSQP/909eln/vKQQQRjQDztsL2jU07yP1ezZ0s33OzKdQnIAABIV4RjAABkkPGT/AP0VmuoShNxAPFx6MHSnnv4y9NnSCNG8ewCAJCuCMcAAMgQxcWuXn3NX+7XVyospHIMiDfHcXTxBY46b+Bf99Ajrj75lOoxAADSEeEYAAAZ4q3p0rJl/vLAAQRjQGM26L/+GkdNmwaWKyqkK69xNX8+ARkAAOmGcAwAgAxsxL/tNlKn9QnHgMa0wQaOLrvY/5wtWSJdcbWrsjICMgAA0gnhGAAAGeCXX1199bW/PHggwRiQCH16OzrsEH/562+kp58lHAMAIJ0QjgEAkGFVY23bSLvvltTVAbLKSSc42rKHv/z0s9IXXxKQAQCQLgjHAABIc6tWuXrtdX/5gAOkvDwqx4BEsc/bFZc5atHc7z927fWuli8nIAMAIB0QjgEAkOamvWkBWeCy40gDDyAYAxJtnXUcnX+u/9n7Z550x92EYwAApAPCMQAA0tz4if4B+I47SB06EI4BybD3Xo7229dffmOqNOV1AjIAAFId4RgAAGns++9dff+Dv0wjfiC5zj3bUYcO/vLtd7n65x8CMgAAUhnhGAAAGVI11n4taacdk7o6QNZr3tzRlf9zlPvfXrYNeb7xFlcVFQRkAACkKsIxAADS1IoVrt6Y5i8P6O/QiB9IAd23cHTkEf7yJ59akJ3MNQIAALUhHAMAIE29PlUqKgpctiqV/vsne40ABB19pKONN/KfjwcedDX3L6rHAABIRYRjAACkIdd1NX6Cf6C9yy7SWmvRiB9IFfn5ji67xFFubmB5dZF0480MrwQAIBURjgEAkIa+/kb65Vd/mUb8QOrpurGjY4/2Q+vPv5BeejmpqwQAACIgHAMAIA2NC6kas5nxtt8uqasDoAZHHCZt0s1ffugRV7PnMLwSAIBUQjgGAECaWbbM1Vtv+csD+zvKyWFIJZCK8vIcXXaxo/z8wHJxsXTDTa7KywnIAABIFYRjAACkmVenSCWlgct5edIB/ZK9RgBq06WLo+OO8QPsr76WXnyJ5wwAgFRBOAYAQJo14g8dUtl7d6ltW6rGgFR36MHSZpv5y4886uqPP6geAwAgFRCOAQCQRj77XJo9218eNJBgDEin4ZUF/w2vtOrPW25n9koAAFIB4RgAAGlkfEjVWKf1pa17JnV1AMSg8waOTjjeD7S/+FJ65VWqxwAASDbCMQAA0sSiRa6mv1O1asxxqBwD0slBB0obb+Qv3/egq4ULK5K5SgAAZD3CMQAA0sTkV6WyssBlG5rVb79krxGA+gyvvPB8C7YDy8uXS7fdsZInEgCAJCIcAwAgDVRUuJowyR9+teee0hprUDUGpKPNN3M0dLC/PGFSid7/4L8paAEAQMIRjgEAkAY++lj6+29/edAAgjEgnZ10gqN27fzlq69boeJi+o8BAJAMhGNAkriuK7es2C7wGgCo07iQRvwbdZG6b8GTBqSz5s0d/d9Zfsj9558VeuY59gkAAEgGwjEgSSo+fFLlY89R3ucv8BoAqNX8+a5mzfKXacQPZIZeu0u77uIvPz/C1Zw5BGQAACQa4RiQDMXL5f7xkXcx969PvWUAqMmkV6Ty/yaza9pE2m8fnisgE9hss+eenaMmTQLLpaXSXfe6XnU5AABIHMIxIBnKS2tfBoD/lJW5mjTZP1Dee6/AcCwAmWGddRyddELTyuX3P5BmvpvUVQIAIOsQjgEAkMLsQHn+v1WHVALILMce3VSdOvm75ffc59KcHwCABCIcAwAgTRrxb7qJ/RCOAZmmoMDRpRc1r1z++x/puRcYWgkAQKIQjgEAkKL+/tvVBx/6y4OpGgMy1u67FajXbv7y8y9Ic+cSkAEAkAiEYwAApKgJk60xd+By8+bSXnsme40ANKazzshRQUHgcgnN+QEASBjCMQAAUlBpqavJk/1lm6GyaVOGVAKZrEMHR0cd4X/O33tfendWUlcJAICsQDgGAEAKeuddadFif5lG/EB2OPRgab11/eW776U5PwAAjY1wDACAFDQ+pBF/j+7SRl2oGgOyQWGho3PO8j/vNOcHAKDxEY4BAJBi/pzt6pNP/WWqxoDssvNOjnbbtWpzfpugAwAANA7CMQAAUsyEif5B8BprSHv0TurqAEiCs85wqjTnf+gRwjEAABoL4RgAACmkuNjVK6/5y/36BoZZAcgu63ZwdMhB/vK0t6SvviYgAwCgMRCOAQCQQqbPkJYt85cH9ScYA7LVEYc5WrOtv3zP/a4qKgjIAACIN8IxAABSyLiQRvzbbC116kQ4BmSrZs0cnXiCvw347jtp6rSkrhIAABmJcAwAgBTx66+uvvzKX6YRP4B++0nduvrPg/UeKyqiegwAgHgiHAMAIEWMD2nE36aN1Gu3pK4OgBSQm+vozNP96rH5/0ojRiV1lQAAyDiEYwAApIDVq11Ned1fPqCflJ/PkEoA0tY9HfXa3X8mnh/hasECqscAAIgXwjEAAFKAzUS3YmXgsuNIA2jEDyDEaSc7yssLXC4qkh5/knAMAIB4IRwDACAFjA9pxL/D9tJ661I1BsDXsaOjYUP95cmvSr/9TkAGAEA8EI4BAJBkP/zo6rvv/eXBAwnGAFR31OGOWjQPXK6oCDTnBwAADUc4BgBAClWNrdVO2nmnpK4OgBTVqpWjI4/ww/N3Z0mff0FABgBAQxGOAQCQRCtWVOj1qf7Bbf8DpLw8KscARHbgUKl9e3/5/odcuS4BGQAADUE4BgBAEk16pUSrVwcu5+RIAw4gGANQs8JCRyce528nvvtOems6zxgAAA1BOAYAQJJYtceoF4sql3fd2SpCCMcA1G7ffaSNNvKXH3nUVWkp1WMAANQX4RgAAEny5Vdl+vHH8srlgTTiBxCF3FxHp53sB+lz5koTJvLUAQBQX4RjAAAkyagXiysvd1hH2mE7XgoA0dlhe2nbbfzlJ592tXIl1WMAANQH4RgAAEmwbJmr16b44djAAY5XDQIA0XAcR6ed4m8zliyVXhhJOAYAQH0QjgEAkASvTnFV/F82lpsrHdCPlwFAbDbp5mjfvf3lkaOlBQsIyAAAiBXhGAAASWjEP26CfwDbe3dHbdtSNQYgdice7yg/P3DZAncbXgkAAGJDOAYAQIJ99rn055/+8uBBBGMA6qdDB0dDB/vLk16R5v5FQAYAQCwIxwAASLDxIVVjnTfI0TZb8xIAqL8jDnfUtEngcnm59BTVYwAAxIRwDACABFq82NX0d/zl4Qc28RprA0B9tWntaPiB/vKUN6Q//qB6DACAaBGOAQCQQJNflcrKApcLCqTBAwt5/gE02CEHO2rRPHC5okJ64inCMQAAokU4BgBAglRUuBo/0T9g7btvgVq35qsYQMOt0dLxArKgaW9JP/9CQAYAQDTYIwcAIEE++lj6+29/+aDh/zUJAoA4OOhAqdUa/vLjTxCOAQAQDcIxAAASZFxII/6Nukg9t8rjuQcQN82aOTr8ML967J13pe++JyADAKAuhGMAACTA/PmuZs3ylwcPdGjEDyDuhg6W1mzrLz/6OOEYAAB1IRwDACABJr0ilVcELjdtIu23LzNUAoi/Jk0cHXWEv3358CPp628IyAAAqA3hGAAAjayszNXESf7B6d57S82bE44BaBz9D5DWaucvM3MlAAC1IxwDAKCRvfe+9O8Cf3nQAIIxAI2nsNDREYdTPQYAQLQIxwAASGAj/s02lTbdhHAMQOMaQPUYAABRIxwDAKARzf3L9Xr+BA0aSDAGoPEVFDg6Mqz32Fdf03sMAIBICMcAAGhE1mvM/e94tEVzaa89eLoBJEb//aX2a/nLTz5NOAYAQCSEYwAANJLSUtebpTKo735S06ZUjgFIDKrHAACIDuEYAACNZMY70pIl/vJAGvEDSLAD+lWtHmPmSgAAqiMcAwAgAY34t9pS6rIhVWMAkls99tHH0tffMLwSAIBQhGMAADSCP/5w9dnn/jKN+AEks3psrXb+8jPPEY4BABCKcAwAgEYwfqJ/8Nm6ldSnF08zgORVjx12qF89Nus96aefCMgAAAgiHAMAIM6Kily98pq/vH+/wMEpACTLgAOkNm385WeeJxwDACCIcAwAgDh7Y6q0YoW/PLA/wRiA5GrSxNEhB/nborenS7//QUAGAIAhHAMAII5c19XYcf4B5447SB07Eo4BSL4hg6SWLQOXXVd6luoxAAA8hGMAAMTR199IP/3sLw8dTDAGIDU0a+booAP9bdLUqdLcuVSPAQBAOAYAQBy9HFI11mEdaacdeXoBpI4Dh0rNmwcul1dIz40gHAMAgHAMAIA4WbzY1VvT/eXBgxzl5lI5BiB1tGzpaOgQf/nV16R58wnIAADZLS/ZKwAAQNorK1bej1O0+q0ZenH3b9S2YJEKc4uUu7SNnBHrqLzTLirr1lcVa3Vr9FXJmfOxty65f38uZ/nfcoqWSflN5DZtq/L2m6m88+4q26SflN80utv763PlfzFCuX99KmfFv5KTI7flOipffweVbH2E3DU3rvtGykvV9Pnhyl3wg7dYtmEvFQ15WJnEWTJbeT++ptzfZypn6Ww5qxdLOblym66pinZdVbahPe/7S4X/NXxKd+Wlyvv5DeX+9o5y//lKzqoFUskquYUt5TZbUxXrbaOyLnuofMNekhNbQOysmK+8b8crd85Hyln4s5yipVJFudxmbeWusa7KNthVZZv0ldtmw0Z7eFq9RLlzP/YeW86CH+Usna2clQuk0lXeP7sFLeWu0UEV7TdX2UZ7qrzzblJOFLvVqxer4LPnlfvbdOUs+UMqLZLbtLXK1ttKOT2HSx13t2egzpvJ//BRFc68I7Au+c206qgJclutF/XDs6GVL45xVVQklZVJI0a6OucsgnwAQPZyXOscHEeLFy+O580BGckOmlrMvNW7XFpaoqI+l0rN2iZ7tQDUQ+5Pr6vw7ZuUs/zvWn/PlaOyzQequPfFUtPWchxHrVu39v5tyZIlXiP/hnAW/qImUy5V7j9f1vm7Fc3bq6T3hSrb9IBaf69g5p0q+PCRGv/dzclTcZ9LVNbzsFpvJ/+Dh1T47t2BvylorlVHT5TbsoMyQmmRCt69U/mfj5BTUVrrr7pN26h413NUtuVBSme5v7ypwmnXKGfFvDp/t3ztHire+0pVrL1F3TdcVuy95/I/f6Hu5zInT2Xdh6m494VSfjPFU5Mxxyp39ody3Iqo/6aidScV732VyjvtXOPv5Mz9VE0mnKmc1Ytq/B0L/ooG3lPrY3IW/6Fmzw6WU1bkLdtnsHSboxSrex+o0KjRgcsFBdKLIxytuSYBGZAM8d4nALJBmzZt4np7DKsEAKCe8j9+Uk0nnl1nMGYcucr/dryajj7Sq4yJp9w/31ez5w+MKhgzOSvnq8kr5yv//Qdq/J28z1+oFoy5ha28KpUgp6JMhW9ep9xf367xdpxFv6ng/Qcrl4t3Py9zgrHi5Wo69gQVfPpMnWFO8MRIk6lXqmD6zYGpAtNQ/oePqOn406MKxkzuvK/UdPRRXoVZrYpXqOmoI1Xw6dPRPZcVZcr/cpSavnisV40VT7nzvokpGDM5S/5Uk5dOVN53EyP+u7N8npqOO61KMGYBX0XzdlV+L++Pd1U45bJa76tw6pWVwVh5h61UuvURqo9DD3JUkB+4XFIijXoxPd+TAADEA+EYAAD1kPvzVBXOuCX2v1v4s1c9YsPE4sFZOldNxp9eebAci8JZ9yrv+8nV/8FCr/fuq3JVca8LtfL097XyjI9UstVhVUK/gnfviXwHrqsmb1wup7zEWyxfb1uVbXmI0kZ5qVQWWPdImrx2iXLnfhLzzRZ88pRXHZUwpUVSjGFPJLk/TlHhzDtj/jundJWaTDrHq3iqiYW1FqTFvE7/fKkmr1yoVOC45Sp8/fKIjzP/48flFC+tXLbPwspTZmrVye9o9UHPSHlN/N/98TXl/Pt9xPvI++pF5c3+wLvs5uaraN/rvKHO9dGunaMDQopHXx4nLV1KQAYAyE6EYwAAxKp4uQqnXl3t6grX0ZM/HadJG7+hFWd8rNUHPqmKtl0iHtDnffJUXJ73gpm3e+FDuPJ1ttSqg57RitPe18rjXvfCLTe3sNrvFU67tlrlTY71kAq5rqJNZ5Vud2xgwclRSa/z5Ra08B/Pv99FrIbL+3JkZXhk912077Ux959Khpy/PlPBtGvU/JHeyln0S8TfsVAx75dp1a63SqDVA+7WitM+0MqTZwSe95z/ynNCFLxzu5xlc9Vo3ArlWhXSa5eo+cO7eZVZDVJeqkKreIugdPNBWnXkeK04/SOv91Vp92GRA7LXI1dEWVVZ3m/Tqz+EwpYq2vsqrTxhmlacOkurhz6q8jU3qvZ7eX/MVN7XLyneXCdXZRvtpaJ9rtXKY17xPtMrT5iq1f3vivi5Nk55sfI/fab6Ov5etXLOKijVpJV3uWL9HeRsNbTKv+f+NqP6ba/8V4UzbqtcLtnh5Oh6/tXi8ENs0pDA5dVF0ksvN+jmAABIWzTkBwAgRjY8MscakIcZ88dBenLueXp5P0cqcFTeaSetHvKImj11gHfQXOU2Pn1a7p5ny8ltwFdxyUrl/Vw9oLHhj6ut4X3TQP8St0mrQLhVUVqt8seqWfK/GqPSHU6svC58mGhFu02q3kF+U1W02cAbflZ5O8v/ltuifcjyPBW+c4e/qjuf3rgN1BvIhn/mfzdRed9P8hrq1yX/4yciXl804F5VrNvTu2w1OIHnvayyeXrl/ZWt9qrHSnpdoHjKmfeN9xgsvMtZ+W/cbjd3zocRhw+Xd9xexX1vqlyuKOyq4n2v815/C62q3MbcT5Tz9xeq6LBVlevzvpsQ8T69fnZb+NMqWtP7ojYPq9lT+1dWIwYVfPyk14MsHizMLO15hEq3P96bfKLKvxU0V/ka62lVlz7eMNBI1W42NDK83tBZFvaZWqvqZ8pZZwvv/RKUs+yviEG2U7zMu1zerptKdzhJDbXOOo7228fVK68Fll8a6+rQg6WmTVM/xAYAIJ6oHAMAIEZ5X4+NeP2IXw/XAf2kwkL/wNJmkCvbaI/qX8Ar5sv9sXqwFXNvpLCQwJRtsHNlMFbl+m59I95O/jdjq1XMVFFRVv2PysOuC5upr/DNa+SUBKqVbJbMysqzFOKsXOBV+dhMms2f2l8FHzwYVTBmQ95y538bsVovGIyFKt3y4IjVY/nfvByX4bU2tNYmPWj2VH+v95wN24xnMGZy534W8frSGt5TZd32i3h9foTPjs2EGvk2qt+2fZ4q1ulR7Xqr8LOKv3hYffiLKtnzsmrBWBV5hSrZ+dSI/+RE6seWk1v75yfsM2b9yELl/vSGNzuo929OrhdAKrf6e6o+Dj3E314tXSa98mpcbhYAgLRCOAYAQCyKl0fsB7S4uLV+X7mRBg+qXnFRsd62EW/K/eO9Bj33ToTqNe92m69Vw/V+ZVeonMW/y1m10P+91p2q/rtViIUevBctVc4Sv6+S6+SoYo31KpfzfnhVeb+86c9oaQfyYQf7SWPVdt+O95qnN3ukjwrfvlG5876O+Ks2rC90AoKg3NkfRfx96yMVUZM1VNGua7WrbehqTcM267R6sfK+GKmmIw9Xs8f38WYDrem2KpqtKTWkQrG291pItWA01+f8Vb1Hm7NyYfW/L2zpVShGUlHD+zt3buSQLVbuGutG9XsVrTtH/vuC5hF+t+pnKvfvL6r+zeyq617lM2jDuN+8tnLRZqaMFBDW14adHe26i788crSrsjJ6jwEAskuK7KkCAJAeLEixJvTh/ly5gXbaUVpv3QjhWOsNIt6WO/fzBq1LpGqkYG+iyNfXPEtmzj9fq7xLb+9yxVqbqqJVR+UsnRP4txX/qOCtG1S68+mB3lNv3VBlAoDy9bbzK9WKlnq/G1S67bGqaL+5kqqiTLm/vxsYbvjzNG9IY00sDCvr0kdlm+yv8s67S3kF1X4np4bG8TbUtMZVsGGoEarN7HmvaNctusdRVqzcX9/yhn9an67aZnW0obRlG+8deBzr71i9cilGbm7158HUNPNqTdfnLPrNCygVGiBZBVTYsGOneLlUujpiQFZTVVxODSFnY8lZXn3oo4k0fNhei9yQUL1w+k0qatbW2zbk/fS63G8nV+111sWvNi2cfkvlY65o1Uklu5wZ50ciHX6oo3dnBbZrf/8jvfm2tO/ecb8bAABSFuEYAAAxCO8dFLSoeE0NHRa5T4/bfM3I1y8JhE/15bZeP+L1eX+8p+LVS6oNrcz78b/GQnUFZ47jNZJvMvHsyiCw4IsR3k+1dcjNV4k1F/+PNW0P9mOzRv7WayxZrL9VnvUR++FV5axeVOPvuXlNVL5hL5Vu0k/lG/aR8pvUfrs1vAdcq9Cq6T6atavhtiIHLP4fVih39gfK+26SF6IEh6pG/NWCFirbeC+Vdeun8g12iduwO++2W3WMeL3NrFjW05+9NCjvxykRf99xK7zhrKHVVRWt1vcmdah+G69V6Tnm/f3Sud6EEZHYUOVEyvtqTMTrS8PW2btu6yO94cvB1ztn0a/eENiIf7/14ZWf7dzZH1aZbKB4n6trrKhriC17OOrR3dVX/+WLL4xwtc9etimg9xgAIDsQjgEAEINgQ+xq1xc00Y47RP4bN6+Gg9miyLcVLa/Cq2WHao3Srcl+05dPVnGv871m+rbOeT+9oYL3H6zxtrxKnRDlXffxDsQL37y+2mQCQW7hGirqd5MqOmzpLef++V6gj5bXjN7xZvmz3kyBGyxV7h8zveFkzqrFcvMK5bZaX2Ub7i63TeThafXhLP5d+RYkfT9ROUv+rPH3LNQr32A3lW3ST2Ub7Vm1kqkuYc9VpZpeZ+/fagjcang/5cz/LhDseY3150dR6dZP5Z17Rax0iwe7j4K3b6pWNZk75yMVvnaxSrc7zhtaa+GP9XELb8Yf/l4LvZXyLn0ihmM25NXeN+X2Hslr6vXYK5h+U8Q+e7U9l43BXhcLBsNZIFy2af/qf9BkDa0e9piaTDhTuQtrHkpbutUh/iQNZcUqfOOKyue8tPuB3iQfQTn//uDNSOos+0tORZkXwJavt03gd5ycelWPXXxZ4L5+/kX68CPVuE0DACDTEI4BABALG+oVwbod85WTU0OVRU39tmx4WQOV7HSamrxxebXrc//5Us1GHxX9DUVoul/WY7jKN+ztVa5YPyev75TjyG2xjsrX3yFQIdOkVeCXS1er8I0rqxzkV3TcLrAuv8/0DvIjzXZY8Lajss0HqnivK6QI/b1iYSFB8yf71fjv1v+sfP2dAoHYxnt7gUW97qeG90CtfdVq+DendFX1X53zsZqNPrLGm3JzC71KN+9xdOnTKJVE1e6zdSeVbdbfG9IZafZW+4la2HDQkm2OVP7nz1cLni1EazLVf0/VxQKiRMj9cYoKX7skYp+0ov531xhQ2nDL1Ue87FXV5f42XTmL/5BseHLT1srvtI1ytjpQK5utL7n/VWu+d19lbz/rs1bc+0LvsvUHLJxymfJ+mx7xfiradlFRv1tUsfYWMT2uXXaWOm8g/f5fO8HnR7jacQcqxwAA2YFwDACAWNQQRHTsUKaKmv6mpt5QsVQr1aCs+zCVzv1E+d+Oa9DtWBVYxOtbtFfpTqeq5u5WAQWz7qmc6dGq2YJDLXN/m6Em4071htNFYlUxFqzkLJ2r1Qc+0bChgP+FCuHK1t/R671V1nUfqWmb+t9+8G5qCqNqC2dqeA9Eavgf6bmyPlRWQWVDJm3oZDzeO7Eq3uN/yvn3R+Uu+KFBt2P90Kpo2kZFB9yuJuNOq7WPWn3fw/GU//ETKphxW7UKOnsdVw96QBVr1dE/LjffCxntJ8iGLjZp/d8Q6CVLKisH8z95qvJ3vPDYJikoXqGmo4/yhmXWxP6t6agjtfqQ51XRfrOoH5uF+4ceIt14c+CxffqZ9N33rjbblIAMAJD5mK0SAIA4HIAXOKtjrzSqZ+VS1Rt3vNkgi3c+o8YG/VUqp6x5fqR/C+tPFgtrKp//6bOVy8V7XRkIb8pKAsPCQsIeW88Vp87SqiNe8oagBeXO/Vj5X46u9zrUun7L/lLOsrnKWTEvPjdoIUUkEarA/H8rquG2onsPOG55YPjc8r+8nl1JYUMDD3papd361vmrbpPWKl9r0xr/LVx55920eviTVWY9rYnNChrpvd6Q93CdKspUOPUqFc64tXow1qS1Vg97vLJSsuH3Va7C1/9XWQlnz3e5VTradub9+6sEY+UdttLKY17RypPf8ao1g2zSCfvsxcr6jLULaY9n1WMAAGQDwjEAAGLgrrFuxOtrCyxq+jendeQm5zHLyfVmklx1/Bsq2el0LzyoaLamFyBYRYuFFCXbHqtVR0/yenzV1L+s3qHBG5d74Y0p3XRA5ayX3tCxkEDK1sub8bJpG28Gy+I9qw4Hzfs6coPzqOXmR+zvZhVtBR8+ombPDlGzJ/dXwax75Sz8ud53U1HTe2DVwhr/xhuSGuVt2WsWKfzJXfCjCt+9W82f7Kumzw1T/kePeQ3qE6pJKxX3v1OrDh+jkp6He+8tC4wteLWKMHuNi3tdoJXHvRZxgoKK5u2lZm0j3nTFettq1bGvqqjvzSrrup8XlNnraf3hrBrRQqLVgx9Q0f63R6wwq1gr+iqpmBSvUJOXT1X+l6Oq32erjlp1yAuqWLdn3O4u/5MnK2c2tee0ZI/LAv/gVij/60BPv8q+fn1vktt2Q7nN23mVffY8hc6saxVosSgocHTQgX6l2PQZ0uw5BGQAgMzHsEoAAGJQvvYWqnAd5ThVDxiDvYEiyVn8W8TrnfXid0Bt3JZrq2SXMyTZT2R5v75d7bqK5u3q3RQ//6PHlfvv94HbadpWxXv4vZhyw2YVLNtg1yrL5Z129kKgYNBhDcatCXllE/8Y2RDQlae8o7yfp3rN7HP/fL8ytAt9LQref8D7KV+za6Bv1yb9Ynr8FWt3l76bUO36nMW/1/g3OYsivwcq1uke8bqVJ89Q3o+vepML5Pz1WfVG+PO/9X4K37ld5etsGXgc3frKbbmOEsH6WZXU1tOqZKVy53xc7eryjtvWPexw84HeT03yaqgwtGAu3qxaz4YFWzBZ7f469NTqQffXGPbV6/6W/KmC9+6vXC7uc7EXfHn/tvgPb7KNIJvQosr7NifXm6U0J2R2S5vZM5ahlWbQAOmZZ6UVKwMjlUeOcnXBeQytBABkNirHAACIwfe/t9CPyzapdr1TtFQ5C36K+DeRQgLvbzrvktDnPvfPD5T712fVri/beN963Z7jBU3+DJgle1xapaeXs3pxld+vVklkzf1DhsJZAGTPY4MUNFfZ5oNUNOwxrTrpbS9cKLcwK4LchT+pcNY9XhP/ps8NVf6Hj8pZOqfOu7DJCCLe3txPIv/B6sXKWVR9hkILEyvW3Djy3zRtrbKtDvX6RllFYPGuZ6ui7UaR7/efL1U4/WY1e3RPNR11hPI/e17Oyn+VTAUfPxFxllOrCGuQ8hLvtsO5TduovOP2iicbLtx0xMERg7HSTfbX6uFPxTUYM94wZGvS74XJu6ls88G1fJ6q33f4Zyz8b6LRvLmjwYP85VdfkxYupHoMAJDZqBwDACAGL77kqvUfQ3XJljdU+7f8z55T8T5XV7nOqj1yf59R7XcrWrRXXtc9q11vw+SaPx7oLxSqdPPBKu57Y+SVWrWozoN0G0ZY+Or5ERu9l257jGLmumry+hWVAYjNmli26QG1Nq6PFHw5Rcuia3ZfD1ZxU7rN0d6Ps+g35X83QXnfT66cOCBU7vzvvJ/CmXfUWYlVsdYmXgVh7rxvqt6GDWP76zNVrLt1levzvxgRcSbFMpvt06n7PKXbaj2V7niK95Mz7xuvKi7vh1eUExaAWbhoAZ39FLx9gxcWeY+j675xmYjAhvXJXq86envl/via8j94uNr1Fa06qdwmRYhk9ZJAD77ang/rYffaRRGrNG2Ip/KbRPwza2CfO+ejatevOLfmIYe5P09Tk1cu8Hp3VbuvHU9VyS5neuFuPFV88oJX7RgcWlu8z1VVfyH88xQ2u2fEz1g9P0/Dhzka9aKr0lKppFQaM9bVySdSPQYAyFyEYwAARGnBQlfT3pSaaKBO7Paw2jWp2mMq/6vRcgtbqLTn4V6voNy/v1DhtGvklFfvj2SBTWFufL6Gm758cmA4Wpc9VL7u1oEeRAUtvQbx1rw77+c3lP/FqIgH+qXbHCm39fox32fel6O8JvrGLWgRaMIfpmLNqpVO9nyEPhPWDym0usjrR1VTs/sGsuekZNezvR8LsCxgyv/xtYiVNVaJZT8F02/R6qPGq6Jd12q/U7rdccqdHJiRM1STCWd5vdTKN9hZTlmx8r4dX6W6rnJ98puptOeh9R7OWNLrAuXOfj8QlP08VU7Jyiq/Z5Mg5M3+wPtx37xOK099TypsoQYpXa3mj/RWeefdVbZhL2/4p9dAP6+JF9TkzPtWed+O857XSIr7XFRj+JX//WTlf/SoyjbeW+WddlJFu03kNg9UQTkr5nlVjwWfPRtxlkbr+1W6zVENe2yh6/LJUyqwxvths4baEGALv8s2GyDZcN3aiqmc3JjCM3f5fJW/5gfrJbv9n9ywyQkqWneqMgzZWTK7WjCe8/eXVf+mhmrDuqy5pqO++7maOCmwPG6CdNQRrpo2JSADAGQmwjEAAKI0bryrsjJphVrqlm+v0C3bnF3td2zIV6RhX6G8yqT6VGvVyPWGS0YaMlnrenToqZLdzo353pzl87wKq6DiXudHrLAq37BPlYP53F/fVu5PbwSqh0pWqmD6zVV+v2zjvZQIVtlVYj99LlHuHzMDAdMvb1UOZwvy+nxFqPjy1nWT/VX2/SvK+2ValetzVi1Q00nV3xfh7HkPDz9i4vWX2tX7KS4t8tYj7/tJyv393WrN6r2qtbCgp76c8pLAfYU97rqUbHucyjeqXikZyiZvKPj8ecl+ouTmFqjogDvjGqraENVI7HltMuVSyX7qsHr40zUOv42kfNIl0n9VXxZwl/Y8rPovFTT3Qte832ZUrk/hWzeouO8NUm6B8j9/Xrn/fld1qGkD+rAdepCjiZMCCeDy5dIrr0rDhtb75gAASGmEYwAARKG42PWqJ4IKt9pbxb0uVOGMW2J6/srX3FhFA+/1wo1kKuu8u4oOuMOrOItV4ZvXyile7l32hu71OKjGCQJKtz5SBZ8EwkJrjt904llery2nZHmVijqrtCvd4SQlVG6+yq3arsseKi5Zqbyf3lDedxOUO/uDalVDkRT1vVFNrVl7Tb3GalBiQz0jhR/1ld/EG9LqDWtdvVj5P7yivO8mKffvz5UKSnY42avYizd7H9lnKdKkBukk96epcr+d7Id9+1xbY4VdyS5nBSaaKC/xlvN/mOxVDtp7IHxIZbEN/axhqGk0OnVytOsurt6dFVgeNcb1epHl5lI9BgDIPIRjAABEYeqb0pIlVXvylHY51htWVjj9JuUs/7vWv3fleMOxrEG89X9K1uGlBQqlO5zohVb1Ceisn1SwasjNLVTRPtfUOnysZPf/k7N6ofK/HV95Xc7qRRFDjkTNtFhjI/8tBns/zor5Xk8vqyirdWhcYUutHvqYCmbeofwvRlar2ArnNmntNdYv2+oQNZqmbbxhvfZjw+7yvp/ozXiZDOXtuqlkt3O88DGeXCdHZZsc4N22u8a6SmvFy1Xw5jWVi6U7nSo3bDhy+LDaov1vU5PXLpZTusq7zhuaHDb5QckOJ3kTOjTUoQc7endWoHrsr7+kd2ZKfXo3+GYBAEg5hGMAANTBdV29OMZvMLTdtlKXLoHQpLzbvlrVpbfyfnxNub9NV+68b+VY+FNW5IUhbssO3lCosm77q2Ktbo3yXBcNfsirdrKm414fr9WL5RQtkUptHdbwhldVrN3dG4JXtvGeUn6zet7RUhW+eX3lYskuZ8ht07n2v8nJU3Hfm1S2aX/lf/Wicv76PNDnK6/A66FkQy9LtjkyPg3j48Rt0d6bpCCqiQrym3izdFrY6AVqv8/0Zrz0HmNOrjejYMWaXb0eXV5lVyP1VIvEesmV7nSa9xMXBc218pjJyp39YeC9tug3733mVSxVlHnvM7f5Wt5QvvLOu3nvt2j7bpX2GO71drPbzvnrU+WsmC95t71MyisMvIdbbxD4LG20Z93vuzRROOPWwGM1a2+u0u1PqPNvbFjyqrW3UP5nzyr395nKWfZX4PlvtqbK19vO62UXPilEfW21pbTZptJ33weWXxjpqncve1mpHgMAZBbHtT3+OFq8OPYpo4FsYwdNLWbe6l0uLS1RUZ9L4z4dPID4+fQzV2f9n/91efMNNtyoYQeHdnDZunVg1r8lS5Z4ARyA7JPq24Jpb7q68hp/nR6419GWPQjHgGzaDgCpqE2b+J5YrXv+cAAAstyLL/k7qR3Xk3beKamrAwAJY5Vi66ztL48czUE7ACDzEI4BAFCLuX+5mvmuv3zgMEc5OVRNAMgOeXmODjrQ3+ZZ37HZcwjIAACZhXAMAIBajH3ZVXB0Q/Pm0v59eboAZJf+B0gtmgcu2/Zw1IuEYwCAzEI4BgBADVatcjXpFX+5//5Ss2ZUjQHILrbdGzTQX35tirRsOQEZACBzEI4BAFCDya9KK1f+94WZIw0bSjAGIDsdONRRbm7gclGRNGlystcIAID4IRwDACCCsjJXo0OGDu26i7RuB8IxANlprbUc9entL48Z63rbSQAAMgHhGAAAEUx/R/r7H3/5kIMIxgBkt9DG/PPnSzNmJnV1AACIG8IxAADCuK6rkaP8iojNN5O27MHTBCC7bbG5420Pg14cQ+UYACAzEI4BABDm8y+k7773lw87xJHjUDkGAKHVY199LX3/PQEZACD9EY4BABBmREjV2HrrSrvvxlMEAMb6jq3Vzn8uXnyJcAwAkP4IxwAACPHb765mvecvH3yQzdBG1RgAmLw8R0OH+NvEaW9JCxYQkAEA0hvhGAAAIUaN9g/yWq0h7d+XpwcAQg3sLxUUBC6XlUkvjyccAwCkN8IxAAD+s3Chqylv+E/H0CFSkyZUjQFAqFatHO23r788foJUXExABgBIX4RjAAD8Z8zLrkpLA5cL8qWhgwnGACCS4cP87eOSpdIb03ieAADpi3AMAABJq1a5Gjfefyr69pXatCEcA4BIumzoaPvtqjbmd12qxwAA6YlwDAAASZNflZYvDzwVjiMdMpxgDACirR775Rfps895vgAA6YlwDACQ9crKXI1+0a942G0XqVMnwjEAqM1OO0odO/rLo8dQOQYASE+EYwCArDf9Henvf/yn4dBDCMYAoM4DiRxHw4f628t3Z0lz5xKQAQDSD+EYACCrWY+cESP9g7ktNpd6dE/qKgFA2ujXV2rRPHDZWo6NGUs4BgBIP4RjAICs9vEn0vc/+MuHHuzIsaZjAIA6NWvmqP8BVfs3rlxJQAYASC+EYwCArPbs8/5BXKf1pd13S+rqAEDaGTbEUc5/RxWrVgUCMgAA0gnhGAAga339jatPP/OXDz/MUW4uVWMAEIsOHZwqJxZsaGV5OdVjAID0QTgGAMhaz73gH7y1by/tu3dSVwcA0tbwYf6Jhb/+kt57P6mrAwBATAjHAABZ6ZdfXc18t2qvsfx8qsYAoD622lLq1tVfHj2GyjEAQPogHAMAKNurxlq3kgaENJQGAMTGJjIZfqB/gsGGrP/8CwEZACA9EI4BALLO3L9cTXvTX7YDuiZNqBoDgIbYaw+pbRt/+cWXCMcAAOmBcAwAkHVeGOGqoiJwuVkzaejgZK8RAKS/ggJHgwf5JxreeENavJiADACQ+gjHAABZZcFCV6+85i8PGSS1bEnVGADEw+CBUn5+4HJJqTRhEs8rACD1EY4BALLKqNGuSksDlwsKpIOHE4wBQLy0betorz395XHjXZWVUT0GAEhthGMAgKyxbJmrcRP85f77Bw7kAADxc+AQf7v67wJpxkyeXQBAaiMcAwBkjZdellavDlzOzZEOPZhgDADibdNNHW2xeci2dyyVYwCA1EY4BgDICqtWuRoTMnPaPvtIHToQjgFAYxg21N++fvGl9NPPBGQAgNRFOAYAyApjx0lLl/nLhx9KMAYAjWWP3tKabf1lqscAAKmMcAwAkBVVYyNG+lULfXpJG3YmHAOAxpKf72jgAH/59anS0qVUjwEAUhPhGAAgK3qNhVaNHXsMwRgANLZBAx3l5gYul5RIk17hOQcApCbCMQBAxleNjRwVUjXWW9qoC+EYADS2dms62qOPv/zyOFfl5VSPAQBSD+EYACCrqsaOO5pgDAAS5cCQxvz/zJPencVzDwBIPYRjAIDM7jUWUjVmFQxdqBoDgITZYnNpk27+8ksvUzkGAEg9hGMAgIw1Zqy07L+qMceRjj2KqjEASCTHcapUj33yqfTrbwRkAIDUQjgGAMhIK1e6GjmaqjEASLY995Bat/KXx1I9BgBIMYRjAICM9OJLVavGjqFqDACSorDQ0YAB/vJrr0vLl1M9BgBIHYRjAICMs2xZ1V5je1qvsQ0ZUgkAyTJkoKPc/448ioqkV17jtQAApA7CMQBAxnl+hKuVKwOX7WDs+OMIxgAgmdq3d7T77lWHVlZUUD0GAEgNhGMAgIyyYKHrNeIP6tdX6rQ+4RgAJFtoY/65f0nvf5DU1QEAoBLhGAAgozzzrKvi4sDl/HzpmKMJxgAgFWy1pbTRRv7ymLFUjgEAUgPhGAAgY/z1t6sJk/zlwQOlddYmHAOAVOA4jg4c4m+TP/xI+vNPAjIAQPIRjgEAMsaTT7kqKwtcbtpEOvJwgjEASCX77C21bOkvjx1HOAYASD7CMQBARvjtd1dT3vCXhx8otW1LOAYAqaRJE0cDDvCXbdbKVasIyAAAyUU4BgDICI8+bjOfBS63aCEdejDBGACkoiGDHOX8dxSyapX06mvJXiMAQLYjHAMApL0vv3I14x1/+fBDHbVsSTgGAKmoQwdHu+7sL7/0sivXpXoMAJA8hGMAgLRmB1QPPOQfVK3VTho+LKmrBACow7Ch/gmMP2dLH3/CUwYASB7CMQBAWpsxU/r6G3/5+OMcr6cNACB1bbuN1LmzvzxmLJVjAIDkIRwDAKStsjJXDz3iH1Bt2Fnqt19SVwkAEAXHcTRsiH8iY9Z70ty/CMgAAMlBOAYASFsTJ0uzZ/vLp53iKDeXqjEASAf77SM1bx64bC3HXh5HOAYASA7CMQBAWlq1ytUTT/kHUlv3lHbaMamrBACIQbNmjvbv6y9PekVavZqADACQeIRjAIC0NGKUq8WLq1aN2TAdAED6GBoytHLFCun1qUldHQBAliIcAwCknQULXY0c5S/vtae02aYEYwCQbtbv6FSp+h37suvNQgwAQCIRjgEA0s6TT7laXRS4nJcnnXQCwRgApKthQ/1t+C+/Sp9/kdTVAQBkIcIxAEBa+eMPV5Mm+8tDBknrrUs4BgDpasftpY7r+csvjaVyDACQWIRjAIC08tAjrsorApdtlrOjjyQYA4B0lpPjaOhgf1v+zkxp3nwCMgBA4hCOAQDSxkcfu3rnXX/5iMMctW5NOAYA6W7/flLTJoHLdgJk3ATCMQBA4hCOAQDSQlmZq7vv9Q+W1l5bGj4sqasEAIiTFi0c7befvzxxolRcTEAGAEgMwjEAQFoYO076/Q9/+YxTHTVpQtUYAGSKYUP8bfqSpdKbbyV1dQAAWYRwDACQ8hYvcfXEk34FwdY9pT69k7pKAIA427Czo2238ZfHjHXlulSPAQAaH+EYACDlPfKYqxUrA5dzcqRzznLkOFSNAUAmV4/98KP0zbdJXR0AQJYgHAMApLTvf3A1abK/PGSQtFEXgjEAyES77iKts7a//NLLVI4BABof4RgAIGXZcBprwh8cVdNqDen4YwnGACBT5eY6GjLY386/9ba0cCEBGQCgcRGOAQBS1htTpa++9pdPPN7RGmsQjgFAJuu/v1RQELhcViaNn5jsNQIAZDrCMQBASlq1ytUDD/vVAhtvJA3on9RVAgAkQKtWjvbZ218eP8FVaSnVYwCAxkM4BgBISc8+72rBAn/ZmvDbcBsAQOY7MKQx/8JF0tszkro6AIAMRzgGAEg5s+e4GjnaX95rT6nnVgRjAJAtunZ1tGUPf3ksjfkBAI2IcAwAkHJN+G+7w4bQBJYLC6XTTiEYA4BsM2yov+23/pM//MjQSgBA4yAcAwCkXBP+Tz71l485ytHa7QnHACDb9N5datfOX35pLOEYAKBxEI4BAFLGsuWu7n3AP/jZsLN06MFJXSUAQJLk5TkaPNA/OTJ1mrR4CQEZACD+CMcAACnjoUdcLV7sL59/ruMdHAEAstPA/lJ+fuBySak0aXKy1wgAkIkIxwAAKeGrr11NmOgv999f2mpLgjEAyGZt2zras4+//PJ4V2VlVI8BAOKLcAwAkHR2oGNN+INat5JOPZlgDABQtTH//PnSu7N4VgAA8UU4BgBIutFjpF9+9ZdPP81Rq1aEYwAAafPNHG22mf9MjKExPwAgzgjHAABJ9c8/rp54yq8a22Zrqe++SV0lAECKGTbEP2Hy2ed2QoWhlQCA+CEcAwAkjeu6uuNuV0VFgWVrunz+/zlyHKrGAAA+6zvWpo2/PPZlwjEAQPwQjgEAkmbGO9Ks9/zlww+VOnUiGAMAVFVQ4HgzVwZNeUNatpyADAAQH4RjAICkWLXK1V33+Ac2HdeTjjycYAwAENnggY5ycwOXreJ48is8UwCA+CAcAwAkxaOPu/p3gb983v85KiwkHAMARLbWWo5696o6tLK8nOoxAEDDEY4BABLuhx9dvfSyv7zP3tL22xGMAQBqN3yY/13x9z/SzFk8YwCAhiMcAwAkVFmZq5tvdVVREVhu0UI68zSCMQBA3bpvIW26ib/84hgqxwAADUc4BgBIqJGjpR9/8pdPOclR27aEYwCAutlsxqHVY59/If30EwEZAKBhCMcAAAnz52xXTzzpH8T03EpVZh8DAKAue+4hrdnWX37xJcIxAEDDEI4BABKioiIwnLKkNLBcUCBdeL6jnByqxgAA0cvPdzRksP/d8cY0adEiAjIAQP0RjgEAEmL8ROmLL/3l44911Gl9gjEAQOwGDZAK8gOXS0sD3zEAANQX4RgAoNHNm+/qgYf8s/rdukkHD+eJBwDUT5s2jvbe219+eZyrkhKqxwAA9UM4BgBoVK7r6vY7XK1eHVjOzZUuudBRXh5VYwCA+gttzL9osfTm2zybAID6IRwDADQq6wUz631/+fBDpa4bE4wBABrGvku27ukvjx7jeidkAACIFeEYAKDRLF7i6u57/AOVDTpJRx9JMAYAiI+DDvS/U378UfryK55ZAEDsCMcAAI3mrntcLV0WuOw40kUXOCosJBwDAMTHLjtLHTr4yy+OoXIMABA7wjEAQKOY9paraW/6y8OGSFv2IBgDAMRPbq6j4UP975YZM6V//iEgAwDEhnAMABB3Cxa6uv1O/+CkwzrSSScQjAEA4m//flLTpoHLFRXSSy8TjgEAYkM4BgCIK2uGfPOtrpaFDKe89GJHzZoRjgEA4q9FC0cH9POXJ06WVq0iIAMARI9wDAAQV3ZQ8l7I7JQHD5e27kkwBgBoPMOGOt7JGLNihfTa6zzbAIDoEY4BAOJm7l+u7r3PP1vfubN04vEEYwCAxrV+R0c77+Qvj3nJVUUF1WMAgOgQjgEA4qK83NX1N7paXRRYzs2VLr+U2SkBAIlx0IH+yZg/Z0sffsQzDwCIDuEYACAuRr0offmVv3zs0Y426UbVGAAgMbbdRuqyob88egyVYwCA6BCOAQAa7JdfXT36uH8Qstlm0hGH8cQCABLHcRwNH+aflLHKsV9/JSADANSNcAwA0CClpa6uvd5VaWlgubBQuvwSR3l5VI0BABJr332k1q395ZGjCccAAHUjHAMANMgTT7v6+Rd/+bSTHXXqRDAGAEi8wkJHw4b430GvT5X+/ZeADABQO8IxAEC9ff2Nq+df8Je321YaMpgnFACQPEMGSQUFgctlZdKYsYRjAIDaEY4BAOpl9WpX193gqqIisNyiuXTJRY5ycqgaAwAkT+vWjvbv5y+PnyCtWkVABgCoGeEYAKBeHnjY1Zy5/vI5Zztauz3BGAAg+Q4Z7sj57ytpxUpp4uRkrxEAIJURjgEAYvbBh65eHucv9+4l7bcPTyQAIDV07Oio1+7+8ugxrsrKqB4DAERGOAYAiMmy5a5uvMU/wGjTRjr/XDtDT9UYACB1HHqw/700b5701vSkrg4AIIURjgEAYnL3Pa4WLPCXLzrfUZvWBGMAgNTSfQtHPbr7yyNGunJdqscAANURjgEAojbjHVdT3vCXreHxbrsSjAEAUr967MefpE8/S+rqAABSFOEYACAqS5a4uvUO/4x7+/bSWacTjAEAUtduu1r/MX95xCgqxwAA1RGOAQCicsfdrhYv9pcvudBRixaEYwCA1JWT4+iQg/zvqvc/kH79lYAMAFAV4RgAoE7T3nL15lv+8uCB0vbbEYwBAFJfv/2k1q395ZGjCccAAFURjgEAarVokas77vQPJDp0kE47hWAMAJAeCgsdDRvif2+9PlX6918CMgCAj3AMAFAjm9XrtjtdLV3mX3fpRY6aNSMcAwCkjyGDLCQLXC4rk8aMJRwDAPgIxwAANXpjqs1Q6S8fOEzauifBGAAgvbRu7XgzLAeNnyCtWkVABgAIIBwDAES0YIGrO+/xDxw6riedciLBGAAgPR18oCPnv6+xFSuliZOTvUYAgFRBOAYAiDic8pbbXC1fHli2g4lLL3bUpAnhGAAgPXXs6KjX7v7y6DGuysqoHgMAEI4BACJ49TVp1vv+8iEHSVv2IBgDAKS3Qw/2v8vmzVOVmZgBANmLyjEAQBXz5ru6+z7/TPoGnaQTjiMYAwCkv+5bOOrR3V9+foTrVUsDALIb4RgAoJIdINx8q6uVK//7ksiRLrvEUWEh4RgAIDMccZj/nfbLr9L7HyR1dQAAKYBwDABQacIk6cOP/OXDD5U234xgDACQOXbeSdqws7/83AtUjgFAtiMcAwB4/v7b1X0P+AcIG3WRjj2aYAwAkFlychwdHlI99sWX0ldfE5ABQDYjHAMAqKLC1Y23uFq9OvBk5OYGZqcsKCAcAwBknr33lNZeu2rvMQBA9iIcAwDo5fHSp5/5T8RRR0ibdCMYAwBkprw8p8rMlTPflX79jYAMALIV4RgAZLk5c1w9+LB/QNB1Y+noIwnGAACZrf/+UutW/vILIwnHACBbEY4BQJYPp7zhZldFRYHlvDzpf5c63hl1AAAyWZMmjoYN9b/v3pgq/fMPARkAZCPCMQDIYi++JH35lb983DGONupCMAYAyA5DB0tNmwQul5dLI0YRjgFANiIcA4As9eefrh5+1D8I2GxT6bBDkrpKAAAkVKtWjgYO8JcnTpYWLiQgA4BsQzgGAFmovNzV9Te5KikJLBfkB2anZDglACDbWGP+/PzAZfteHDmacAwAsg3hGABkodFjpG++9ZdPON7Rhp0ZTgkAyD7t2jk6YH9/edx4ackSAjIAyCaEYwCQhbNTPvq4v9PffQvp4OFJXSUAAJLqiEMd5eYGLq8uspNIhGMAkE0IxwAgy2anvOlWfzilDSO5+EI7IKBqDACQvdZZx1Hf/fzll16Wli8nIAOAbEE4BgBZZMIk6fMv/OVjj3bUeQOCMQAAjjzMUc5/R0crVwYCMgBAdiAcA4AsMW++qwce8s+Cd92Y2SkBAAjq2NHR3nv6z4cNrVy1iuoxAMgGhGMAkAVc19Vtd9hOfmA5N0e65CJmpwQAINSRRzhy/iuoXrZMenk8zw8AZAPCMQDIAq+/Ib33vr982GFSt64MpwQAIJTN3Ny7l788crSroiKqxwAg0xGOAUCGW7TI1d33+Tv2G3SSjjmSYAwAgEiODvmOXLw40K8TAJDZCMcAIMPdeY/rDQ0xNlTEZqcsLCQcAwAgkq4bO9p1F3/5hZGuioupHgOATEY4BgAZbPo7rt56218eNlTq0Z1gDACAaKvHFiyQXn2N5wsAMhnhGABkqGXLXd1xp3+mu8M60knHE4wBAFCXzTdztMP2/vKzL7gqLaV6DAAyFeEYAGSo+x5wtXCRv3zh+Y6aNSMcAwAg1uqxefOkya/wvAFApiIcA4AM9NHHrl551V/uv7+0/XYEYwAARGurLR1tt62//Mxz9B4DgEyVl+wVAADEl005f8vt/tCPNdeUTj+VYAzpo6ioSJMnT9asWbP0448/aunSpSooKFC7du20zTbbqF+/furRo0eD72fGjBm68MILq1z3/vvv1/l3n3/+uV544QV99dVXWr58udq0aeOt11FHHaWNNtooqvu+99579fzzz6t169YaMWKEdxvx8uijj+rxxx/3Lm+99dZ68MEHo/7ba665Rq+8EiiP2X///XXFFVdU+52//vpLQ4cOjfj3OTk5atq0qZo3b66WLVuqc+fO6tatm7p37+6ti/17ND755BOdfvrpMb0uQGM4/lhHH38S+E6d/680aXKgfycAILMQjgFAhnniaVd//+0vn3eOo5YtCceQHj744ANdffXVWrQoZEywpJKSEq1YsUK///67xo4dqwEDBuj8889XYWFhve7HArebbrop5r8bPXq07rzzTrmuH0D/+++/mjJlit566y0vXOrTp0+tt/H9999r5MiR3uVzzjknrsFYslVUVGjlypXez/z58/XLL79o2rRp3r+ts8463ut2+OGHq0mTJsleVSAqNonNjju4+uDDwPIzz7vqf4CY9RkAMgzhGABkkJ9+djVqlL/cu5fUa3eCMaQHq+S6+OKLvYDFNGvWTL169VKnTp1UXl7uVZFZNZldnjhxoheg3XzzzcrLi3135tZbb/X+Pj8/X6WlpVH9zbfffqu77rrLC8bsPvfdd1+tu+66+umnnzR9+nQvwLNgb/PNN1f79u0j3kZZWZmuv/567zHsvPPO6tu3r9Ld0UcfrTXWWKNyubi42AsyFyxY4D1nc+bM8a7/559/vKq2V199VZdffrm22mqrJK41EL0TjnP0wYeBQHzhQmn8BOmg4TyDAJBJCMcAIEOUl7u65TZX5YFcQc2aSeecSTCG9LB48WJdddVVlcHYdtttp+uuu84bdhjKKsesYswCl3fffVejRo3yKpFi8eabb2rq1Kne5eOOO04PP/xwVH9n1V62fjY08L777lPPnj0r/23SpEne+q5evVovvfSSTj311Ii38dxzz3lhmgV/FgRmgkGDBnkhYU3stRozZoz3Y+GgLVvF3P333+8FiUCq22xTR7vu4urdWf7MlQP6S02b8h0LAJmChvwAkCFeHi99972/fPKJjtZaix13pAcbrrhq1Srv8lprreVVhIUHY8Z6WFnVV25urrf8xBNPeEMko2XVYrfccot3edddd9V+++0X9d9+9tln3v+t4is0GDP9+/fXhhtuWNmTLJI//vjDW19z2mmnae2111Y26NixoxeGWQi5pjVBlLwQ8bzzztOyZcuSvXpA1L3HghYvDnznAgAyB+EYAGSA+fNdPfyo3wNpi82lwQOTukpATKwKLGjgwIFeQ/eaWAi10047eZett5VVgkXLQrclS5Z4zeJjrdyy6rZgQFfTepnwfmnGhmLecMMN3tDLLbfcUsOGDVO22WKLLbznIDgM1p5Pm9gASAfdujrqtbu//MIIV6tW+d+7AID0RjgGABngzntcrV4duGwFNRee7yg3l6oxpA+bATFok002qfP3N9tss8rL0YZj1uvKeoOZs88+26tQi0Ww+b/NUBlJsAoqUrN5G2r5xRdfeLNuXnrppXKc7Px8Wp+x0Go9qxiMtucbkGzHHeN/bpcslUaPSerqAADiiHAMANLc9HdcvTPTXz70YGmjLtl54I30ZU3cg6KZgTL0d3744Yc6f9+aw9ssk8aqzmwYZKyCFWMffvhhlfU1Cxcu9JrPh/5ekM3a+MADD3iXjz322Borz7LF8OF+J3MbSvvNN98kdX2AaG28kaO99vCXR4xytXQp1WMAkAkIxwAgja1c6equu/0dc+uJfezRBGNIP6H9xebNm1fn74f+jlVsWQBVmxtvvNH7vYY0wt97770rZ1206q9ffvnF651l4c4FF1xQ2TNtr732qjaU0/5t44031pFHHqls161btyrDZoO93IB0cMLxjnL/O4JauVJ67gXCMQDIBMxWCQBp7JHHXP27wF++4FxHhYWEY0g/Nkzy33//9S7b0EfrO1YTm/Fw5syZ1SrD2rdvH/H3J06cWNnT7Mwzz9Q666xTr3UcMmSIJk+erJ9//tm7vdA+aUFWlda7d+/K5SlTpni/ZxMIXHbZZZX9thLFQsPnn38+6t//7bff1Nhstk8LCm2YaTBsBNLF+h0d7b+/q4mTAssvvSwddKDLBDgAkOYIxwAgTX3zraux4/zl/faRtt+OYAzpaZ999tGMGTO8yxYmTZs2rVoFVpDNehgeqASrtiJVmN11113e5e22206DBw+u9zpaL7F7771X1113XbVgzAKffv36eRVkwX5i1vg/eN+HHHJIZZ+0oqIijRo1Sm+88YbXa836kG266aY69NBDteOOOyqe5s6d661zqlljjTUqL8cy2yiQCo49ytGUKa5KSqWSEunJZ1xdeB7fvwCQzgjHACANlZW5uuU2V+5/oznsOPOM09kxR/qyIYvWnP2rr77ylq+44grv8gEHHKANNthA5eXl+vHHHzVixAi9/fbbXgBlVVjBZu4WOEWaIdKCLJvRsmnTpnFphN+mTRvdfvvt+uOPP7z1s9u2IaE9e/bU2muvXeV3rceZzcjYsWNHnXjiid51NrTTqtdC+6RZsPf+++97P6eccoqOOeYYZTob3hpkzyGQTtq3dzRkiKtRowPLkydLhx3sqmNHvocBIF0RjgFAGho5WvrlV3/59FMdtWnNTjnSl4VWN910k8466yyvl5eFYSNHjvR+IjnnnHP0yCOPVIZjoT2sgsaOHauPPvrIu3zqqadqXWvKFycW2NlPTWbNmuUNqbTHdckll1TOYGnBmgVjVi1mVWYWClpgdv/99+v111/3quK6d+/uVbnFw9Zbb60HH3ww6t+/5ppr9Morr6ixhVb6tWjRotHvD4i3Iw9zNHGSK3srl1dIjz3p6qrL+R4GgHRFQ34ASDNz/3L15NN+A+Cte0r7903qKgFxseaaa+rxxx/XcccdV2XYXahOnTp5FVnWkyy04qhly5bVhhPed9993mWr6gqdITERwc8tt9ziXbb13Hbbbb3LNoTShlIaa8w/YMAAr6LNKs6uvPJKrbfeel6121NPPaVMFzqUslWrVkldF6A+Wrd2dMhBfhg2dZr000805weAdEXlGACkETtwvv1OV8XFgeWCfOmC85wGDxUDUoVVWJ100kleQPb999/rzz//1PLly73wq0uXLl5vLvPll19W/o01u7dgKdQdd9zhzSRZWFgYl+GUsXjggQe8nmjt2rXzhlAG2bDJiooK7/KgQYOq/I09hv79+3uVYzZ7ow0TDVabZRp7Dqw6MKi+EyQAyXbwcGnsy9KS/7LeBx52dedtfB8DQDoiHAOANPLGVOnDwCgxz1FHOuq0PjviyDzWT8yGF9pPJMHeZGajjTbyKrDCZ2k0xcXFOuigg6K+X5ttMnTopjXSj4XNwPjSSy95l23YZOiQQQv6jIVekWbWXH/99b3/25DS2bNnq2vXrspE1jsutOpvm222Ser6APXVvLmjo46U7rkvUDH20cfSBx+62nEHvpcBIN0wrBIA0sSyZa7uud8fstG5s3T4oUldJSBprD9X0M4775wSr0RJSYluvPFGr8LTZtrs3bt3lX8PBkKhzehDhfZNq2n2zUzw4osvVl628HDzzTdP6voADTFkkLReSDtDqx4rL2d4JQCkGyrHACBN3P+QqyVL/OULznWUn8/ZaWQfa7IfnO3RhiMOGTKk2u/ssssu3jDMutjwxenTp1cu77fffpWXO1sCHYMnn3xSv//+u9cv7dxzz60x/FqyZInKysq86rhQ//77b+XlmgK0dGcVfzZRQdDBBx9c7XkA0ol9D598knTFVYFAzEYMT3ld2r9fstcMABAL9kYAIA189rmrySETyA0cIG21JcEYss/ixYt18803Vy5bw/tIPatsdspoWJP80HDs6quvrtd6/fTTT3r22We9y2effbY3uUBNwyat55b1Fdt+++2r/Psnn3xSGfh17NhRmcZ6yNnMnRYMGnuODj2U8lekvz16S5tvJn37XWD50cdd7bmHDaHmexoA0gXDKgEgxZWUuLr1dn+IRts20iknscONzPPBBx/oww8/rGxaH+67777Taaedpjlz5njLFiCFNrxPFusRZsMpLfTZYYcddMABB9TYzyw4McATTzxRGRIZa1D/5ptvVs6uGd5DLZ3ZzKH33nuvTjzxRC1YsKCyMu7222+v0pMNSFf2uT79VP97+d8F0ouB1oMAgDRB5RgApLhnnnP152x/+eyzHK3RknAMmceGStpMj23bttXWW2+tTp06ec3rrVrMhuN9++23Xj8vs+666+qee+5JieGHI0eO9NbNAq2LL764xt+zGTX33ntvvfHGG17l2LHHHqs+ffpo2bJlmjx5skpLS73fO/roo5VOxo8f7w0lDe29tmLFCi8Is0DTJhcIvm7BCrorr7yycuZRIBNYNffuu7l6Z2Zg+dnnXfU/QGrTmu9rAEgHhGMAkMJ+/c3Vcy/4yzvvJO3ZJ5lrBDS+RYsWadq0aTX+uzW7/7//+z+1a9cuJaqiHn30Ue/yKaec4oV2tTn//PO9vmQ2DDP4E8qqq6z6LJ08/fTTUf2eDX+1YbCHHXaYF3oCmebUkxzNmuWqvMIm1ZAef8LV+ecSjgFAOiAcA4AUVVERGE4ZHHnVtIl03jlO5bAsINPYcMTWrVvr008/1c8//+yFZFZV1bJlS6211lpejy4LxlJpdkMbTmlN/bfYYgsNHz68zt9v1aqVHnnkEb3wwguaOnWq1/MsPz/fq6Ky/lu77rqr0pltn6yCziYfsGoym9Sga9eu2nLLLb1qQLZfyGSdOjkaONDVy+MCyxMmSYMHudp4I763ASDVOW5onXsc2NAHAHV88FYvVouZt3qXS0tLVNTnUqlZW542VDF2nKs77vI30Wed7uig4exgZwoLCSwICs5eGOevYwBpgm1BZlmyxNUhR7hasSKwvHVP6Z47ObGF2rEdAGLXpk0bxRMN+QEgBf37r6uHHvHDks02lYYNTeoqAQCAOrRu7ej4Y/0TWZ99Lk2fwdMGAKmOcAwAUtAdd7tevxKTmyNdeL6j3FyqxgAASHVDBkmdO/vL9z/oqriY6mAASGWEYwCQYqbP8Ge7MoccLHXdmGAMAIB0kJfn6Owz/O/tv/+RRo5O6ioBAOpAOAYAKWTFCterGgtab13puGMIxgAASCfbb+dot5D5NZ593tX8+VSPAUCqIhwDgBRifcYWLvSXLzjPUWEh4RgAAOnmjFMd5eUFLhcVBb7jAQCpiXAMAFLEF1+6GjfBX96/r7TdtgRjAACko44dbZZpf/n1qdJXXxOQAUAqIhwDgBRQUuLqltv8HebWraXTTyUYAwAgnR1zpKM12/rLd9/rqqKCgAwAUg3hGACkgOdekP74018+6wxHrVoRjgEAkM6aNXN08on+9/n3P0ivTknqKgEAIiAcA4Ak+/0P12vUG7TjDtI+eyV1lQAAQJz03U/adBN/2XqPLVtO9RgApBLCMQBIovJyVzff6qq0NLDcpIl0/v85chyqxgAAyAQ5OY7OOcv/Xl+8WHr0ccIxAEglhGMAkERjxlpzXn/5hOMcdehAMAYAQCbpvoWjA/b3l8eNtyGWBGQAkCoIxwAgSWbPcfXwo/6O8RabS8OH8XIAAJCJTjnJUcuWgcuuK91+p+tVkAMAko9wDACSwGaquvFmVyUlgeWCfOmSixzl5lI1BgBAJmrT2vECsqDvvpcmTk7qKgEA/kM4BgBJ8NLL0pdf+cvHHeuo8wYEYwAAZLIBB0ibbeYvWwX54iVUjwFAshGOAUCCzZ3rejNVBW22qXTIQbwMAABkQ3P+885xlPPfUdjy5dKDDxGOAUCyEY4BQKKHU97iqrg4sJxvwykvdJSXR9UYAADZYNNNHA0e6C+/8pr0yacEZACQTIRjAJBAL4+XPv/CXz7mKEdduhCMAQCQTU483lHbNv7yzbe5KioiIAOAZCEcA4AE+etvVw897O/4dusmHX4oTz8AANmmZUtH/3e2f3Lsr7+kx58kHAOAZCEcA4AEDae86RZXq4sCy3l50qUXMZwSAIBs1ae3tPtu/vKoF20GSwIyAEgGwjEASIAxY6VPP/OXjz7S0cYbMZwS/9/efYBHVexvHH8ngYRAQEJTmvSiKKiAighiuUrTq5erXq+9ISAgyl/AAkivAiJNsWAXRBQLdrFgg2sDFOlNQSnSe8j8nznL7tlACElI2+z38zz75JzZs9nNomdn3zPzGwBAtDImUJw/sVhgPyVFGjrCKjmZgAwAchvhGADksFWrU69OWaumdOP1vO0AAES7MmWM7u7oXyxbvlx66ZU8fUkAEJUIxwAgB7mrvwMGWe3fH9iPKyz1fojplAAAIKBtG+msM/13Y8rzVitWMnoMAHIT4RgA5KDnXrBavMTfb3+nUfVqTKcEAAD+9Moe3Y3i4gL7Bw5IA4cwvRIAchPhGADkkF8XWT3/gr9/RgPpmn/zdgMAgNQqVTLqcKd/8WzJksAFNgBA7iAcA4AcsHev1YDBVgdTAvtFi0oPP2AUE8OoMQAAcKR/t5POPMPfdxfYWL0SAHIH4RgA5IAJk6zWrvX3u3UxOukkgjEAAJA2dwHtwZ7Gu6DmuAtsAwdb7dvHCDIAyGmEYwCQzebOs5rxpr/f7HypVUveZgAAkL7y5Y26dvYvpq1eIz0xmXAMAHIa4RgAZKPt262GDPM7sUlJUo//M16xXQAAgGNp00pqep6/P2269MOPBGQAkJMIxwAgm1hrNWS41cZNflvP/zNKKkkwBgAAMrd65Qkl/LbBQ6127SIgA4CcQjgGANnkjZnSl3P8/batpfObEowBAIDMKV3a6P7ufh/iz7+kseMJxwAgpxCOAUA2WLbcalxYp7XKydI9XQjGAABA1rS4wOjSS/z9d2e5i3AEZACQEwjHAOA47dlj1bef1f4Dgf24wtIjfYwSEgjHAABA1nW7x6hsGX/flW/YsIGADACyG+EYABynxx633mpSQXd3MqpVk2AMAAAcnxLFjR7s5fcptm+X+g20Sk4mIAOA7EQ4BgDH4aNPrN6Z5e83O1/615W8pQAAIHs0bmR0w3/9/Z/nS8+9QDgGANmJcAwAsmjlKqvhI/zOably0gM9jLfKFAAAQHa54zajeqf6+1Oel374kYAMALIL4RgAZMHu3Va9+1rt2RvYj42R+j5sVKIEwRgAAMhehQoZPdLbKDExsG+t1H+Q1ZatBGQAkB0IxwAgk6y1Gv6o1arVfluHu4wa1CcYAwAAOaN8eaNe9/t9jU2bpMFDrdcvAQAcH8IxAMikN2ZKH3/i7zdvJv3nGt5GAACQs1pcYHTlP/39b76Vpk3nXQeA40U4BgCZ8Osiq7Hj/Cu0FStID/akzhgAAMgdXToZ1aju7098wuq33xg9BgDHg3AMADJo2zar3o+45dMD+3Fx0sB+rv4H0ykBAEDuiI836tfXqEiRwL7rl/TuZ7V9BwEZAGQV4RgAZEBKitWAwVZ//eW33dfNqFYtgjEAAJC7qlYx6tbV74OsXy8NGGS9/goAIPMIxwAgA55/Ufr2O3+/TWupbWuCMQAAkDfatJJaXZa6/thzL/CvAQBZQTgGAMfw5Ryrp57xr8TWrCHddw/BGAAAyDvGGP3ffUa1avptz0yx+uY7Ro8BQGYRjgFAOlassOo/yO9kJhYL1Blz9T4AAADykuuPDOzv6p8G9q2V+g2w+mMdARkAZAbhGACkU4C/50NWe/YcOmHGyCuAW6kSwRgAAMgfKlYw6vuw3zfZuVN6uI/Vvn0EZACQUYRjAJCG5OTAypSuwG1Qpw5G55xNMAYAAPKXJuca3Xqzv790mTRylJV1Q8kAAMdEOAYAh3EdycfGWf3wo9/mCt5eezVvFQAAyJ9uvdno3HP8/fc+kGa+nZevCAAiB+EYABzmtenSG2/6+/VOlVfw1hW+BQAAyI9iYoz6PGRU/iS/bcxYq5/nM3oMAI6FcAwAwnzxpdXjE/xOZNky0qABFOAHAAD5X4kSxuu3xMUF9pOTpYd6U6AfAI6FcAwADvl1kVW/ga4+R2A/IUEaPtSoTGlGjAEAgMhQu5ZRr/v9vsvWbVLPB6127mQEGQAcDeEYAEhat956Hcd9+wJvR2yMNOARo1o1CcYAAEBkufQfRjfd4O+vWiX17W+9BYcAAEciHAMQ9bZvt7q/p9WWLf5bcd+9rqgtwRgAAIhMd9xm1KK5v//dXGncRMIxAEgL4RiAqLZnj9X9vaxWr/Hbrr9O+uflBGMAACCyC/Q/9IBR7dp+2/TXpTdnEpABwOEIxwBELTe1oPcjVr/86rddfKF0150EYwAAIPIlJBgNG2RUpozfNvoxq/99T0AGAOEIxwBEpZQUqyHDrL79zm9reJa8K6zuSisAAEBBULas0dBBbuXtwP7BFOnhvlZr1hCQAUAQ4RiAqGOt1bgJVh985LfVqS0NGeiWPicYAwAABUvdOkYPP+D3cXbulHo8aL26qwAAwjEAUWjK89K06f5+5crSyOFGRYsSjAEAgILpwhbGK9If9PvvgRFkrGAJAIRjAKLMy69aPf2sf5XU1eAYNdwoqSTBGAAAKNhuvlG69BJ//4cfpaEjrDeqHgCiGdMqAUSN6TOsJkzyO38nlAgEY+XLE4wBAICCzxijnvcbnVbPb3v/A2nik4RjAKIb4RiAqPDWO1Zjxvodv8REadRIo+rVCcYAAED0iI83Xp3VihX8tpdfkV6dRkAGIHoRjgEo8N7/0GrEo36Hr2hR6dHhRnVqE4wBAIDok5RkNGqEUakkv80tVvThRwRkAKIT4RiAAu2T2VaDh7paGoH9IkWkEUON6p1KMAYAAKJXxYrm0IJEftugoVbfzSUgAxB9CMcAFFhfzrHqP9AqJSWwH1dYGjrIqEF9gjEAAIDatQJTLAsXDrwXBw9KD/ex+nURARmA6EI4BqBA+vQz6y1P7jp5TqFC0qABRo0aEowBAAAENTzLqPdDRuZQF2nPXqlHL6s1awjIAEQPwjEABc4HH1o90t8PxmJjpH59jZqcSzAGAABwuItaGN17j99P2rpNuu9+q02bCMgARAfCMQAFytvvWA0c4k+ldCPG+vczuqAZwRgAAMDR/OtKo1tu8vf//Evq3sNqxw4CMgAFH+EYgALj9Tesho30i++7GmODBxCMAQAAZMTttxpd3tbfX75C6vmg1d69BGQACjbCMQAFwitTrUY/5nfc4uOlYUOMzmvCiDEAAICMMMaoezejZuf7bfMXSA/1sTpwgIAMQMFFOAYg4k153mr8RL/DlpAgPTrcqHEjgjEAAIDMKFTI6JHeRmc08Nu+myv1H+TquRKQASiYCMcARCxrrZ58KkVPPeN31IoVk0aPdB06gjEAAICsiI83GjbYqG4dv232Z9LwR11dVwIyAAUP4RiAiA3G3Gix51/024oXlx4bZXRaPYIxAACA41GsmPFG4let6re9O0saN8HVdyUgA1CwEI4BiDhuSP/wkVavTvPbSpaUHh/jrnASjAEAAGSHE04wGjPSqEIFv23adOmJyQRkAAoWwjEAEWX/fqu+/azeftdvK11aGveYUc0aBGMAAADZqUwZozGPGpUp47e9+LL0zBRGjwEoOAjHAESM3butejxg9dkXflv5k6TxjxlVrUIwBgAAkBMqlDd67FGjpCS/7dnnAosiAUBBQDgGICJs22bVrbvV/77326pVlSaOM6pUiWAMAAAgJ1WpYrzariVP8NvcokgvvkxABiDyEY4ByPc2brS6+x6rXxf5bfVOlcaPdUP8CcYAAAByQ/VqRqMfNSpRwm+b9KTV1NcIyABENsIxAPna2t+tOna2WrXKb2vcSBo90nXMCMYAAAByU62axuuHJSb6bY+Pt3p9BgEZgMhFOAYg31q61KpTF6s///LbLmwhDRtsVLQowRgAAEBeqFM7EJAVK+a3jR5r9eZbBGQAIhPhGIB86ef5Vl26WW3Z4rdd3lZ6pLdRXBzBGAAAQF46pa7Ro8ONEhL8tpGjrN6ZRUAGIPIQjgHId7751uq++6127vLbbviv1KO7UWwswRgAAEB+cFo9o5HDjIoU8duGjbB67wMCMgCRhXAMQL7y4UdWvR6y2rfPb+vUwahD+xgZQzAGAACQnzSobzR8iFF8fGDfWmnIMKuPPiEgAxA5CMcA5BuvTrPqP8jq4MHAfkyM1Ot+o//+h1AMAAAgvzrrTKOhg4ziCgf2U1KkgYOsPplNQAYgMhCOAchzKSlW4yemaNwEvwNVuLA04BGjtm0IxgAAAPK7xo2MBg00Xh/OOZgi9RtgvVkBAJDfEY4ByFPJyVaDhli9MtVvcysfuQKvFzQnGAMAAIgUTc4xGtDPqFAhfwTZgMFW775HQAYgfyMcA5Bndu+26vmg1Qcf+W2lS0njxhhveD4AAAAiy/nnGQ3q748gC9Yge/MtAjIA+RfhGIA8sWWr1T33WX0312+rVEmaON6oVi2CMQAAgEjV9DyjYYON4uL8tpGjrKbPICADkD8RjgHIdevXW3XsbLXoN7+tbh1p4jijCuUJxgAAACLd2Y2NRgw1KlLEbxsz1urlVwnIAOQ/hGMActXSZVYd7rb6/Xe/7ezG0tjRRkklCcYAAAAKioZnGa+ObEKC3zZhktVzLxCQAchfCMcA5JoffrTqfI/V5r/9tsv+IW/YfdGiBGMAAAAFTYP6RqNHGiUW89smP2319LMpsq4gGQDkA4RjAHLF7M+suvew2rXLb/vPNdJDD7iCrQRjAAAABdVp9YzGjDIqXtxve/Y5adKTloAMQL5AOAYgx81406pPP6sDB/y2uzsade4Uo5gYgjEAAICCrm4d45XRKHmC3/bSK9KjY6xSUhhBBiBvEY4ByDFuqPxTz6Ro1Bh3VTDQFhsr9X7Q6LprCcUAAACiSa2aRmPHGJVK8tvenCkNGGyVnExABiDvEI4ByBGugzP8Uaspz/ttCUUC9cUuu5RgDAAAIBpVr2Y0bqzRiSf6bR99LD34sNW+fQRkAPIG4RiAbOc6Ng/3tXr7Hb/NDaF3VwrPPYdgDAAAIJqdXNlowuNGJ1f2277+Vofq0xKQAch9hGMAstX27VbdulvN+cpvK3+SNGGc0Sl1CcYAAAAgnVjOaPzjRrVr++/GTz9LXe612rKVgAxA7iIcA5BtNmyw6tTVasFCv61mDWnieHdlkGAMAAAAvqSSRmNHGTWo77ctWSJ17mq9fiUA5BbCMQDZYtVqqw6drVat8tvOPEMa95hRmdIEYwAAADhSYqLRo8ONmpzrt61eI3XqYrX2dwIyALmDcAzAcVuw0KpjZ3eFz29rcYE0cpjxOjwAAADA0RQpYjR4gNHFF/ltf/4l3d3FaslSAjIAOY9wDMBxmfN1oMbYjh1+27+ulPr1MYqPJxgDAADAsRUubNTnIaMrr/Db/t4i3d3V6ru5BGQAchbhGIAse+ddq4e8Zbf9tjtuM7r3HqPYWIIxAAAAZJzrP3a/1+jG6/22PXukHr2s3n2PgAxAzimUg78bQAFlrdUzU6yefc5vi4mR7r/P6PK2hGIAAADIGmOM7rrTqFQpq7HjrKyVDqZIQ4ZZ/fmn1W23GO8YAMhOjBwDkCnJydbrnIQHY3Fx8upEEIwBAAAgO1zdzmhgP+P1M4Nc/3PIcOv1RwEgOxGOAciwXbus7u9lNet9v+2EEtLY0UbnN+UKHgAAALLPBc2NHhtlvP5m0Kz3pB4PWK9fCgDZhXAMQIZs3Gi9gqjz/ue3VaggTRxvdFo9gjEAAABkv9NPM15/s3x5v23uPKlTF6v16wnIAGQPwjEAx7RipdVdd1stW+63nXKK9MR4o5MrE4wBAAAg57j+put31q3jty1fId3Zwern+QRkAI4fBfmBCLRhwwa99dZb+uqrr/Tnn39q165dKlWqlGrUqKFLLrlE//jHP1SoUPb87/3Dj1YPPmy1Y+c22ZTFkl2s0qWW6O8Ni9W69R9ecX7n4YcfVtu2bTP9+9etW6dZs2bp+++/1+rVq7Vz506vyGrx4sVVpUoVNWzYUK1atVL58MuFAAAAKBAOHDig5cuXa/HixaHbsmXLtO/QcuhnnnmmJk6cqFKljB4fI/UbaDXnq8Bjt26T7rnP6v7uUptWmbtg6/qcv/zyixYtWqTffvvN65Nu3bpV27Zt8+53fdFq1aqF+qInnnhi9v/xAPINwjEgwrzzzjsaNWqUdu/enardhWTu5gKzqVOnasCAAapcufJxPdeHH1sNHmq1f+8bSkkeEWrfuEHZ4umnn9aUKVO8TtHhXIdo06ZNXmjmjrn55pt1++23Z88TAwAAIM+5C7wtW7ZMsy+YloQEo8EDpCefsnrx5UBbcnJgJUs306HTXUaxsRkLyWbMmKEJEyYc9f5gX3TevHl65plndNNNN3l9UVbKBAomwjEggrz77rsaNGhQaLRWxYoV1axZM5UoUUKrVq3S559/7n2Qu6tfXbp00eTJk1W2bNlMP4/7/a7D8cTk4DD1/UccEx8f7/0MXtXLrPHjx+uFF14I7bu/oWnTpt5VOdfpWL9+vebMmeNd1du/f7/3t7ifHTt2zNLzAQAAIH9JSUk5IhgLziDYvn17mo+JiTHq0N6oWlWrYSOs9h96+NRp0urVVo/0lhITMz6KzPVp3eyLqlWrev3QhIQEr8/pRpK5YGzjxo3e/lNPPaXNmzerZ8+ex/dHA8iXCMeACOHCouHDh4eCsXbt2um+++5TbGxs6Bj3IX7vvfd60xPdKLJhw4Zp5MiRmXoetzT2mMet3pzpt8XEJqhyxTN07rl1VKdO4OY6EJ07d9aPP/6Y6b/Fvb6XXnoptH/llVeqa9euKlq06BFXE0ePHu2NlnNcmHbVVVfppJNOyvRzAgAAIH+JiYlR9erVvb5l7dq1Q/3M2bNna+DAgek+9rJLjSpWlFf+4+8tgbZvv5M63G01dJBUqVL6AdnZZ5+tBg0aqF69ekctR5KcnKwXX3xRkyZN8vbfeOMNXXrppd5UTwAFC+EYECHcyKngKC33Qd69e3evQxGuQoUKGjx4sDcF0X2Yu5FXLrzK6Af4nj1Wjwyw+uprvy2xmDR44D911plXZtvf8tlnn3lXCoOv+f77708V8gUVK1ZMvXr18qZWunDQPebbb7/1wjQAAABENtfXe/nlQ/Mjs8CtmD75CemBh6yWLA20rVotte9kNbCfdNaZRw/I6tate8zf70KzW265RUuWLNGnn37qtX300UeEY0ABxGqVQATYu3dv6APZue22244IxoLcsPALL7ww1VTMjNi82arrfamDsXLlpAmPm3Q7FlnhRrUFuat1aQVj4Z2S0047LbQfLJIKAAAAnFjOaPxYoxYX+O+Fm5F5b3erV6fZ0KyL4+GK8ofP1ABQ8BCOARHAjZZyAVmwNlfjxo3TPf6iiy4KbX/xxRfH7BQsWWp1Z0erRYv8tho15C2ZXb169gZj4fXKHLcq0LFs2XJorLzElEoAAAAcUai/f1+jW2/22w6mSOMmWPXua7Vr1/EFZOH1z4oUKcK7DxRAhGNABHAF9oNOP/30o44aCzrjjDNSfZind4Xr8y+sOnWx2hC2AmWjhtKEsUZly2Z/MOaET/P84YcfUv19h1u4cGGorpkLBs8///wceU0AAACIXK5Q/+23xqj/I0YJYfnVZ19It99ltWJF1gIyN2th5ky/GG+jRo2y4+UCyGcIx4AIsHLlytB25cqVj3l8UlKSEhMTQ/srVqw44hg3mmzK81YP9bE6NCjN88/LpZHDjIoVy5lgzHEBV7DOw8GDB3X33Xd7hU6XLVvmFeF3K1QuXbrUW17bFep3x7hi/f379/dqUwAAAABpuaiF0ZOTjKqc7Lf9/nugDtmHH2UsIHN1ft0CUtOnT/dq+QZLgrgFqdq2bcsbDxRAFOQHIoBbNjqobNmyGXqMO86FTIc/3tm3z2rIcKuPP/HbYmOkrp2N/nVVYAntnORqjI0ZM0YPPfSQV2zfBWJTpkzxbmlNwbz44ovVvn17ValSJUdfFwAAACJftapGkydJQ0dYfTo70OYuBvcfZDV/oVXXu43i4vz+7oYNG3TFFVek+zvPOecc70It0yqBgomRY0AE2L17d2g7ISEhQ48JPy788Zs2WXW+J3Uw5lakHDncqN2/TI4HY0ElS5bU2LFjNXz4cG8RgaOpVKmSV5A/o6EgAAAAULSoUb8+Rvd0MQpf++nNmdJdnaxWr87YKDLXBx0xYoQee+wxnXDCCbyxQAHFyDEgArih3UGFCxfO0GPi4uJC28Fi/gsWWj3cx2rz3/5xbpbmsEFGJ5+cO6FYOLcU9rPPPusNWw/WE6tYsaIX0P3++++aM2eOli9f7nVGpk2bpmHDhql27dq5/joBAAAQeVyf8up2Ut06Up9HrDZuCrQvXSbd1t7qni7S5W3klSPp0qVLqgvLa9eu1dy5c7Vx40b16tVLl112mVfuw13gBVDwEI4BESB8dccDBw5k6DH79+8Pbbvh32/OtBrzuFVycurC+65oaYniuR+MuRFjM2bM8LZbtmypnj17HjEqznVM3HHvv/++1q9f73VaXnzxRUaRAQAAIMNOP83omcnSgMFWc+cF2ty15+EjrebOlXr8X4Kuv/76Ix7n+t2u7/nkk09q1qxZmj9/vlcnt0yZMrz7QAHDtEogArhi9EF79uzJ0GOCo8WcL75M0MjRqYMxdxXNFd7Pi2DMdS6CwZhbfbN3795pThd1f7e7z02rDK4W5DonAAAAQGYkJRmv79u5k1GhQqlXs7zldquffj5ymqWbsXHrrbeqQ4cO3r6b2TBkyBDeeKAAIhwDIkDp0qVD225od0a4wqJB8xf6j3ezLXs/6OovxKhQodwPxpyXXnoptH3NNdd4BfqPxt137bXXhvY///xzb6VNAAAAIDNiYoz+c43RExOMV1okaMNGqeu9Vk88laIDB47sZ1533XWhi9VfffWV1q1bxxsPFDCEY0AEqF69emjb1T84lq1bt4ZWqnSMCTz+pBOlieOMLrs0b0Ixx61M6eqIBdWsWfOYjwk/Zvv27dqyZUuOvT4AAAAUbHVqGz39hFHb1n5bSor0wotS+45Wy1fYI2r5Vq1aNbS/ePHi3Hy5AHIB4RgQAerUqRPaXrBgQbojp9zVrsFDfgxrKSGZ8mp4lvTUE8brDOSl8JUzM+rwvzcmhlMXAAAAjm81y149Yrz6u4mJfrsr1n/HXVYvv2p18KDfB00Oq0+S4pI0AAUK3zCBCHDuued6RfWDI6fmzTtUSfQwf/xh1amr1Zdfzg61mdjmuu0Wo1EjjEqWzNtgzHFLYLuVg4KWLVt2zMeEH+Ou3LmVLQEAAIDjdVELo+eeMd6F5CC3/tWESVZ3d7VascJ6F3dXrVoVur9cuXK88UABQzgGRAAXjF100UWh/WeffTbVaCq3Pes9q1vusPr1l5WyKZ+G7uvapY1uvzVGsbF5H4wFw61atWqF9l977bV0r74dPHhQU6dODe03aNCAkWMAAADINieWMxo90ujerkZhi8Rr4S/SrXdade32dGgleHeR9pRTTuHdBwoYwjEgQrRv317xhz6tf/zxR40aNcoLjrbvsOrb32rwMHdVa70OHnjIDfz2jjv77PN13X/OPOrvvPLKK71Rae42efLkXPtbrrjiitC2WxJ70KBBqVbXDHJX6fr166dffvkl1Hb11Vfn2usEAABAZHF92mD/1vV107J06VL17NlTP/zwQ+girSvW3+5fRs8+ZXTqoezL2p3av/cxLZzvLyZ1/fXXq1D4cpcACgT+rwYixEknnaQePXpowIABoRFXn332tXbvbaY9e4rLpqyRTflc0t7Q8b1798y253/ggQeOaFu5cmVo+/XXX/dW7wnXsGFD/fvf/z7ica6jMnv2bH3//ffe/rvvvqs5c+aoadOmqlSpUmipbNfmppEGtWzZUs2bN8+2vwkAAAB5a/r06aE+YdCff/6Zqr+ZVj90yJAhWX5ON+vCrYDubklJSTr11FNVpUoVFS9e3JuhUK/uNm3fulKrVrrXFRgx5piY5lq15jpt2WKVlJQ/ZmUAyB6EY0AEadOmjfdh7kaNuVFVGzf+IenVI46rW7euF6KVLVs2257bhVnpWbRokXcLl5CQkOax7mrbiBEjNHLkSM2aNctr27ZtW2j7cK6T4pbQ7tixY5ZfPwAAAPKfX3/9Nd1+pluF/Vj90MwKr3/rVkF3F3gPv8ibWhHFFLpJJvYGffRJIX071+quO6TL2yrflC4BcHwIx4AIU71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" + ] + }, + "metadata": { + "image/png": { + "height": 361, + "width": 611 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = az.plot_posterior(idata_gauss_demo, var_names=[\"delta\"], ref_val=0.0, figsize=(6, 3.5))\n", + "ax.set_title(r\"Posterior on $\\delta$ at $N=200$ (one simulated dataset)\");" + ] + }, + { + "cell_type": "markdown", + "id": "3f515a2e", + "metadata": {}, + "source": [ + "The posterior concentrates above zero; under the decision rule the team would conclude the new flow lifts revenue. That is a single draw of the experiment. Assurance asks how often this conclusion would arrive if we re-ran the experiment many times under our actual prior.\n", + "\n", + "The conjugate posterior on $\\delta$ given $\\hat d = \\bar y_B - \\bar y_A$ has mean and variance:\n", + "\n", + "$$\n", + "\\sigma_d^2 = \\frac{2\\sigma_{\\text{obs}}^2}{N}, \\quad\n", + "\\tau^2_{\\text{post}} = \\left(\\frac{1}{\\tau_0^2} + \\frac{1}{\\sigma_d^2}\\right)^{-1}, \\quad\n", + "\\mu_{\\text{post}} = \\tau^2_{\\text{post}}\\left(\\frac{\\mu_0}{\\tau_0^2} + \\frac{\\hat d}{\\sigma_d^2}\\right),\n", + "$$\n", + "\n", + "where $(\\mu_0, \\tau_0)$ is the prior on $\\delta$. We check this matches the PyMC posterior before relying on it." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "42e775d9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:33.189849Z", + "iopub.status.busy": "2026-05-28T16:43:33.189765Z", + "iopub.status.idle": "2026-05-28T16:43:33.197403Z", + "shell.execute_reply": "2026-05-28T16:43:33.196916Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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analyticalMCMC
posterior mean of delta0.7450.751
posterior sd of delta0.3120.312
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" + ], + "text/plain": [ + " analytical MCMC\n", + "posterior mean of delta 0.745 0.751\n", + "posterior sd of delta 0.312 0.312" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def gaussian_posterior_delta(d_hat, N, sigma_obs, prior):\n", + " sigma_d_sq = 2 * sigma_obs**2 / N\n", + " post_var = 1.0 / (1.0 / prior.sigma**2 + 1.0 / sigma_d_sq)\n", + " post_mean = post_var * (prior.mu / prior.sigma**2 + d_hat / sigma_d_sq)\n", + " return post_mean, np.sqrt(post_var)\n", + "\n", + "\n", + "d_hat_demo = y_B_demo.mean() - y_A_demo.mean()\n", + "analytical_mu, analytical_sd = gaussian_posterior_delta(\n", + " d_hat_demo, N=200, sigma_obs=SIGMA_OBS, prior=EFFECT_PRIOR\n", + ")\n", + "mcmc_mu = idata_gauss_demo.posterior[\"delta\"].mean().item()\n", + "mcmc_sd = idata_gauss_demo.posterior[\"delta\"].std().item()\n", + "\n", + "pd.DataFrame(\n", + " {\"analytical\": [analytical_mu, analytical_sd], \"MCMC\": [mcmc_mu, mcmc_sd]},\n", + " index=[\"posterior mean of delta\", \"posterior sd of delta\"],\n", + ").round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "fb2d9901", + "metadata": {}, + "source": [ + "The two posteriors agree to within MCMC noise. The closed form will carry the decision step; the generative model will carry the data step." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "62b0f123", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:33.198644Z", + "iopub.status.busy": "2026-05-28T16:43:33.198556Z", + "iopub.status.idle": "2026-05-28T16:43:33.808133Z", + "shell.execute_reply": "2026-05-28T16:43:33.807546Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " N assurance\n", + "0 100 0.267\n", + "1 200 0.398\n", + "2 400 0.501\n", + "3 800 0.603\n", + "4 1600 0.640\n", + "5 3200 0.681\n", + "6 6400 0.717" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng):\n", + " seed = int(rng.integers(2**31 - 1))\n", + " pp = pm.sample_prior_predictive(\n", + " draws=n_sims, model=gaussian_generative_model(N, prior), random_seed=seed\n", + " )\n", + " y_A = pp.prior[\"y_A\"].values.reshape(n_sims, N)\n", + " y_B = pp.prior[\"y_B\"].values.reshape(n_sims, N)\n", + " d_hat = y_B.mean(axis=1) - y_A.mean(axis=1)\n", + " post_mu, post_sd = gaussian_posterior_delta(d_hat, N, sigma_obs, prior)\n", + " prob_positive = 1.0 - stats.norm.cdf(0.0, loc=post_mu, scale=post_sd)\n", + " return float((prob_positive > threshold).mean())\n", + "\n", + "\n", + "N_GRID = np.array([100, 200, 400, 800, 1600, 3200, 6400])\n", + "N_SIMS = 1000\n", + "\n", + "assurance_df = assurance_curve(\n", + " gaussian_assurance_at_N,\n", + " N_grid=N_GRID,\n", + " n_sims=N_SIMS,\n", + " rng=np.random.default_rng(RANDOM_SEED),\n", + " prior=EFFECT_PRIOR,\n", + " sigma_obs=SIGMA_OBS,\n", + " threshold=DECISION_THRESHOLD,\n", + ")\n", + "assurance_df" + ] + }, + { + "cell_type": "markdown", + "id": "191f92bd", + "metadata": {}, + "source": [ + "For comparison we compute frequentist power at a single fixed effect size. The convention is to set the effect to the prior mean — which is itself an arbitrary choice, and a revealing one." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "87ca67e9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:33.809679Z", + "iopub.status.busy": "2026-05-28T16:43:33.809545Z", + "iopub.status.idle": "2026-05-28T16:43:34.041546Z", + "shell.execute_reply": "2026-05-28T16:43:34.041113Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 461, + "width": 711 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "def gaussian_power(N, delta_alt, sigma_obs, alpha=0.05):\n", + " sigma_d = np.sqrt(2 * sigma_obs**2 / N)\n", + " z_crit = stats.norm.ppf(1 - alpha)\n", + " return 1.0 - stats.norm.cdf(z_crit - delta_alt / sigma_d)\n", + "\n", + "\n", + "power_at_prior_mean = np.array([gaussian_power(N, EFFECT_PRIOR.mu, SIGMA_OBS) for N in N_GRID])\n", + "power_at_optimistic = np.array(\n", + " [gaussian_power(N, EFFECT_PRIOR.mu + EFFECT_PRIOR.sigma, SIGMA_OBS) for N in N_GRID]\n", + ")\n", + "power_at_pessimistic = np.array(\n", + " [gaussian_power(N, max(EFFECT_PRIOR.mu - EFFECT_PRIOR.sigma, 0.01), SIGMA_OBS) for N in N_GRID]\n", + ")\n", + "\n", + "prior_prob_positive = 1.0 - stats.norm.cdf(0.0, EFFECT_PRIOR.mu, EFFECT_PRIOR.sigma)\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "ax.plot(N_GRID, assurance_df[\"assurance\"], marker=\"o\", linewidth=2, label=\"Assurance (Bayesian)\")\n", + "ax.plot(\n", + " N_GRID,\n", + " power_at_prior_mean,\n", + " marker=\"s\",\n", + " linestyle=\"--\",\n", + " label=r\"Power at $\\theta_0 = \\mu_{\\text{prior}}$\",\n", + ")\n", + "ax.plot(\n", + " N_GRID,\n", + " power_at_optimistic,\n", + " marker=\"^\",\n", + " linestyle=\":\",\n", + " alpha=0.7,\n", + " label=r\"Power at $\\mu + \\sigma$\",\n", + ")\n", + "ax.plot(\n", + " N_GRID,\n", + " power_at_pessimistic,\n", + " marker=\"v\",\n", + " linestyle=\":\",\n", + " alpha=0.7,\n", + " label=r\"Power at $\\mu - \\sigma$\",\n", + ")\n", + "ax.axhline(\n", + " prior_prob_positive,\n", + " color=\"C0\",\n", + " linestyle=\"-.\",\n", + " alpha=0.6,\n", + " label=rf\"Assurance ceiling $P(\\delta>0)$ = {prior_prob_positive:.2f}\",\n", + ")\n", + "ax.set_xscale(\"log\")\n", + "ax.set_xlabel(\"Sample size per arm\")\n", + "ax.set_ylabel(\"Probability of declaring uplift\")\n", + "ax.set_title(\"Assurance vs frequentist power, Gaussian outcome\")\n", + "ax.legend(loc=\"lower right\", fontsize=9);" + ] + }, + { + "cell_type": "markdown", + "id": "45105766", + "metadata": {}, + "source": [ + "The three power curves fan out because each conditions on a single assumed world. Power at $\\mu + \\sigma$ assumes an effect of $0.9$ and saturates almost immediately; power at $\\mu - \\sigma$ is pinned near the test's five-percent floor, because $\\mu - \\sigma$ lands at essentially zero effect, where even large samples gain almost no detection. Power at the prior mean is the curve a frequentist planner would actually quote. The assurance curve is the prior-weighted integral across the entire fan, and it tells a different story than any single power calculation.\n", + "\n", + "The story is in the crossing. Between $N=400$ and $N=800$ the assurance curve drops below power-at-the-mean, having sat above it at smaller samples. At small samples assurance is buoyed by the optimistic tail of the prior: it counts the plausible draws above $0.4$, effects of $0.8$ or $1.0$ that even a hundred observations have a real chance of detecting, which the single-point power calculation never sees. The frequentist who quotes power at the mean is being pessimistic about small experiments in a way the prior is not.\n", + "\n", + "At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one, because it carries the prior's honest admission that the effect might be zero or negative. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\\delta > 0) \\approx 0.79$. No sample size buys more assurance than the prior's confidence that there is something to find, and that ceiling, rather than any point on a power curve, is the quantity a planner should internalise before paying for a larger experiment.\n", + "\n", + ":::{admonition} The minimum detectable effect leads a double life\n", + ":class: note\n", + "\n", + "Power is usually quoted at a \"minimum detectable effect\" (MDE), and that single number carries more weight than it appears to. Read one way, the MDE is a *utility threshold*: an effect smaller than this is not worth shipping, so failing to detect it does not matter, and powering at the MDE is a coherent design target. Read another way, it is a *forecast*: a quiet assumption that the true effect is about this large and positive, sometimes reverse-engineered from the sample size a team can already afford. The frequentist machinery itself is innocent here, because the false-positive rate $\\alpha$ already governs the null case; it is the floor that the $\\mu - \\sigma$ curve traces. What conditions the null away is the reported summary, \"$X\\%$ power to detect the MDE\", which is computed entirely inside the world where the effect is real and exactly the MDE. Assurance does not abolish this assumption. It relocates it from an implicit point to an explicit prior, where the probability that the effect is null or harmful becomes visible and open to argument.\n", + ":::\n", + "\n", + "## The same machinery on a binary outcome\n", + "\n", + "The conversion-rate version of this experiment is the canonical Bayesian A/B test setup, and the same machinery transfers without conceptual modification. The generative model fixes the baseline conversion rate, places the planning prior on the lift, and emits binomial counts per arm. The inference step places Beta priors on the per-arm rates and reads off the posterior probability that the lift is positive ({cite:p}`stucchio2015bayesian`, {cite:p}`kruschke2014doing`)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d1c18825", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:34.042994Z", + "iopub.status.busy": "2026-05-28T16:43:34.042888Z", + "iopub.status.idle": "2026-05-28T16:43:34.047171Z", + "shell.execute_reply": "2026-05-28T16:43:34.046475Z" + } + }, + "outputs": [], + "source": [ + "BASELINE_RATE = 0.10\n", + "RATE_LIFT_PRIOR = EffectPrior(mu=0.01, sigma=0.012)\n", + "BETA_PRIOR_KAPPA = 200 # prior concentration, in units of pseudo-observations\n", + "\n", + "\n", + "def beta_prior_params(p_mean, kappa):\n", + " return kappa * p_mean, kappa * (1 - p_mean)\n", + "\n", + "\n", + "def bernoulli_generative_model(N, rate_prior, baseline_rate):\n", + " \"\"\"Unconditioned model: fixed baseline rate, planning prior on the lift, binomial counts per arm.\"\"\"\n", + " with pm.Model() as model:\n", + " lift = pm.Normal(\"lift\", mu=rate_prior.mu, sigma=rate_prior.sigma)\n", + " p_B = pm.Deterministic(\"p_B\", pm.math.clip(baseline_rate + lift, 1e-4, 1 - 1e-4))\n", + " pm.Binomial(\"n_A\", n=N, p=baseline_rate)\n", + " pm.Binomial(\"n_B\", n=N, p=p_B)\n", + " return model\n", + "\n", + "\n", + "def bernoulli_assurance_at_N(\n", + " N, n_sims, rate_prior, baseline_rate, kappa, threshold, rng, n_post=2000\n", + "):\n", + " seed = int(rng.integers(2**31 - 1))\n", + " pp = pm.sample_prior_predictive(\n", + " draws=n_sims,\n", + " model=bernoulli_generative_model(N, rate_prior, baseline_rate),\n", + " random_seed=seed,\n", + " )\n", + " n_A = pp.prior[\"n_A\"].values.reshape(-1).astype(int)\n", + " n_B = pp.prior[\"n_B\"].values.reshape(-1).astype(int)\n", + " alpha0, beta0 = beta_prior_params(baseline_rate, kappa)\n", + " p_A_post = rng.beta(alpha0 + n_A[:, None], beta0 + N - n_A[:, None], size=(n_sims, n_post))\n", + " p_B_post = rng.beta(alpha0 + n_B[:, None], beta0 + N - n_B[:, None], size=(n_sims, n_post))\n", + " prob_positive = (p_B_post > p_A_post).mean(axis=1)\n", + " return float((prob_positive > threshold).mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bc3152a4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:34.048727Z", + "iopub.status.busy": "2026-05-28T16:43:34.048622Z", + "iopub.status.idle": "2026-05-28T16:43:35.166470Z", + "shell.execute_reply": "2026-05-28T16:43:35.166026Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [lift, n_A, n_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [lift, n_A, n_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [lift, n_A, n_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [lift, n_A, n_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [lift, n_A, n_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [lift, n_A, n_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [lift, n_A, n_B]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 461, + "width": 711 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "N_GRID_BERN = np.array([500, 1000, 2000, 4000, 8000, 16000, 32000])\n", + "\n", + "assurance_bern_df = assurance_curve(\n", + " bernoulli_assurance_at_N,\n", + " N_grid=N_GRID_BERN,\n", + " n_sims=N_SIMS,\n", + " rng=np.random.default_rng(RANDOM_SEED),\n", + " rate_prior=RATE_LIFT_PRIOR,\n", + " baseline_rate=BASELINE_RATE,\n", + " kappa=BETA_PRIOR_KAPPA,\n", + " threshold=DECISION_THRESHOLD,\n", + ")\n", + "\n", + "\n", + "def bernoulli_power(N, p_A, lift_alt, alpha=0.05):\n", + " p_B = p_A + lift_alt\n", + " p_bar = 0.5 * (p_A + p_B)\n", + " se_null = np.sqrt(2 * p_bar * (1 - p_bar) / N)\n", + " se_alt = np.sqrt(p_A * (1 - p_A) / N + p_B * (1 - p_B) / N)\n", + " z_crit = stats.norm.ppf(1 - alpha)\n", + " return 1.0 - stats.norm.cdf((z_crit * se_null - lift_alt) / se_alt)\n", + "\n", + "\n", + "power_bern_at_prior_mean = np.array(\n", + " [bernoulli_power(N, BASELINE_RATE, RATE_LIFT_PRIOR.mu) for N in N_GRID_BERN]\n", + ")\n", + "power_bern_at_optimistic = np.array(\n", + " [\n", + " bernoulli_power(N, BASELINE_RATE, RATE_LIFT_PRIOR.mu + RATE_LIFT_PRIOR.sigma)\n", + " for N in N_GRID_BERN\n", + " ]\n", + ")\n", + "power_bern_at_pessimistic = np.array(\n", + " [\n", + " bernoulli_power(N, BASELINE_RATE, max(RATE_LIFT_PRIOR.mu - RATE_LIFT_PRIOR.sigma, 1e-4))\n", + " for N in N_GRID_BERN\n", + " ]\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "ax.plot(\n", + " N_GRID_BERN,\n", + " assurance_bern_df[\"assurance\"],\n", + " marker=\"o\",\n", + " linewidth=2,\n", + " label=\"Assurance (Bayesian)\",\n", + ")\n", + "ax.plot(\n", + " N_GRID_BERN,\n", + " power_bern_at_prior_mean,\n", + " marker=\"s\",\n", + " linestyle=\"--\",\n", + " label=r\"Power at lift $=\\mu_{\\text{prior}}$\",\n", + ")\n", + "ax.plot(\n", + " N_GRID_BERN,\n", + " power_bern_at_optimistic,\n", + " marker=\"^\",\n", + " linestyle=\":\",\n", + " alpha=0.7,\n", + " label=r\"Power at $\\mu + \\sigma$\",\n", + ")\n", + "ax.plot(\n", + " N_GRID_BERN,\n", + " power_bern_at_pessimistic,\n", + " marker=\"v\",\n", + " linestyle=\":\",\n", + " alpha=0.7,\n", + " label=r\"Power at $\\mu - \\sigma$\",\n", + ")\n", + "ax.set_xscale(\"log\")\n", + "ax.set_xlabel(\"Sample size per arm\")\n", + "ax.set_ylabel(\"Probability of declaring uplift\")\n", + "ax.set_title(\"Assurance vs frequentist power, Bernoulli outcome\")\n", + "ax.legend(loc=\"lower right\");" + ] + }, + { + "cell_type": "markdown", + "id": "45306fae", + "metadata": {}, + "source": [ + "The picture is the same. Here the generative model produced the synthetic experiments and the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that the conjugate decision matches a full PyMC inference fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "67f5d44e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:35.167955Z", + "iopub.status.busy": "2026-05-28T16:43:35.167846Z", + "iopub.status.idle": "2026-05-28T16:43:36.543870Z", + "shell.execute_reply": "2026-05-28T16:43:36.543462Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [n_A, n_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [p_A, p_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 0 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 361, + "width": 611 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "def bernoulli_two_arm_model(n_A, n_B, N, baseline_rate, kappa):\n", + " alpha0, beta0 = beta_prior_params(baseline_rate, kappa)\n", + " with pm.Model() as model:\n", + " p_A = pm.Beta(\"p_A\", alpha=alpha0, beta=beta0)\n", + " p_B = pm.Beta(\"p_B\", alpha=alpha0, beta=beta0)\n", + " pm.Deterministic(\"lift\", p_B - p_A)\n", + " pm.Binomial(\"obs_A\", n=N, p=p_A, observed=n_A)\n", + " pm.Binomial(\"obs_B\", n=N, p=p_B, observed=n_B)\n", + " return model\n", + "\n", + "\n", + "demo_model_bern = pm.do(\n", + " bernoulli_generative_model(N=4000, rate_prior=RATE_LIFT_PRIOR, baseline_rate=BASELINE_RATE),\n", + " {\"lift\": 0.015},\n", + ")\n", + "demo_draw_bern = pm.sample_prior_predictive(draws=1, model=demo_model_bern, random_seed=RANDOM_SEED)\n", + "n_A_demo = int(demo_draw_bern.prior[\"n_A\"].values.reshape(-1)[0])\n", + "n_B_demo = int(demo_draw_bern.prior[\"n_B\"].values.reshape(-1)[0])\n", + "\n", + "with bernoulli_two_arm_model(\n", + " n_A_demo, n_B_demo, N=4000, baseline_rate=BASELINE_RATE, kappa=BETA_PRIOR_KAPPA\n", + "):\n", + " idata_bern_demo = pm.sample(\n", + " draws=1000,\n", + " tune=1000,\n", + " chains=2,\n", + " target_accept=0.95,\n", + " random_seed=RANDOM_SEED,\n", + " progressbar=False,\n", + " )\n", + "\n", + "az.plot_posterior(idata_bern_demo, var_names=[\"lift\"], ref_val=0.0, figsize=(6, 3.5))\n", + "plt.title(r\"Posterior on conversion lift at $N=4000$\");" + ] + }, + { + "cell_type": "markdown", + "id": "005827fd", + "metadata": {}, + "source": [ + "The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The machinery is the same.\n", + "\n", + "## The cost of an under-informed prior\n", + "\n", + "The assurance curve is conditional on the prior. A tightly informative prior centred near the truth produces an optimistic-looking curve; a flat prior produces a curve that requires substantially more data to reach the same assurance. Neither is wrong. They report what each prior commitment buys the planner." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "279cd463", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:36.545635Z", + "iopub.status.busy": "2026-05-28T16:43:36.545514Z", + "iopub.status.idle": "2026-05-28T16:43:38.412662Z", + "shell.execute_reply": "2026-05-28T16:43:38.412173Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 461, + "width": 711 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "INFORMED_PRIOR = EffectPrior(mu=0.4, sigma=0.3)\n", + "FLAT_PRIOR = EffectPrior(mu=0.4, sigma=1.5)\n", + "SCEPTICAL_PRIOR = EffectPrior(mu=0.1, sigma=0.5)\n", + "\n", + "prior_comparison = {}\n", + "for name, prior in [\n", + " (\"Informed\", INFORMED_PRIOR),\n", + " (\"Flat\", FLAT_PRIOR),\n", + " (\"Sceptical\", SCEPTICAL_PRIOR),\n", + "]:\n", + " df = assurance_curve(\n", + " gaussian_assurance_at_N,\n", + " N_grid=N_GRID,\n", + " n_sims=N_SIMS,\n", + " rng=np.random.default_rng(RANDOM_SEED),\n", + " prior=prior,\n", + " sigma_obs=SIGMA_OBS,\n", + " threshold=DECISION_THRESHOLD,\n", + " )\n", + " prior_comparison[name] = df\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "for name, df in prior_comparison.items():\n", + " ax.plot(df[\"N\"], df[\"assurance\"], marker=\"o\", linewidth=2, label=name)\n", + "ax.set_xscale(\"log\")\n", + "ax.set_xlabel(\"Sample size per arm\")\n", + "ax.set_ylabel(\"Assurance\")\n", + "ax.set_title(\"Assurance under three prior commitments\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "fda04dc5", + "metadata": {}, + "source": [ + "The three curves disagree most where they should: at small sample sizes where the data has not yet overwhelmed the prior. The informed prior reaches high assurance fastest because it is willing to commit to a narrow effect range; the sceptical prior assumes a smaller effect and so needs more data to detect it; the flat prior carries enough probability mass over zero that even moderate datasets do not push the posterior reliably above the decision threshold. The point is not to advocate for a specific prior. It is to make the dependence of the assurance number on the prior commitment visible, so that the prior can be the subject of negotiation rather than a hidden choice.\n", + "\n", + "## Planning as a posterior over future posteriors\n", + "\n", + "The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty, and a fair one, because the lights are the only ones we have.\n", + "\n", + "This re-frames the planning conversation. Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. And it does its work by making explicit an assumption the power calculation leaves buried: that there is a believed distribution of effects at all. Naming the assumption a conventional analysis would rather not state, and giving it a posterior, is the move this sequence repeats.\n", + "\n", + "In each case here, the Gaussian and the Bernoulli, the spine never changed. What changed was the likelihood. The same is true across this notebook and its successors. Planning, interpretation, synthesis are three questions the same machinery answers in turn; the posterior is the constant, the question changes. We have just seen the first of the three.\n", + "\n", + "## Authors\n", + "\n", + "- Authored by [Nathaniel Forde](https://nathanielf.github.io/) in May 2026.\n", + "\n", + "## References\n", + "\n", + ":::{bibliography}\n", + ":filter: docname in docnames\n", + ":::\n", + "\n", + "## Watermark" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b30e957b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:38.414030Z", + "iopub.status.busy": "2026-05-28T16:43:38.413924Z", + "iopub.status.idle": "2026-05-28T16:43:38.429931Z", + "shell.execute_reply": "2026-05-28T16:43:38.429362Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Thu May 28 2026\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.12.12\n", + "IPython version : 9.8.0\n", + "\n", + "pytensor: 2.38.2\n", + "xarray : 2025.12.0\n", + "\n", + "pymc : 5.28.5\n", + "numpy : 2.3.5\n", + "matplotlib: 3.10.8\n", + "seaborn : 0.13.2\n", + "pandas : 2.3.3\n", + "arviz : 0.23.0\n", + "scipy : 1.16.3\n", + "\n", + "Watermark: 2.5.0\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w -p pytensor,xarray" + ] + }, + { + "cell_type": "markdown", + "id": "dbbeeb58", + "metadata": {}, + "source": [ + ":::{include} ../page_footer.md\n", + ":::" + ] + } + ], + "metadata": { + "jupytext": { + "default_lexer": "ipython3" + }, + "kernelspec": { + "display_name": "pymc-marketing-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/causal_inference/assurance_planning_simulation.myst.md b/examples/causal_inference/assurance_planning_simulation.myst.md new file mode 100644 index 000000000..e877141f3 --- /dev/null +++ b/examples/causal_inference/assurance_planning_simulation.myst.md @@ -0,0 +1,530 @@ +--- +jupytext: + default_lexer: ipython3 + text_representation: + extension: .md + format_name: myst + format_version: 0.13 +kernelspec: + display_name: pymc-marketing-dev + language: python + name: python3 +--- + +(assurance_planning)= +# Assurance Planning via Simulation + +:::{post} May 2026 +:tags: experimentation, decision analysis, power analysis, assurance, simulation +:category: intermediate, reference +:author: Nathaniel Forde +::: + +## The question planners are actually asking + +When a team approves an experiment, the question they are silently asking is whether the experiment will be useful. The frequentist quantity called "power" answers a different question entirely: given some specific effect size we have not observed, what is the probability the experiment crosses a fixed threshold? The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold. + +Assurance is the Bayesian alternative, and its construction is mechanical: draw an effect from the prior, generate a dataset, fit the model, apply the decision rule, repeat. The result is a single number with a clear meaning: the probability that the experiment is about to conclude what its stakeholders need it to. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the conclusion is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. + +:::{admonition} Where this lands in regulatory practice +:class: note + +The assurance framing here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\Pr(\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference, which is exactly the split the generative and inference models below are built on. The setting differs from product experimentation; the machinery is identical. +::: + +```{code-cell} ipython3 +import warnings + +from dataclasses import dataclass + +import arviz as az +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pymc as pm +import seaborn as sns + +from scipy import stats + +warnings.filterwarnings("ignore", category=RuntimeWarning) +warnings.filterwarnings("ignore", category=UserWarning) +``` + +```{code-cell} ipython3 +%config InlineBackend.figure_format = 'retina' +az.style.use("arviz-darkgrid") +rng = np.random.default_rng(42) +RANDOM_SEED = 42 +``` + +### The scenario + +The running scenario across this notebook and its two successors is a product-experimentation question: an e-commerce team is evaluating a redesigned checkout flow. The continuous outcome is revenue per visitor; the binary outcome is conversion. We will not commit to a particular vertical or product; the structure is generic to any A/B test with one of these outcome types. What matters is that the same prior beliefs and the same decision rule will be asked of two different likelihoods. The Gaussian case will carry the argument; the Bernoulli case will confirm it travels. + +```{code-cell} ipython3 +@dataclass +class EffectPrior: + """Prior beliefs about a treatment effect, on the scale of the outcome.""" + + mu: float + sigma: float + + def sample(self, rng, size=1): + return rng.normal(self.mu, self.sigma, size=size) +``` + +## Power and assurance: two questions in the same wardrobe + +Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null: + +$$ +\text{Power}(N) = P\!\left(\text{reject } H_0 \mid \theta = \theta_0,\; N\right). +$$ + +The conditioning bar is the load-bearing part. The planner who has confidently named $\theta_0$ has implicitly used up the uncertainty that motivated the experiment in the first place. Assurance refuses the substitution. Instead it integrates over the planner's prior beliefs about the effect: + +$$ +\text{Assurance}(N) = \int P\!\left(\text{decision} \mid \theta,\; N\right)\, \pi(\theta)\, d\theta. +$$ + +This is the marginal probability of a successful decision, where "successful" is whatever decision rule the team has agreed in advance and $\pi(\theta)$ is the team's prior over the effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in. + +A planning question is not a hypothesis test. When stakeholders ask "will the experiment work?", they are not committing to a specific effect size and asking after its detectability; they are asking after the experiment in the world they actually inhabit, with all of its uncertainty intact. The frequentist answer requires them to invent a specific world before the calculation will proceed. The Bayesian answer integrates over the worlds they already hold open. + +## The simulation engine + +Assurance is an expectation over the *prior predictive distribution*: the joint distribution of effect and data that the model generates before any real data arrive. As a Monte Carlo estimator the integral becomes a procedure in three steps, repeated for each candidate sample size: + +1. **Sample the prior predictive** of an unconditioned generative model. Each draw is a complete synthetic experiment: an effect drawn from the prior, and a dataset of size $N$ generated under it. +2. **Fit the analysis model** to each synthetic dataset and apply the decision rule. +3. **Record** the fraction of synthetic experiments in which the decision succeeded. + +The first step is the one worth dwelling on. Drawing a hypothetical truth and then generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive. + +```{code-cell} ipython3 +def assurance_curve(assurance_fn, N_grid, n_sims, rng, **kwargs): + """Estimate the assurance curve by evaluating assurance at each sample size.""" + records = [] + for N in N_grid: + a = assurance_fn(N=int(N), n_sims=n_sims, rng=rng, **kwargs) + records.append({"N": int(N), "assurance": a}) + return pd.DataFrame(records) +``` + +## Gaussian outcome: revenue per visitor + +The Gaussian case is the cleanest place to make the mechanics explicit. Each arm produces a revenue-per-visitor outcome with known noise scale $\sigma_{\text{obs}}$ (we treat this as known here; in practice it is estimated from a pilot or historical data). The team is interested in the treatment effect $\delta = \mu_B - \mu_A$, and holds a prior over $\delta$ informed by past experiments in similar product surfaces. That prior is not conjured from nothing: {ref}`meta_analysis_experiments` shows how a meta-analysis of past experiments produces exactly this kind of prior, so the synthesis at the end of one experiment's lifecycle is the planning input at the start of the next. + +Two models do the work. The *generative model* is unconditioned: it places the planning prior on $\delta$ and generates synthetic datasets, and it is the object we hand to `pm.sample_prior_predictive`. The *inference model* conditions on a dataset and returns the posterior on $\delta$; the decision rule asks whether the posterior probability of a positive effect exceeds a threshold (0.95 below). For the assurance loop the conjugate Normal–Normal posterior on $\delta$ admits a closed form, which lets us evaluate the decision on each synthetic experiment without running MCMC every time. We verify that closed form against the inference model's MCMC posterior at a single sample size before relying on it. + +```{code-cell} ipython3 +SIGMA_OBS = 4.0 +DECISION_THRESHOLD = 0.95 +BASELINE_REVENUE = 10.0 +EFFECT_PRIOR = EffectPrior(mu=0.4, sigma=0.5) + + +def gaussian_generative_model(N, prior): + """Unconditioned model: planning prior on the effect plus the data-generating likelihood.""" + with pm.Model() as model: + delta = pm.Normal("delta", mu=prior.mu, sigma=prior.sigma) + pm.Normal("y_A", mu=BASELINE_REVENUE, sigma=SIGMA_OBS, shape=N) + pm.Normal("y_B", mu=BASELINE_REVENUE + delta, sigma=SIGMA_OBS, shape=N) + return model + + +def gaussian_two_arm_model(y_A, y_B, prior): + """Inference model: conditions on an observed dataset and returns the posterior on the effect.""" + with pm.Model() as model: + mu_A = pm.Normal("mu_A", mu=BASELINE_REVENUE, sigma=5.0) + delta = pm.Normal("delta", mu=prior.mu, sigma=prior.sigma) + mu_B = pm.Deterministic("mu_B", mu_A + delta) + pm.Normal("obs_A", mu=mu_A, sigma=SIGMA_OBS, observed=y_A) + pm.Normal("obs_B", mu=mu_B, sigma=SIGMA_OBS, observed=y_B) + return model +``` + +A single synthetic experiment makes the inference model concrete. We use the do-operator to fix the generative effect at a chosen value, draw one dataset from the prior predictive, and condition the inference model on it. + +```{code-cell} ipython3 +demo_true_delta = 0.6 +demo_model = pm.do(gaussian_generative_model(N=200, prior=EFFECT_PRIOR), {"delta": demo_true_delta}) +demo_draw = pm.sample_prior_predictive(draws=1, model=demo_model, random_seed=RANDOM_SEED) +y_A_demo = demo_draw.prior["y_A"].values.reshape(-1) +y_B_demo = demo_draw.prior["y_B"].values.reshape(-1) + +with gaussian_two_arm_model(y_A_demo, y_B_demo, EFFECT_PRIOR): + idata_gauss_demo = pm.sample( + draws=1000, + tune=1000, + chains=2, + target_accept=0.95, + random_seed=RANDOM_SEED, + progressbar=False, + ) +``` + +```{code-cell} ipython3 +ax = az.plot_posterior(idata_gauss_demo, var_names=["delta"], ref_val=0.0, figsize=(6, 3.5)) +ax.set_title(r"Posterior on $\delta$ at $N=200$ (one simulated dataset)"); +``` + +The posterior concentrates above zero; under the decision rule the team would conclude the new flow lifts revenue. That is a single draw of the experiment. Assurance asks how often this conclusion would arrive if we re-ran the experiment many times under our actual prior. + +The conjugate posterior on $\delta$ given $\hat d = \bar y_B - \bar y_A$ has mean and variance: + +$$ +\sigma_d^2 = \frac{2\sigma_{\text{obs}}^2}{N}, \quad +\tau^2_{\text{post}} = \left(\frac{1}{\tau_0^2} + \frac{1}{\sigma_d^2}\right)^{-1}, \quad +\mu_{\text{post}} = \tau^2_{\text{post}}\left(\frac{\mu_0}{\tau_0^2} + \frac{\hat d}{\sigma_d^2}\right), +$$ + +where $(\mu_0, \tau_0)$ is the prior on $\delta$. We check this matches the PyMC posterior before relying on it. + +```{code-cell} ipython3 +def gaussian_posterior_delta(d_hat, N, sigma_obs, prior): + sigma_d_sq = 2 * sigma_obs**2 / N + post_var = 1.0 / (1.0 / prior.sigma**2 + 1.0 / sigma_d_sq) + post_mean = post_var * (prior.mu / prior.sigma**2 + d_hat / sigma_d_sq) + return post_mean, np.sqrt(post_var) + + +d_hat_demo = y_B_demo.mean() - y_A_demo.mean() +analytical_mu, analytical_sd = gaussian_posterior_delta( + d_hat_demo, N=200, sigma_obs=SIGMA_OBS, prior=EFFECT_PRIOR +) +mcmc_mu = idata_gauss_demo.posterior["delta"].mean().item() +mcmc_sd = idata_gauss_demo.posterior["delta"].std().item() + +pd.DataFrame( + {"analytical": [analytical_mu, analytical_sd], "MCMC": [mcmc_mu, mcmc_sd]}, + index=["posterior mean of delta", "posterior sd of delta"], +).round(3) +``` + +The two posteriors agree to within MCMC noise. The closed form will carry the decision step; the generative model will carry the data step. + +```{code-cell} ipython3 +def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng): + seed = int(rng.integers(2**31 - 1)) + pp = pm.sample_prior_predictive( + draws=n_sims, model=gaussian_generative_model(N, prior), random_seed=seed + ) + y_A = pp.prior["y_A"].values.reshape(n_sims, N) + y_B = pp.prior["y_B"].values.reshape(n_sims, N) + d_hat = y_B.mean(axis=1) - y_A.mean(axis=1) + post_mu, post_sd = gaussian_posterior_delta(d_hat, N, sigma_obs, prior) + prob_positive = 1.0 - stats.norm.cdf(0.0, loc=post_mu, scale=post_sd) + return float((prob_positive > threshold).mean()) + + +N_GRID = np.array([100, 200, 400, 800, 1600, 3200, 6400]) +N_SIMS = 1000 + +assurance_df = assurance_curve( + gaussian_assurance_at_N, + N_grid=N_GRID, + n_sims=N_SIMS, + rng=np.random.default_rng(RANDOM_SEED), + prior=EFFECT_PRIOR, + sigma_obs=SIGMA_OBS, + threshold=DECISION_THRESHOLD, +) +assurance_df +``` + +For comparison we compute frequentist power at a single fixed effect size. The convention is to set the effect to the prior mean — which is itself an arbitrary choice, and a revealing one. + +```{code-cell} ipython3 +def gaussian_power(N, delta_alt, sigma_obs, alpha=0.05): + sigma_d = np.sqrt(2 * sigma_obs**2 / N) + z_crit = stats.norm.ppf(1 - alpha) + return 1.0 - stats.norm.cdf(z_crit - delta_alt / sigma_d) + + +power_at_prior_mean = np.array([gaussian_power(N, EFFECT_PRIOR.mu, SIGMA_OBS) for N in N_GRID]) +power_at_optimistic = np.array( + [gaussian_power(N, EFFECT_PRIOR.mu + EFFECT_PRIOR.sigma, SIGMA_OBS) for N in N_GRID] +) +power_at_pessimistic = np.array( + [gaussian_power(N, max(EFFECT_PRIOR.mu - EFFECT_PRIOR.sigma, 0.01), SIGMA_OBS) for N in N_GRID] +) + +prior_prob_positive = 1.0 - stats.norm.cdf(0.0, EFFECT_PRIOR.mu, EFFECT_PRIOR.sigma) + +fig, ax = plt.subplots(figsize=(7, 4.5)) +ax.plot(N_GRID, assurance_df["assurance"], marker="o", linewidth=2, label="Assurance (Bayesian)") +ax.plot( + N_GRID, + power_at_prior_mean, + marker="s", + linestyle="--", + label=r"Power at $\theta_0 = \mu_{\text{prior}}$", +) +ax.plot( + N_GRID, + power_at_optimistic, + marker="^", + linestyle=":", + alpha=0.7, + label=r"Power at $\mu + \sigma$", +) +ax.plot( + N_GRID, + power_at_pessimistic, + marker="v", + linestyle=":", + alpha=0.7, + label=r"Power at $\mu - \sigma$", +) +ax.axhline( + prior_prob_positive, + color="C0", + linestyle="-.", + alpha=0.6, + label=rf"Assurance ceiling $P(\delta>0)$ = {prior_prob_positive:.2f}", +) +ax.set_xscale("log") +ax.set_xlabel("Sample size per arm") +ax.set_ylabel("Probability of declaring uplift") +ax.set_title("Assurance vs frequentist power, Gaussian outcome") +ax.legend(loc="lower right", fontsize=9); +``` + +The three power curves fan out because each conditions on a single assumed world. Power at $\mu + \sigma$ assumes an effect of $0.9$ and saturates almost immediately; power at $\mu - \sigma$ is pinned near the test's five-percent floor, because $\mu - \sigma$ lands at essentially zero effect, where even large samples gain almost no detection. Power at the prior mean is the curve a frequentist planner would actually quote. The assurance curve is the prior-weighted integral across the entire fan, and it tells a different story than any single power calculation. + +The story is in the crossing. Between $N=400$ and $N=800$ the assurance curve drops below power-at-the-mean, having sat above it at smaller samples. At small samples assurance is buoyed by the optimistic tail of the prior: it counts the plausible draws above $0.4$, effects of $0.8$ or $1.0$ that even a hundred observations have a real chance of detecting, which the single-point power calculation never sees. The frequentist who quotes power at the mean is being pessimistic about small experiments in a way the prior is not. + +At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one, because it carries the prior's honest admission that the effect might be zero or negative. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\delta > 0) \approx 0.79$. No sample size buys more assurance than the prior's confidence that there is something to find, and that ceiling, rather than any point on a power curve, is the quantity a planner should internalise before paying for a larger experiment. + +:::{admonition} The minimum detectable effect leads a double life +:class: note + +Power is usually quoted at a "minimum detectable effect" (MDE), and that single number carries more weight than it appears to. Read one way, the MDE is a *utility threshold*: an effect smaller than this is not worth shipping, so failing to detect it does not matter, and powering at the MDE is a coherent design target. Read another way, it is a *forecast*: a quiet assumption that the true effect is about this large and positive, sometimes reverse-engineered from the sample size a team can already afford. The frequentist machinery itself is innocent here, because the false-positive rate $\alpha$ already governs the null case; it is the floor that the $\mu - \sigma$ curve traces. What conditions the null away is the reported summary, "$X\%$ power to detect the MDE", which is computed entirely inside the world where the effect is real and exactly the MDE. Assurance does not abolish this assumption. It relocates it from an implicit point to an explicit prior, where the probability that the effect is null or harmful becomes visible and open to argument. +::: + +## The same machinery on a binary outcome + +The conversion-rate version of this experiment is the canonical Bayesian A/B test setup, and the same machinery transfers without conceptual modification. The generative model fixes the baseline conversion rate, places the planning prior on the lift, and emits binomial counts per arm. The inference step places Beta priors on the per-arm rates and reads off the posterior probability that the lift is positive ({cite:p}`stucchio2015bayesian`, {cite:p}`kruschke2014doing`). + +```{code-cell} ipython3 +BASELINE_RATE = 0.10 +RATE_LIFT_PRIOR = EffectPrior(mu=0.01, sigma=0.012) +BETA_PRIOR_KAPPA = 200 # prior concentration, in units of pseudo-observations + + +def beta_prior_params(p_mean, kappa): + return kappa * p_mean, kappa * (1 - p_mean) + + +def bernoulli_generative_model(N, rate_prior, baseline_rate): + """Unconditioned model: fixed baseline rate, planning prior on the lift, binomial counts per arm.""" + with pm.Model() as model: + lift = pm.Normal("lift", mu=rate_prior.mu, sigma=rate_prior.sigma) + p_B = pm.Deterministic("p_B", pm.math.clip(baseline_rate + lift, 1e-4, 1 - 1e-4)) + pm.Binomial("n_A", n=N, p=baseline_rate) + pm.Binomial("n_B", n=N, p=p_B) + return model + + +def bernoulli_assurance_at_N( + N, n_sims, rate_prior, baseline_rate, kappa, threshold, rng, n_post=2000 +): + seed = int(rng.integers(2**31 - 1)) + pp = pm.sample_prior_predictive( + draws=n_sims, + model=bernoulli_generative_model(N, rate_prior, baseline_rate), + random_seed=seed, + ) + n_A = pp.prior["n_A"].values.reshape(-1).astype(int) + n_B = pp.prior["n_B"].values.reshape(-1).astype(int) + alpha0, beta0 = beta_prior_params(baseline_rate, kappa) + p_A_post = rng.beta(alpha0 + n_A[:, None], beta0 + N - n_A[:, None], size=(n_sims, n_post)) + p_B_post = rng.beta(alpha0 + n_B[:, None], beta0 + N - n_B[:, None], size=(n_sims, n_post)) + prob_positive = (p_B_post > p_A_post).mean(axis=1) + return float((prob_positive > threshold).mean()) +``` + +```{code-cell} ipython3 +N_GRID_BERN = np.array([500, 1000, 2000, 4000, 8000, 16000, 32000]) + +assurance_bern_df = assurance_curve( + bernoulli_assurance_at_N, + N_grid=N_GRID_BERN, + n_sims=N_SIMS, + rng=np.random.default_rng(RANDOM_SEED), + rate_prior=RATE_LIFT_PRIOR, + baseline_rate=BASELINE_RATE, + kappa=BETA_PRIOR_KAPPA, + threshold=DECISION_THRESHOLD, +) + + +def bernoulli_power(N, p_A, lift_alt, alpha=0.05): + p_B = p_A + lift_alt + p_bar = 0.5 * (p_A + p_B) + se_null = np.sqrt(2 * p_bar * (1 - p_bar) / N) + se_alt = np.sqrt(p_A * (1 - p_A) / N + p_B * (1 - p_B) / N) + z_crit = stats.norm.ppf(1 - alpha) + return 1.0 - stats.norm.cdf((z_crit * se_null - lift_alt) / se_alt) + + +power_bern_at_prior_mean = np.array( + [bernoulli_power(N, BASELINE_RATE, RATE_LIFT_PRIOR.mu) for N in N_GRID_BERN] +) +power_bern_at_optimistic = np.array( + [ + bernoulli_power(N, BASELINE_RATE, RATE_LIFT_PRIOR.mu + RATE_LIFT_PRIOR.sigma) + for N in N_GRID_BERN + ] +) +power_bern_at_pessimistic = np.array( + [ + bernoulli_power(N, BASELINE_RATE, max(RATE_LIFT_PRIOR.mu - RATE_LIFT_PRIOR.sigma, 1e-4)) + for N in N_GRID_BERN + ] +) + +fig, ax = plt.subplots(figsize=(7, 4.5)) +ax.plot( + N_GRID_BERN, + assurance_bern_df["assurance"], + marker="o", + linewidth=2, + label="Assurance (Bayesian)", +) +ax.plot( + N_GRID_BERN, + power_bern_at_prior_mean, + marker="s", + linestyle="--", + label=r"Power at lift $=\mu_{\text{prior}}$", +) +ax.plot( + N_GRID_BERN, + power_bern_at_optimistic, + marker="^", + linestyle=":", + alpha=0.7, + label=r"Power at $\mu + \sigma$", +) +ax.plot( + N_GRID_BERN, + power_bern_at_pessimistic, + marker="v", + linestyle=":", + alpha=0.7, + label=r"Power at $\mu - \sigma$", +) +ax.set_xscale("log") +ax.set_xlabel("Sample size per arm") +ax.set_ylabel("Probability of declaring uplift") +ax.set_title("Assurance vs frequentist power, Bernoulli outcome") +ax.legend(loc="lower right"); +``` + +The picture is the same. Here the generative model produced the synthetic experiments and the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that the conjugate decision matches a full PyMC inference fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably. + +```{code-cell} ipython3 +def bernoulli_two_arm_model(n_A, n_B, N, baseline_rate, kappa): + alpha0, beta0 = beta_prior_params(baseline_rate, kappa) + with pm.Model() as model: + p_A = pm.Beta("p_A", alpha=alpha0, beta=beta0) + p_B = pm.Beta("p_B", alpha=alpha0, beta=beta0) + pm.Deterministic("lift", p_B - p_A) + pm.Binomial("obs_A", n=N, p=p_A, observed=n_A) + pm.Binomial("obs_B", n=N, p=p_B, observed=n_B) + return model + + +demo_model_bern = pm.do( + bernoulli_generative_model(N=4000, rate_prior=RATE_LIFT_PRIOR, baseline_rate=BASELINE_RATE), + {"lift": 0.015}, +) +demo_draw_bern = pm.sample_prior_predictive(draws=1, model=demo_model_bern, random_seed=RANDOM_SEED) +n_A_demo = int(demo_draw_bern.prior["n_A"].values.reshape(-1)[0]) +n_B_demo = int(demo_draw_bern.prior["n_B"].values.reshape(-1)[0]) + +with bernoulli_two_arm_model( + n_A_demo, n_B_demo, N=4000, baseline_rate=BASELINE_RATE, kappa=BETA_PRIOR_KAPPA +): + idata_bern_demo = pm.sample( + draws=1000, + tune=1000, + chains=2, + target_accept=0.95, + random_seed=RANDOM_SEED, + progressbar=False, + ) + +az.plot_posterior(idata_bern_demo, var_names=["lift"], ref_val=0.0, figsize=(6, 3.5)) +plt.title(r"Posterior on conversion lift at $N=4000$"); +``` + +The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The machinery is the same. + +## The cost of an under-informed prior + +The assurance curve is conditional on the prior. A tightly informative prior centred near the truth produces an optimistic-looking curve; a flat prior produces a curve that requires substantially more data to reach the same assurance. Neither is wrong. They report what each prior commitment buys the planner. + +```{code-cell} ipython3 +INFORMED_PRIOR = EffectPrior(mu=0.4, sigma=0.3) +FLAT_PRIOR = EffectPrior(mu=0.4, sigma=1.5) +SCEPTICAL_PRIOR = EffectPrior(mu=0.1, sigma=0.5) + +prior_comparison = {} +for name, prior in [ + ("Informed", INFORMED_PRIOR), + ("Flat", FLAT_PRIOR), + ("Sceptical", SCEPTICAL_PRIOR), +]: + df = assurance_curve( + gaussian_assurance_at_N, + N_grid=N_GRID, + n_sims=N_SIMS, + rng=np.random.default_rng(RANDOM_SEED), + prior=prior, + sigma_obs=SIGMA_OBS, + threshold=DECISION_THRESHOLD, + ) + prior_comparison[name] = df + +fig, ax = plt.subplots(figsize=(7, 4.5)) +for name, df in prior_comparison.items(): + ax.plot(df["N"], df["assurance"], marker="o", linewidth=2, label=name) +ax.set_xscale("log") +ax.set_xlabel("Sample size per arm") +ax.set_ylabel("Assurance") +ax.set_title("Assurance under three prior commitments") +ax.legend(); +``` + +The three curves disagree most where they should: at small sample sizes where the data has not yet overwhelmed the prior. The informed prior reaches high assurance fastest because it is willing to commit to a narrow effect range; the sceptical prior assumes a smaller effect and so needs more data to detect it; the flat prior carries enough probability mass over zero that even moderate datasets do not push the posterior reliably above the decision threshold. The point is not to advocate for a specific prior. It is to make the dependence of the assurance number on the prior commitment visible, so that the prior can be the subject of negotiation rather than a hidden choice. + +## Planning as a posterior over future posteriors + +The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty, and a fair one, because the lights are the only ones we have. + +This re-frames the planning conversation. Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. And it does its work by making explicit an assumption the power calculation leaves buried: that there is a believed distribution of effects at all. Naming the assumption a conventional analysis would rather not state, and giving it a posterior, is the move this sequence repeats. + +In each case here, the Gaussian and the Bernoulli, the spine never changed. What changed was the likelihood. The same is true across this notebook and its successors. Planning, interpretation, synthesis are three questions the same machinery answers in turn; the posterior is the constant, the question changes. We have just seen the first of the three. + +## Authors + +- Authored by [Nathaniel Forde](https://nathanielf.github.io/) in May 2026. + +## References + +:::{bibliography} +:filter: docname in docnames +::: + +## Watermark + +```{code-cell} ipython3 +%load_ext watermark +%watermark -n -u -v -iv -w -p pytensor,xarray +``` + +:::{include} ../page_footer.md +::: diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.ipynb b/examples/causal_inference/multiple_experiments_meta_analysis.ipynb new file mode 100644 index 000000000..f86f4ee53 --- /dev/null +++ b/examples/causal_inference/multiple_experiments_meta_analysis.ipynb @@ -0,0 +1,1702 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "58b4b1c0", + "metadata": {}, + "source": [ + "(meta_analysis_experiments)=\n", + "# Multiple Experiments and Bayesian Meta-analysis\n", + "\n", + ":::{post} May 2026\n", + ":tags: experimentation, meta-analysis, hierarchical models, partial pooling, replication\n", + ":category: intermediate, reference\n", + ":author: Nathaniel Forde\n", + ":::\n", + "\n", + "## The replication-as-evidence problem\n", + "\n", + "Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question — \"did it work?\" — and gets, depending on which colleague answers, two incompatible defaults: vote-counting (\"four out of eight worked, so it's a wash\"), or pool-everything (\"the combined estimate is positive, so it works\"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that lets the markets be different, and lets that difference be part of what we are estimating.\n", + "\n", + "This notebook builds that model. The hierarchical Bayesian meta-analysis treats each experiment as a noisy estimate of its own market's true effect, and treats the per-market effects as draws from a population whose mean and variance are themselves the quantities of substantive interest {cite:p}`borenstein2009meta`, {cite:p}`higgins2009meta`. The structure is the one Rubin used for the 8-schools problem in 1981 {cite:p}`rubin1981estimation`, {cite:p}`gelman2013bayesian`, transposed to product experimentation. We develop it on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the third of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`sensitivity_confounding` for the interpretation counterpart. Readers wanting a deeper view of the partial-pooling vocabulary should also consult the existing PyMC notebooks on {ref}`multilevel_modeling` and {ref}`hierarchical_partial_pooling`, which we treat as predecessors rather than re-derive.\n", + "\n", + ":::{admonition} Where this lands in regulatory practice\n", + ":class: note\n", + "\n", + "The hierarchical model here is the borrowing mechanism a regulator now describes by name. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials presents subgroup analysis through a *one-way Bayesian hierarchical model* whose subgroup estimate is \"a weighted average of its raw estimated treatment effect ... and the overall estimated treatment effect\" {cite:p}`fda2026bayesian`, the shrinkage picture this notebook builds. The same guidance treats hierarchical models as the main way to borrow information across related trials by assuming the group parameters are drawn from a common distribution, which is the $\\theta_k \\sim \\mathcal{N}(\\mu, \\tau)$ structure below. Borrowing across studies, and the use of one trial's synthesis as the next trial's prior, is the regulatory form of the lifecycle loop these three notebooks trace.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d5b9bd7c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:13.317886Z", + "iopub.status.busy": "2026-05-28T17:00:13.317662Z", + "iopub.status.idle": "2026-05-28T17:00:17.044240Z", + "shell.execute_reply": "2026-05-28T17:00:17.043619Z" + } + }, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pymc as pm\n", + "import seaborn as sns\n", + "\n", + "from scipy import stats\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b8567665", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:17.046097Z", + "iopub.status.busy": "2026-05-28T17:00:17.045902Z", + "iopub.status.idle": "2026-05-28T17:00:17.049304Z", + "shell.execute_reply": "2026-05-28T17:00:17.048862Z" + } + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "az.style.use(\"arviz-darkgrid\")\n", + "rng = np.random.default_rng(11)\n", + "RANDOM_SEED = 11" + ] + }, + { + "cell_type": "markdown", + "id": "ebedae74", + "metadata": {}, + "source": [ + "## Heterogeneity is not new: groups within a single study\n", + "\n", + "The across-study problem looks novel, but its structure appears inside a single experiment whenever the treatment effect varies across user segments. It is worth meeting the problem on this familiar ground first, because the tool that solves it here is the tool we will carry across studies, and its classical name is the analysis of variance.\n", + "\n", + "Consider one market's experiment broken out across six user segments. The redesign helps some segments more than others, and the per-segment treatment effects are themselves draws from a population. We give the section its own random generator so the across-study results later in the notebook are unaffected." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "409b8357", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:17.050896Z", + "iopub.status.busy": "2026-05-28T17:00:17.050792Z", + "iopub.status.idle": "2026-05-28T17:00:17.060188Z", + "shell.execute_reply": "2026-05-28T17:00:17.059653Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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segmenttreatmentrevenue
0New / mobile115.883319
1New / mobile014.339618
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" + ], + "text/plain": [ + " segment treatment revenue\n", + "0 New / mobile 1 15.883319\n", + "1 New / mobile 0 14.339618\n", + "2 New / mobile 0 7.025209\n", + "3 New / mobile 0 13.596865\n", + "4 New / mobile 1 11.434889" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seg_rng = np.random.default_rng(2024)\n", + "\n", + "SEG_NAMES = [\n", + " \"New / mobile\",\n", + " \"New / desktop\",\n", + " \"Returning / mobile\",\n", + " \"Returning / desktop\",\n", + " \"High-value\",\n", + " \"Reactivated\",\n", + "]\n", + "N_PER_SEGMENT = np.array([600, 600, 900, 900, 1400, 1400]) # visitors per segment\n", + "SEG_MU, SEG_TAU = 0.30, 0.50\n", + "BASELINE, SIGMA_OBS = 10.0, 4.0\n", + "\n", + "\n", + "def simulate_within_study_segments(seg_names, N_per_seg, seg_mu, seg_tau, baseline, sigma_obs, rng):\n", + " true_effects = rng.normal(seg_mu, seg_tau, size=len(seg_names))\n", + " rows = []\n", + " for name, N_seg, eff in zip(seg_names, N_per_seg, true_effects):\n", + " treat = rng.integers(0, 2, size=int(N_seg))\n", + " revenue = baseline + eff * treat + rng.normal(0, sigma_obs, size=int(N_seg))\n", + " rows.append(pd.DataFrame({\"segment\": name, \"treatment\": treat, \"revenue\": revenue}))\n", + " return pd.concat(rows, ignore_index=True), true_effects\n", + "\n", + "\n", + "study_df, seg_true_effects = simulate_within_study_segments(\n", + " SEG_NAMES, N_PER_SEGMENT, SEG_MU, SEG_TAU, BASELINE, SIGMA_OBS, seg_rng\n", + ")\n", + "study_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "8cd8bfc3", + "metadata": {}, + "source": [ + "The per-segment treatment effect is a difference of arm means; its standard error follows from the within-arm variances." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c85af685", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:17.061723Z", + "iopub.status.busy": "2026-05-28T17:00:17.061611Z", + "iopub.status.idle": "2026-05-28T17:00:17.070132Z", + "shell.execute_reply": "2026-05-28T17:00:17.069622Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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segmentdse
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3Returning / desktop-0.3390.268
4High-value-0.9120.215
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" + ], + "text/plain": [ + " segment d se\n", + "0 New / mobile 0.614 0.338\n", + "1 New / desktop 0.869 0.334\n", + "2 Returning / mobile 1.243 0.251\n", + "3 Returning / desktop -0.339 0.268\n", + "4 High-value -0.912 0.215\n", + "5 Reactivated 0.229 0.212" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def segment_effect_estimates(df):\n", + " recs = []\n", + " for name, g in df.groupby(\"segment\", sort=False):\n", + " t = g.loc[g.treatment == 1, \"revenue\"]\n", + " c = g.loc[g.treatment == 0, \"revenue\"]\n", + " recs.append(\n", + " {\n", + " \"segment\": name,\n", + " \"d\": t.mean() - c.mean(),\n", + " \"se\": np.sqrt(t.var(ddof=1) / len(t) + c.var(ddof=1) / len(c)),\n", + " }\n", + " )\n", + " return pd.DataFrame(recs)\n", + "\n", + "\n", + "seg_est = segment_effect_estimates(study_df)\n", + "seg_est.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "e1511edd", + "metadata": {}, + "source": [ + "The classical question \"does the effect differ across segments?\" is a test for the treatment-by-segment interaction, and the two-way analysis of variance answers it with an $F$-test. We compute it directly, as a comparison of nested least-squares fits, which keeps the dependency surface small and makes the variance decomposition explicit. The interaction row is the one to read." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "96f71013", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:17.071493Z", + "iopub.status.busy": "2026-05-28T17:00:17.071375Z", + "iopub.status.idle": "2026-05-28T17:00:17.084306Z", + "shell.execute_reply": "2026-05-28T17:00:17.083679Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sum_sqdfFPR(>F)
C(segment)402.2605.05.0570.00
C(treatment)23.8881.01.5020.22
C(segment):C(treatment)828.4515.010.4150.00
Residual92081.3615788.0NaNNaN
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" + ], + "text/plain": [ + " sum_sq df F PR(>F)\n", + "C(segment) 402.260 5.0 5.057 0.00\n", + "C(treatment) 23.888 1.0 1.502 0.22\n", + "C(segment):C(treatment) 828.451 5.0 10.415 0.00\n", + "Residual 92081.361 5788.0 NaN NaN" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def anova_two_way(df, outcome, factor, treatment):\n", + " \"\"\"Type-II two-way ANOVA via nested least-squares fits (treatment is binary).\"\"\"\n", + " y = df[outcome].to_numpy(dtype=float)\n", + " n = len(y)\n", + " ones = np.ones((n, 1))\n", + " A = pd.get_dummies(df[factor], drop_first=True).to_numpy(dtype=float) # factor dummies\n", + " B = pd.get_dummies(df[treatment], drop_first=True).to_numpy(dtype=float) # treatment dummy\n", + " AB = A * B # interaction columns\n", + "\n", + " def fit(*blocks):\n", + " X = np.hstack([ones, *blocks])\n", + " beta, *_ = np.linalg.lstsq(X, y, rcond=None)\n", + " resid = y - X @ beta\n", + " return float(resid @ resid), np.linalg.matrix_rank(X)\n", + "\n", + " rss_full, k_full = fit(A, B, AB)\n", + " rss_add, k_add = fit(A, B)\n", + " rss_A, k_A = fit(A)\n", + " rss_B, k_B = fit(B)\n", + " df_resid = n - k_full\n", + " mse = rss_full / df_resid\n", + "\n", + " terms = {\n", + " f\"C({factor})\": (rss_B - rss_add, k_add - k_B),\n", + " f\"C({treatment})\": (rss_A - rss_add, k_add - k_A),\n", + " f\"C({factor}):C({treatment})\": (rss_add - rss_full, k_full - k_add),\n", + " }\n", + " rows = [\n", + " {\n", + " \"sum_sq\": ss,\n", + " \"df\": float(dof),\n", + " \"F\": (ss / dof) / mse,\n", + " \"PR(>F)\": stats.f.sf((ss / dof) / mse, dof, df_resid),\n", + " }\n", + " for ss, dof in terms.values()\n", + " ]\n", + " rows.append({\"sum_sq\": rss_full, \"df\": float(df_resid), \"F\": np.nan, \"PR(>F)\": np.nan})\n", + " return pd.DataFrame(rows, index=list(terms) + [\"Residual\"])\n", + "\n", + "\n", + "anova_tbl = anova_two_way(study_df, \"revenue\", \"segment\", \"treatment\")\n", + "anova_tbl.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "398b3030", + "metadata": {}, + "source": [ + "The interaction $F$-test reports whether heterogeneity is detectable; it does not estimate how large it is. The meta-analytic siblings of the same variance decomposition do, and because they recur through the rest of the notebook it is worth stating plainly what each one measures.\n", + "\n", + ":::{admonition} Three numbers for heterogeneity\n", + ":class: note\n", + "\n", + "Given per-group effect estimates $\\hat d_k$ with standard errors $s_k$ and inverse-variance weights $w_k = 1/s_k^2$:\n", + "\n", + "- **Cochran's $Q$** measures how far the estimates spread beyond what sampling noise alone would produce. It is $Q = \\sum_k w_k (\\hat d_k - \\bar d)^2$, where $\\bar d$ is the precision-weighted mean, and it is simply the inverse-variance-weighted version of the between-groups sum of squares from ANOVA. If every group shared one true effect, $Q$ would follow a $\\chi^2$ distribution with $K-1$ degrees of freedom, so a $Q$ much larger than $K-1$ is evidence of real heterogeneity.\n", + "- **$I^2 = \\max\\!\\big(0,\\, (Q - (K-1))/Q\\big)$** rescales $Q$ onto the unit interval: the share of the total variation in the estimates due to genuine between-group differences rather than sampling error. $I^2 = 0$ means the spread is all noise; $I^2 = 0.9$ means most of it is real.\n", + "- **The DerSimonian–Laird estimator** is the classical, non-Bayesian way to turn $Q$ into a point estimate of the between-group variance $\\tau^2$. It is a method-of-moments calculation, and once $\\hat\\tau^2$ is in hand the random-effects pooled mean re-weights each group by $1/(s_k^2 + \\hat\\tau^2)$ instead of $1/s_k^2$, so noisy groups count for less and no single precise group dominates.\n", + "\n", + "See {cite:p}`borenstein2009meta` for the full treatment and {cite:p}`higgins2009meta` for the random-effects model these statistics serve.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b06c639a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:17.085639Z", + "iopub.status.busy": "2026-05-28T17:00:17.085514Z", + "iopub.status.idle": "2026-05-28T17:00:17.089334Z", + "shell.execute_reply": "2026-05-28T17:00:17.088778Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cochran's Q = 53.38 (df = 5, p = 0.000)\n", + "I² = 0.91 DerSimonian–Laird between-segment SD τ = 0.807\n" + ] + } + ], + "source": [ + "d = seg_est[\"d\"].values\n", + "s = seg_est[\"se\"].values\n", + "w = 1.0 / s**2\n", + "d_fixed = np.sum(w * d) / np.sum(w)\n", + "se_fixed = np.sqrt(1.0 / np.sum(w))\n", + "df_q = len(d) - 1\n", + "Q = np.sum(w * (d - d_fixed) ** 2)\n", + "p_Q = stats.chi2.sf(Q, df_q)\n", + "I2 = max(0.0, (Q - df_q) / Q)\n", + "C_dl = np.sum(w) - np.sum(w**2) / np.sum(w)\n", + "tau2_DL = max(0.0, (Q - df_q) / C_dl)\n", + "w_re = 1.0 / (s**2 + tau2_DL)\n", + "mu_DL = np.sum(w_re * d) / np.sum(w_re)\n", + "se_DL = np.sqrt(1.0 / np.sum(w_re))\n", + "\n", + "print(f\"Cochran's Q = {Q:.2f} (df = {df_q}, p = {p_Q:.3f})\")\n", + "print(f\"I² = {I2:.2f} DerSimonian–Laird between-segment SD τ = {np.sqrt(tau2_DL):.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "04a75e3b", + "metadata": {}, + "source": [ + "The hierarchical Bayesian model is the same random-effects analysis of variance, with one difference that matters when the number of groups is small: it returns a posterior over $\\tau$ rather than a single number. With only six segments $\\tau$ is weakly identified, and the DerSimonian–Laird point estimate can collapse toward zero even when real heterogeneity is present; the posterior shows that uncertainty honestly instead of hiding it in a point." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "378cc883", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:17.090690Z", + "iopub.status.busy": "2026-05-28T17:00:17.090583Z", + "iopub.status.idle": "2026-05-28T17:00:20.461589Z", + "shell.execute_reply": "2026-05-28T17:00:20.461059Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [mu, tau, offset]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 2_000 tune and 2_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + } + ], + "source": [ + "coords_seg = {\"segment\": SEG_NAMES}\n", + "with pm.Model(coords=coords_seg) as segment_model:\n", + " mu = pm.Normal(\"mu\", mu=0.0, sigma=1.0)\n", + " tau = pm.HalfNormal(\"tau\", sigma=1.0)\n", + " offset = pm.Normal(\"offset\", mu=0.0, sigma=1.0, dims=\"segment\")\n", + " theta = pm.Deterministic(\"theta\", mu + tau * offset, dims=\"segment\")\n", + " pm.Normal(\"d_obs\", mu=theta, sigma=s, observed=d, dims=\"segment\")\n", + " idata_seg = pm.sample(\n", + " draws=2000,\n", + " tune=2000,\n", + " chains=2,\n", + " target_accept=0.95,\n", + " random_seed=RANDOM_SEED,\n", + " progressbar=False,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "500ed2c0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:20.463171Z", + "iopub.status.busy": "2026-05-28T17:00:20.463068Z", + "iopub.status.idle": "2026-05-28T17:00:20.468775Z", + "shell.execute_reply": "2026-05-28T17:00:20.468325Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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pooled effectse / sdbetween-segment τ
Fixed-effect ANOVA (complete pooling)0.1480.1050.000
Random-effects ANOVA (DerSimonian–Laird)0.2700.3480.807
Hierarchical Bayes (partial pooling)0.2290.3700.876
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" + ], + "text/plain": [ + " pooled effect se / sd \\\n", + "Fixed-effect ANOVA (complete pooling) 0.148 0.105 \n", + "Random-effects ANOVA (DerSimonian–Laird) 0.270 0.348 \n", + "Hierarchical Bayes (partial pooling) 0.229 0.370 \n", + "\n", + " between-segment τ \n", + "Fixed-effect ANOVA (complete pooling) 0.000 \n", + "Random-effects ANOVA (DerSimonian–Laird) 0.807 \n", + "Hierarchical Bayes (partial pooling) 0.876 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mu_post = idata_seg.posterior[\"mu\"]\n", + "tau_post = idata_seg.posterior[\"tau\"]\n", + "comparison = pd.DataFrame(\n", + " {\n", + " \"pooled effect\": [d_fixed, mu_DL, float(mu_post.mean())],\n", + " \"se / sd\": [se_fixed, se_DL, float(mu_post.std())],\n", + " \"between-segment τ\": [0.0, np.sqrt(tau2_DL), float(tau_post.mean())],\n", + " },\n", + " index=[\n", + " \"Fixed-effect ANOVA (complete pooling)\",\n", + " \"Random-effects ANOVA (DerSimonian–Laird)\",\n", + " \"Hierarchical Bayes (partial pooling)\",\n", + " ],\n", + ")\n", + "comparison.round(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1988dd9b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:20.470049Z", + "iopub.status.busy": "2026-05-28T17:00:20.469926Z", + "iopub.status.idle": "2026-05-28T17:00:21.211662Z", + "shell.execute_reply": "2026-05-28T17:00:21.211129Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 391, + "width": 661 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6.5, 3.8))\n", + "az.plot_posterior(idata_seg, var_names=[\"tau\"], ax=ax)\n", + "ax.axvline(\n", + " np.sqrt(tau2_DL),\n", + " color=\"C1\",\n", + " linestyle=\"--\",\n", + " label=f\"DerSimonian–Laird τ = {np.sqrt(tau2_DL):.2f}\",\n", + ")\n", + "ax.set_title(r\"Posterior of between-segment SD $\\tau$ (one study, six segments)\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "2e5f189d", + "metadata": {}, + "source": [ + "Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor throughout was the segment, and that is the only thing about to change. Replace \"segment\" with \"study\" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution.\n", + "\n", + "## The hierarchical re-framing\n", + "\n", + "The model is the one we just fit, with markets in place of segments. Let $\\theta_k$ be the true treatment effect in market $k$, and let $\\hat d_k$ be the observed estimate from market $k$'s experiment with standard error $s_k$. The single-experiment view treats each $\\hat d_k$ as the answer to its own question; vote-counting and pool-everything are degenerate cases of that view. The hierarchical view writes:\n", + "\n", + "$$\n", + "\\theta_k \\sim \\mathcal{N}(\\mu, \\tau), \\qquad \\hat d_k \\mid \\theta_k \\sim \\mathcal{N}(\\theta_k, s_k),\n", + "$$\n", + "\n", + "where $\\mu$ is the population mean effect across markets and $\\tau$ is the between-market standard deviation. The substantive story lives in both: $\\mu$ tells the team what to expect *on average* from the redesign; $\\tau$ tells them how variable that expectation is when the next market is queried. Neither quantity is recoverable from any single experiment. Both are recoverable from the joint." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b2a086f6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:21.212963Z", + "iopub.status.busy": "2026-05-28T17:00:21.212872Z", + "iopub.status.idle": "2026-05-28T17:00:21.220209Z", + "shell.execute_reply": "2026-05-28T17:00:21.219697Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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marketN_per_armtrue_thetad_hatsez_score
0Market A2000.3090.4750.3851.232
1Market B2500.6400.9000.3712.427
2Market C3000.6060.7280.3302.208
3Market D3500.1720.8880.3002.961
4Market E4000.2260.5960.2932.031
5Market F5000.1680.4940.2442.029
6Market G7000.4420.6630.2153.081
7Market H12000.286-0.0140.167-0.081
\n", + "
" + ], + "text/plain": [ + " market N_per_arm true_theta d_hat se z_score\n", + "0 Market A 200 0.309 0.475 0.385 1.232\n", + "1 Market B 250 0.640 0.900 0.371 2.427\n", + "2 Market C 300 0.606 0.728 0.330 2.208\n", + "3 Market D 350 0.172 0.888 0.300 2.961\n", + "4 Market E 400 0.226 0.596 0.293 2.031\n", + "5 Market F 500 0.168 0.494 0.244 2.029\n", + "6 Market G 700 0.442 0.663 0.215 3.081\n", + "7 Market H 1200 0.286 -0.014 0.167 -0.081" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "K = 8\n", + "TRUE_MU = 0.30\n", + "TRUE_TAU = 0.25\n", + "SIGMA_OBS = 4.0\n", + "MARKET_NAMES = [f\"Market {chr(65 + i)}\" for i in range(K)]\n", + "# Per-market sample sizes vary; smaller markets have noisier estimates, which\n", + "# is the regime where partial pooling does substantively visible work.\n", + "N_PER_MARKET = np.array([200, 250, 300, 350, 400, 500, 700, 1200])\n", + "\n", + "\n", + "def simulate_meta_dataset_gaussian(K, N_per_market, true_mu, true_tau, sigma_obs, rng):\n", + " theta = rng.normal(true_mu, true_tau, size=K)\n", + " d_hat = np.zeros(K)\n", + " s = np.zeros(K)\n", + " for k in range(K):\n", + " N_k = int(N_per_market[k])\n", + " y_A = rng.normal(10.0, sigma_obs, size=N_k)\n", + " y_B = rng.normal(10.0 + theta[k], sigma_obs, size=N_k)\n", + " d_hat[k] = y_B.mean() - y_A.mean()\n", + " s[k] = np.sqrt(y_A.var(ddof=1) / N_k + y_B.var(ddof=1) / N_k)\n", + " return theta, d_hat, s\n", + "\n", + "\n", + "true_theta, d_hat_obs, se_obs = simulate_meta_dataset_gaussian(\n", + " K, N_PER_MARKET, TRUE_MU, TRUE_TAU, SIGMA_OBS, rng\n", + ")\n", + "markets_df = pd.DataFrame(\n", + " {\n", + " \"market\": MARKET_NAMES,\n", + " \"N_per_arm\": N_PER_MARKET,\n", + " \"true_theta\": true_theta,\n", + " \"d_hat\": d_hat_obs,\n", + " \"se\": se_obs,\n", + " \"z_score\": d_hat_obs / se_obs,\n", + " }\n", + ")\n", + "markets_df.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "958dfc38", + "metadata": {}, + "source": [ + "The per-market `z_score` column is what a frequentist replication exercise would consult: anything above 1.96 in absolute value counts as \"significant\", anything below does not. The columns disagree about how many markets \"worked\"; the underlying true effects disagree less. This is the gap the hierarchical model is built to close.\n", + "\n", + "## No pooling, complete pooling, partial pooling\n", + "\n", + "Three estimators reflect three commitments about how much the markets share. *No pooling* fits each market in isolation; the per-market estimate is $\\hat d_k$. *Complete pooling* fits a single mean across all markets, treating them as draws from one distribution with no between-market variance. *Partial pooling* fits the hierarchical model above and lets the data weigh how exchangeable the markets are. The PyMC code below makes all three explicit so the shrinkage that distinguishes them becomes visible." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ea8e78de", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:21.221575Z", + "iopub.status.busy": "2026-05-28T17:00:21.221477Z", + "iopub.status.idle": "2026-05-28T17:00:23.739435Z", + "shell.execute_reply": "2026-05-28T17:00:23.738976Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [mu]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 0 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [mu, tau, theta_offset]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 2_000 tune and 2_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + } + ], + "source": [ + "coords = {\"market\": MARKET_NAMES}\n", + "\n", + "with pm.Model(coords=coords) as complete_model:\n", + " mu_complete = pm.Normal(\"mu\", mu=0.0, sigma=1.0)\n", + " pm.Normal(\"d_hat\", mu=mu_complete, sigma=se_obs, observed=d_hat_obs, dims=\"market\")\n", + "\n", + "with complete_model:\n", + " idata_complete = pm.sample(\n", + " draws=1000,\n", + " tune=1000,\n", + " chains=2,\n", + " target_accept=0.95,\n", + " random_seed=RANDOM_SEED,\n", + " progressbar=False,\n", + " )\n", + "\n", + "with pm.Model(coords=coords) as partial_model:\n", + " mu = pm.Normal(\"mu\", mu=0.0, sigma=1.0)\n", + " tau = pm.HalfNormal(\"tau\", sigma=1.0)\n", + " theta_offset = pm.Normal(\"theta_offset\", mu=0.0, sigma=1.0, dims=\"market\")\n", + " theta = pm.Deterministic(\"theta\", mu + tau * theta_offset, dims=\"market\")\n", + " pm.Normal(\"d_hat\", mu=theta, sigma=se_obs, observed=d_hat_obs, dims=\"market\")\n", + "\n", + "with partial_model:\n", + " idata_partial = pm.sample(\n", + " draws=2000,\n", + " tune=2000,\n", + " chains=2,\n", + " target_accept=0.95,\n", + " random_seed=RANDOM_SEED,\n", + " progressbar=False,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "35d9a55e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:23.741306Z", + "iopub.status.busy": "2026-05-28T17:00:23.741162Z", + "iopub.status.idle": "2026-05-28T17:00:23.857692Z", + "shell.execute_reply": "2026-05-28T17:00:23.857186Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 538, + "width": 740 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "no_pool_mean = d_hat_obs\n", + "no_pool_se = se_obs\n", + "complete_pool_mean = idata_complete.posterior[\"mu\"].mean().item()\n", + "complete_pool_se = idata_complete.posterior[\"mu\"].std().item()\n", + "partial_pool_summary = az.summary(idata_partial, var_names=[\"theta\"], kind=\"stats\")\n", + "partial_pool_mean = partial_pool_summary[\"mean\"].values\n", + "partial_pool_sd = partial_pool_summary[\"sd\"].values\n", + "\n", + "forest = pd.DataFrame(\n", + " {\n", + " \"market\": MARKET_NAMES,\n", + " \"no_pool_mean\": no_pool_mean,\n", + " \"no_pool_lo\": no_pool_mean - 1.96 * no_pool_se,\n", + " \"no_pool_hi\": no_pool_mean + 1.96 * no_pool_se,\n", + " \"partial_mean\": partial_pool_mean,\n", + " \"partial_lo\": partial_pool_mean - 1.96 * partial_pool_sd,\n", + " \"partial_hi\": partial_pool_mean + 1.96 * partial_pool_sd,\n", + " }\n", + ").round(3)\n", + "\n", + "fig, ax = plt.subplots(figsize=(7.5, 5.5))\n", + "y_pos = np.arange(K)\n", + "ax.errorbar(\n", + " forest[\"no_pool_mean\"],\n", + " y_pos - 0.18,\n", + " xerr=[\n", + " forest[\"no_pool_mean\"] - forest[\"no_pool_lo\"],\n", + " forest[\"no_pool_hi\"] - forest[\"no_pool_mean\"],\n", + " ],\n", + " fmt=\"o\",\n", + " color=\"C0\",\n", + " label=\"No pooling\",\n", + " capsize=3,\n", + ")\n", + "ax.errorbar(\n", + " forest[\"partial_mean\"],\n", + " y_pos + 0.18,\n", + " xerr=[\n", + " forest[\"partial_mean\"] - forest[\"partial_lo\"],\n", + " forest[\"partial_hi\"] - forest[\"partial_mean\"],\n", + " ],\n", + " fmt=\"s\",\n", + " color=\"C3\",\n", + " label=\"Partial pooling\",\n", + " capsize=3,\n", + ")\n", + "ax.axvline(\n", + " complete_pool_mean,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + " alpha=0.7,\n", + " label=f\"Complete pooling (mean = {complete_pool_mean:.3f})\",\n", + ")\n", + "ax.axvline(0.0, color=\"grey\", linestyle=\":\", alpha=0.5)\n", + "ax.set_yticks(y_pos)\n", + "ax.set_yticklabels(MARKET_NAMES)\n", + "ax.set_xlabel(\"Estimated treatment effect\")\n", + "ax.set_title(\"Forest plot: three pooling strategies on eight markets\")\n", + "ax.legend(loc=\"upper right\", bbox_to_anchor=(1.0, 1.0), framealpha=0.95)\n", + "plt.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "33737f97", + "metadata": {}, + "source": [ + "The partial-pooling estimates are pulled toward the population mean: the canonical *shrinkage* picture {cite:p}`gelman2006multilevel`, {cite:p}`gelman2020regression`. The pull is strongest for the markets whose individual estimates are noisiest (widest no-pooling intervals) or most extreme; it is weakest for markets whose estimates are tight and central. This is the data-driven version of \"borrowing strength\" that vote-counting cannot do and complete-pooling does only by force." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "261a8315", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:23.859356Z", + "iopub.status.busy": "2026-05-28T17:00:23.859242Z", + "iopub.status.idle": "2026-05-28T17:00:24.025864Z", + "shell.execute_reply": "2026-05-28T17:00:24.025453Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 511, + "width": 827 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "for k in range(K):\n", + " ax.plot([0, 1], [no_pool_mean[k], partial_pool_mean[k]], color=\"grey\", alpha=0.5, zorder=1)\n", + " ax.scatter(0, no_pool_mean[k], color=\"C0\", zorder=3, s=55)\n", + " ax.scatter(1, partial_pool_mean[k], color=\"C3\", zorder=3, s=55)\n", + " ax.text(1.04, partial_pool_mean[k], MARKET_NAMES[k], ha=\"left\", va=\"center\", fontsize=9)\n", + "ax.axhline(\n", + " complete_pool_mean,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + " alpha=0.7,\n", + " label=f\"Complete-pooling mean = {complete_pool_mean:.3f}\",\n", + ")\n", + "ax.set_xticks([0, 1])\n", + "ax.set_xticklabels([\"No pooling\", \"Partial pooling\"])\n", + "ax.set_xlim(-0.15, 1.25)\n", + "ax.set_ylabel(\"Estimated treatment effect\")\n", + "ax.set_title(\"Shrinkage: per-market estimates pulled toward the population mean\")\n", + "ax.legend(loc=\"lower right\");" + ] + }, + { + "cell_type": "markdown", + "id": "30196761", + "metadata": {}, + "source": [ + "### The substance lives in $\\tau$\n", + "\n", + "The hierarchical model returns two population-level quantities, and the conventional reporting habit of leading with $\\mu$ obscures the more important one. The posterior of $\\tau$ — the between-market standard deviation of true effects — is what tells the team how transportable any single result is to a new context. A small $\\tau$ means the markets are nearly exchangeable, and the experiment generalises cleanly; a large $\\tau$ means the markets are heterogeneous, and the next market is a meaningfully new experiment. Reporting only $\\mu$ collapses this into a point and hides the variability that the next stakeholder will live with." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "74a6c277", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:24.027416Z", + "iopub.status.busy": "2026-05-28T17:00:24.027318Z", + "iopub.status.idle": "2026-05-28T17:00:24.150868Z", + "shell.execute_reply": "2026-05-28T17:00:24.150385Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 368, + "width": 1081 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(11, 3.8))\n", + "az.plot_posterior(idata_partial, var_names=[\"mu\"], ref_val=TRUE_MU, ax=axes[0])\n", + "axes[0].set_title(r\"Population mean $\\mu$\")\n", + "az.plot_posterior(idata_partial, var_names=[\"tau\"], ref_val=TRUE_TAU, ax=axes[1])\n", + "axes[1].set_title(r\"Between-market SD $\\tau$\")\n", + "plt.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "ef1c249d", + "metadata": {}, + "source": [ + "The posterior of $\\tau$ is concentrated well away from zero, which is itself the result of substantive interest: the eight markets disagree about the size of the treatment effect in a way the data demand be respected. The next section turns this into a prediction.\n", + "\n", + "## The same machinery on a binary outcome\n", + "\n", + "The conversion-rate version repeats the structure on the log-odds scale. Each market $k$ has its own baseline rate and its own treatment log-odds effect $\\theta_k$; the population sits a level above ({cite:p}`carpenter2016hierarchical`)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a8ee0c5e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:24.152347Z", + "iopub.status.busy": "2026-05-28T17:00:24.152239Z", + "iopub.status.idle": "2026-05-28T17:00:24.155238Z", + "shell.execute_reply": "2026-05-28T17:00:24.154796Z" + } + }, + "outputs": [], + "source": [ + "TRUE_MU_LOGIT = 0.20\n", + "TRUE_TAU_LOGIT = 0.25\n", + "BASELINE_RATES = rng.beta(20, 180, size=K)\n", + "\n", + "\n", + "def simulate_meta_dataset_bernoulli(\n", + " K, N_per_market, baseline_rates, true_mu_logit, true_tau_logit, rng\n", + "):\n", + " from scipy.special import expit, logit\n", + "\n", + " theta = rng.normal(true_mu_logit, true_tau_logit, size=K)\n", + " n_A = np.zeros(K, dtype=int)\n", + " n_B = np.zeros(K, dtype=int)\n", + " for k in range(K):\n", + " p_A_k = baseline_rates[k]\n", + " p_B_k = expit(logit(p_A_k) + theta[k])\n", + " n_A[k] = rng.binomial(int(N_per_market[k]), p_A_k)\n", + " n_B[k] = rng.binomial(int(N_per_market[k]), p_B_k)\n", + " return theta, n_A, n_B\n", + "\n", + "\n", + "true_theta_bern, n_A_obs, n_B_obs = simulate_meta_dataset_bernoulli(\n", + " K, N_PER_MARKET, BASELINE_RATES, TRUE_MU_LOGIT, TRUE_TAU_LOGIT, rng\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e4d6eb38", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:24.156412Z", + "iopub.status.busy": "2026-05-28T17:00:24.156332Z", + "iopub.status.idle": "2026-05-28T17:00:29.024766Z", + "shell.execute_reply": "2026-05-28T17:00:29.024321Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [mu, tau, theta_offset, baseline_logit]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 2_000 tune and 2_000 draw iterations (4_000 + 4_000 draws total) took 4 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + } + ], + "source": [ + "with pm.Model(coords=coords) as partial_bern_model:\n", + " mu_logit = pm.Normal(\"mu\", mu=0.0, sigma=0.5)\n", + " tau_logit = pm.HalfNormal(\"tau\", sigma=0.5)\n", + " theta_offset = pm.Normal(\"theta_offset\", mu=0.0, sigma=1.0, dims=\"market\")\n", + " theta = pm.Deterministic(\"theta\", mu_logit + tau_logit * theta_offset, dims=\"market\")\n", + " baseline_logit = pm.Normal(\"baseline_logit\", mu=-2.0, sigma=1.0, dims=\"market\")\n", + " p_A = pm.Deterministic(\"p_A\", pm.math.invlogit(baseline_logit), dims=\"market\")\n", + " p_B = pm.Deterministic(\"p_B\", pm.math.invlogit(baseline_logit + theta), dims=\"market\")\n", + " pm.Binomial(\"obs_A\", n=N_PER_MARKET, p=p_A, observed=n_A_obs, dims=\"market\")\n", + " pm.Binomial(\"obs_B\", n=N_PER_MARKET, p=p_B, observed=n_B_obs, dims=\"market\")\n", + "\n", + "with partial_bern_model:\n", + " idata_partial_bern = pm.sample(\n", + " draws=2000,\n", + " tune=2000,\n", + " chains=2,\n", + " target_accept=0.95,\n", + " random_seed=RANDOM_SEED,\n", + " progressbar=False,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a5796f5c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:29.026195Z", + "iopub.status.busy": "2026-05-28T17:00:29.026087Z", + "iopub.status.idle": "2026-05-28T17:00:29.151208Z", + "shell.execute_reply": "2026-05-28T17:00:29.150714Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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++1JeGnPQ2L799yOSE8WIpSZnHDURY6n6W6GV/z30NekkaKAsSAAB4pS3AYCvNg3a1uHcc88tMaizl9vzVpJNB3E6aH3hhRfKtI76I6cDHvfgWEsp6mwDbz/G+oP43//+VxKFvwGlzpjQwY+3baH0gEQzbmM5WSHa+5jOFNBZDFpyzT3rS7evN3oAq4GKZN+On376qXXw75lNZudpBWGfnaGBmX/961/WTJ1k2D46gNAgirfPsL/to8k0ZRGp59Ntd/HFF/ucZaOfHf0tiUZmuQYnr7rqKitQ6S0QqN+V+p2p28PbvnH22WfHTc/3SL5Wbe+iv8Va+cNXgLVy5cplmi0bCzTgWZ4EEC05bf8e1JNGzKwAACA+aLXRCRMmOJYFOvs6Xo499SRjackdbvaTcB5a9cCdOOstmWbbtm1F17UCpLfqs9HYhtF8v+Ip7qAnpS666CJHVVcdR+p40lvsSbeJblfPSTNN/tAEcHtSh2c8qlUC3TQhSRMz4iFGpu+fjovs+7jnfdTqOqGi1VPOOeccxwk7HWO/9957pVbpiJfvpbLyVJq009ntOqM+Hmklavf37J9//hnS59B9wj5hIVYTQLTVlD3hTKshhavNhxux1OSMoyZyLNX92Zw+fXpIHhfJhxYwQJzSAyv3IMDd+1bpD9N1111X4odQy16df/75RWWldAClJ0E0O10HJfaBhg5amzRpElDZTzsta6gnnpT2QtWShFr2Sgd1nizI7777zhrY2E++vPnmm9aAIB5adQRKe79pGw3d3jqzWFv12AdsOkjXmTZa0u+XX35x/MC//vrrcvXVV3t9XD348GwnHTDpNrX/TQ+4fbFnk5ZHNPcxPcjzBDa0tKA+n5bI1ZOZSpOPRo8ebT3+3r17i+6n21Qr0+hzxopIbked2f/QQw85ZoDpd4jOGNFKH7qP6sBcM/gXL15sBa/0+fT2mjTiLwiYCNtHP49a+th+X90eOuNLL3qCWT+/+ncNeGmFIw086/bRAVKw2yeSz/foo486gnm6zTQ4pVns2utT+1V6aBBQe0VrlrsGZeyzAUNNX8vtt99eooRqp06drPXTwEq9evWsZfoe66wFnZGmiUye6jUaaNHZdppd720Aq2VT7YEZewlsDaL5qnSh35XluW80Xqs9sVK/J93vnSZi6uddW35peWPPIFVn1Ol7rjOMdCaWlhiP1d8eD/tvZlkSQMpbQQQAAESHnmjSYzD7OE8D8FpZtDThPh4L5bGSOwFEkztuvfVWv+MmHQ95o/f1V4LcnVzSo0cPn7eN5DFtJJ8r3uMOum94JozpPqgJAXoiScf7uh312Ffbn2iih8e6deusdT355JPln//8pzWG0HGhJr3o2FnjA3oCTcejuo9oFRR7MoyeSJsxY0ZQrS/CFSPzR8fcnrGwPq+2PdH/exLhtXqGtsrU8V1ZaaxF182eZKItAN544w3r/4nyvVQexx9/vBVncreY0RiHfrY0xuMrsSrW6HbR7W+nn41QfuZ1jK7xO3/0e99+ol6/m+66665SH1s/g6Fib6laWgveUCKWmpxx1ESPpepj2rm/M4FAGWZp9f8AhIUeiGm5RQ89kNcD9EB/4IYMGWINyjx0QOetH6Bmt9uzBHXQpidw9cSPL3qwqiUJ7VVG9OBR+wJ6Bhve+Dq40+fS7EpvGepKDzJ0hoK9Uoj2INXn8zfw0mx6e5/KYLahh578ss+M0MzRBg0ahOz2evCkBxSB9j3Ug66XX365xIH71KlTi4ILgfbz1JJtZSm3F+xrjPY+poPaBx98UE4//XS/z6cZz/aDNR3g3n333RIusbod9XvjlFNOccwg0IxoTcRq27atz+fSUnk33nij11koZd3XghHJ/Uy/j+wH6Dob6JVXXvEbANUkGV0fb4dVpW2fSD3fli1bpE+fPkX30SCgfmd26NDB5/O476/Bt/79+0uoaXUKbVHioeumSRf+PtdK10dnSNl/PzT5UPeX0mZlDRw40BGA1H08EOW5b6Rf6//+9z8rwGuniZhaejmQwKYGBTXIqwPbcP/2lJUG3+0BDf2e8JYQ628gr+vsoTMY9TMJAABiKy6i4xhN+tDjEz3ZrPEPPT61jw887SVi6XisvMdKGiPQJBBNQvac3NCxg47hfMUg9CS+xxFHHCEbN260/q0n6HXc54tO2tEWBh56ol1PFEV7G0Z6rBBPcQdv66onpvSzoHFDX/Tkl728v44NtOKLJoDr+EYTFny1U9LXp+NweysBHR9pLKs0kYyReds2+j2hx/v2WGJp3I/jbjtlj/noyciDBw8WLdOxtq5/IJUQ4ul7qTx0/9F901crJd1/NVGtXbt21kUnFGqCULRi8P5oQpVOorIL5Dsm1DSh64Ybbgj56wuG7vujRo0quq7xQ00+CGccVRFLjf62iUYcNdFjqVpxRJNL7G1hFi5cGHQCK0ALGCDO6Ix8TaawJ38o7c3mppmG7hJRevDl74fek2WoA337j4oGWspSkk+zifWH1Vfyh9JB5Wuvvea4jR5ofP755xLv9ER7oANbTzDn+uuvdwwgdNtrVn8sioV97I477ih1QKzP5x54lKfEXDxvR80+dpeP1P6F/pI/PAOx+++/XxJ9++gBtTs7+5FHHvE7gFCaXa6f3WBF8vk0QGcf5OjMm0AHLKpu3bphSf7Qk/fufpja87i0z7XSwJDe1/4e6/tb3t7Q4RLJ16oBNe1t7U7+0ISQQGe16Swwf8kf0aazFe3JH5r4EUzyh3L3wNWKKAAAIDr0hKSebPV20ZmcelJLK3w899xzjuQPPUGkMYVAkj/i7dhTYwT2ExJ6QtzfbFJ7+xc9kW+PLegMWF/rqon+WireQ09wdO7cOerbMFber3iKO2iCg7/kD88JcS3976EJRpr8oSff9QSar+QPpRUz3G1fNHHIW3vZWIuRXX755UElfwRKTwbqetqTP/Q90Nn0gSR/xMp+Hgm6/2hCj6+2O7reWpFSX4NWM9F4t+4z+m89eWuvshNt9s+Qh7vFUCS4q1pGY0zrbjGivz/hRiw1OeOoyRBLdSc+aZUne3IUECgSQIA4orOOdbBi7yOpdDDh7QSNO9tXT+RoVngg9GSw+6Dgyy+/dAxmSqMHCzqrwN5rzd+ARUtT2mkZvbL29ox3OvPGzv2ex4po72PNmzcPeKb2P/7xD8cBrPZFjcbALNrb0Z1YpRnFWvYvEBp8KC1RJN63z2effea4rkFmLdsbiCuvvDLomSmRfD532cfSZlJEis7etM+S09ljJ5xwQsD311lB9m2mJwM00SkWRfK16gDdUwpY6SxRDSIm0oyBZcuWlat9iw7iSQABACB+6YlEnWWsJ9l9VapIhGNP93jNnuThr42LngixnwzRE/SaBOKrzbCWLbe/Tk9rjGhuw1h4v+Ip7qDJUoEkcOuMbm8npPS+gbRu0HGrVpexJxD5qowRKzEyrTRsr5IQChqzfOyxx6wTj/b45SWXXGIlqums7UDEwn4eSXryVGO+/trOuCc3aAUCjTFrMohW1dHPViwmgNi/RyPFPaaNRltT98npww47LOzPSSw1OeOoyRBL1WTMGjVqOJaRAIKyIAEEiBF6cK4H/O6L/mDr7Hwt2zV06NASPVk1G/7hhx/22h/RXrpTnXXWWdYPSKC0JKA9eUN/8DQoECg9KA/mB1VLZbp/2FatWiXJSGdb2MtZxmqvt2jvYzr7K5AEI6Uz3ps2beo3Qz3Rt6NWEHJnSOtzBUqfI5jbx+N+5g6SuL+X/NF1CqTneLSezx2ccJ88jxZ7mVDP+xUsTy9jD3s521gSydf6ww8/lJjpoEHPRGLvB16WQJf2yNbZLB56zFKlSpWQrR8AAAgvPWGq8RKt/PH9998n7LGne0arrwQQ7Tlvr4yi92vfvr0jXuSOKfl6TG07EwvbMBber3iKO+gxf6C0tUZ57u+eHKLtnWM5RnbyyScHnJARCE2o0oQSrfJhj9H++9//tiqsBLrPxMp+HmlapUJft1aaDTQRxLPdv/76aytGbm9jFA3eYvHe2iYnegUQnXiye/dux7JIxB6IpUZ/20QjjpossVT3Z8h+fAcEKnGmAAJxTvvYam/HYOgPlg4svLV/0QGmp0esx+DBg4N6fD0RoidT7CdYtMSWv0CAnWasBzurolGjRtYJGXvJskBmH8QTLWeo78+mTZusgJVmh7tb+ij7rJHNmzdbswl0MBkrYmEf0wzfYJ/P3v7EncWb6NtReznay7Jqyc1gt6H2PUzU7bN27VrHc2lbKm1jFQz93nv66acDum2kn08DfDqA85Qu1N7pzz77rFx77bVBDQRDaefOnY5EPw3IBbtPeqpI2QWTSJaIr1VnQ61fv96x7KSTTpJEU94EEHegLBozpQAAQDE9CXjxxRf73CQ6ltFjKq00oBUAPLNRNZ5y++23Wyc8Hn/8cZ8Vz+L12FO3i1Zb2LhxY1EbPK0Q656Fak/i0ON+rRyi20Jfo+dkid7m1ltvLfEc7sQQX2PySG7DWHm/4inucNxxxwV828MPP7xE8orG5cp6/7K+zkjFyOytlMpL21ZfffXVVszSo0KFCtbYPNhYaKzs59Ggrab0xK1eNKlHv6c0cWXp0qWOapbe6Pe/VgTRk6Lu1kuRYq/a4hHp2Irui/YTw7r/+GvhFKnt4KvFT6gQS42NbRPpuGYyxVLdnyFvnzOgNCSAAHFKBwP/+te/rN5s3rhLL+pAriwltTSj3/5jH0xJx7KcSNH72BNA9IR1ItADk5EjR1olC3V7aiWGYOhBhva51BL+sSIW9rFge0q6S9jaZ34nw3Z0zzxq0aKFdaAcDC3jqGXoNEiRaNvH/X2jM7c0iBOMJk2aWLNAAin7Genn03Zh2qvSXhlC+ztrWyCdLaRlszt27Oiz1HM4aMDM3ktTqy+42xQFwt0uTEvExppIvlb3jASdLaef90RT3gSOaM+UAgAAJfukB9K+Qun4+I033rAunuOj7777zjqhqC0ZEu3YU5M5dNa7PWHDXa3BnsShxzV6/K/0hI0nAUTHRDpT2x5b0HGxnnD10PGAtpSI9jaMlfcrnuIO9rYswa6nJnQEU7XCXf0gmJNT0YiRtWzZUkJBK51oCwFNwvKoXbu2vPrqq16rqpQmVvbzaNPvHM/3jlbR0IpGOq7Vk85aydaTAOf2wgsvWMkvwZ4ADgXdB92CjemUl3tMq/t5pNu+ekvWCXciDLHU2Ng2kY5rJlMs1Z0AYp/QCQSKBBAgxulBm5640XJTmsGrB8Na8aO0Eznu0mvBDlg93DNK3I8bqoGnr/u4M1bjkfbJ++9//1vu16KD6VhKAImFfSzYUv3uZAf3ADnRt6N7Rk6dOnXK9Fx6v0ASQDQAG2gQSA+UvVUoiOT2cX9Gy/IdpjOQNHAWSPnbSD+fuu+++6wgir13pM4Y0XZjetHfHA0Y6OBFEw01WOytr22ouPth6/Vgq2F5E8z3SKRE8rW6P5+6/8dSBalQ0Aon9teuv4/B9mJ190omAQQAgPihY0GtZKFjk0ceeaRo+VdffSVDhgyxAvKJdOyp7VzsCSA6A9WeAKIVE+wtH+yzdO0tZHQMrCdTdRt56P3sJ+B1HODrBGIkt2GsvF/hijuEYrxcnnV1jw/sLVbCGV+JVowsVPE0TVKz70Maq3399deDHovE2n4eSzSRT8dmetHvOU2QWbBggXXSdfr06Y7b6t90f9Lv/GASmELB2zYua5ytrNxj2mhUtfT2e+Gtik8oEUv1/duQyHHUZIqluhMjI53YhcTAXgPECP1x0B+MUHGf6C1rFqJ78BjMACLYwWN5ny8Wvfzyy/Lcc8+F5LFiIVkh1vaxSA/u4n07umcnlPW5Ar2flsSzHxz7owMSbwGtaG6fsnyHeXsuXyL9fJ5ghPbJ1T67EyZM8DrA0Bkkevnwww+twIsGUi655BLp3LmzhFq4vuM95cBjSSRfqzsBJJxJPLHS/qUsyRtUAAEAIP6df/751vGtfWaqVgXxlgASz8ee7rLrmsShJz49Y2KtImCvNNGzZ8+if+tJCR0HeCoCaBsYewKIvXWMt+eK1jaMlfcrXHGHUIyXQ7mukYivRDNGFqrZ2e798tRTTy1z8oe3x0vkMXF59k2NR7z11ltWnOLhhx92/F2rhcybN69MrXPKw1vV6rKcjC6PWBjTequ+EO5qBcRSff82JHIcNZliqe7vcHfVLSAQJIAASSJUA7lwDwjtZQ/jnQ4+3ANbLYGnffe092irVq2kXr161gGH9mjUgwO7AQMGBBwMiAXxso8l83Z0l2DUspplUdb7xdt+VtbnKuv3WKSer1atWvLiiy9awQqdRTh16lSrb7qv93rixInWZcSIEfLggw+WeXDl6/GTRTK91mgkgAQ700l7jO/YscPxudDfZAAAEF/0GFpLc9sTQObPn2/NENWy54lyPKYtJjSRY+XKlUUJvzrz23MMZE/i0HFfp06dSrSQ+f7770u0ivF23V4xxC2S2zCe3y8kboxMk6u0Ao/HU089ZX0PXX755WV6PPbz4JP+Vq1aJZ9++mmJpLhIJ4AsXry4xLJIJ2C4K4BoO5xI0wQD/bza9+VIt9wmlhqdbROq5yrP+aBEjaW6P0OxVBUe8YMEECBBuWf7lvXAy51VGcwsYn1OT8/ZYO4TyR+3cFbV0NkcdjqY1XKFgZZeC6Z/arLuY4kgktsxVM8VyYFcJLePO/vbWz/XQAS6jpF+PrfWrVvLPffcY122bNliBcq1v66WVtUggnswNGrUKGvW4DvvvFOi3G9Zub/jdbbhu+++K4kokq+1Ro0afmeAJGICSLCBrliYKQUAAELD3SJXj2P1eNZdySLejz11fT0JIEpPQnsSQOxJHFqC3N07XpM6PAkgf/75p3UiXWMTOg7Q2fP2Wa7+Wg5HchvG+/uFxIyRvfrqq3LzzTfLpEmTipZpC5IDBw7IjTfeGPTjsZ8HT2fVuxNA9Hst0ubMmVPiBPGRRx4ZsefXWJD9dWucRhMFI01PwGs7jXXr1hUt27x5c1ifk1hqbGybaMQ1kyGWqo+hjx2KVj5IbiSAAAnKPdOlrFnyGzZsKHNCxsaNG4NOANH7+Hsd/vqVlqW/YLhOimkfOP3x99AfeM0SDfTHWkuHlfUAJpn2sUQQye2os8bs1q5dG/Tz5ObmBryOkydPlnjaPu7ncn8fBZpUppUFAhHp5/Onbt26Mnz4cOvi6Ts8duxYa4Bi3+Ya4NBMd3u/8fJw/0asX79eElUkX6s7AUT3f91X3L+bydwCZunSpVHvlQwAAELD26xKe6WvRDn21CSO9957r+i6Jn1cccUVVpnwRYsWeW3/4muZVgzRY3p3+xetFOJPJLdhvL9fkRgvx4tEipFp1ZLnn39e/vnPf1on9jxeeuklK0nl7rvvDurxEn0/D4fGjRtb3/v2E7ha9SmSNAHPPftfWz5EsqqxViKwn2xu2rRpieS/SGnYsKEjASQUcSp/iKXGxraJRlwzGWKpegzrrg5VnlZjSF6JEwUG4ODO+NUD4bIMIpYtW1YiQ7+ss2vLch9/z+fuYafZ9sHQ3nXhmkGgs3Ls1UXatWsXVBa4boeyJLQk2z6WCCK5HY855hjHdT0Y9fSBDuakayRLlEZy+7iXrVmzJui+uZpUE+h3UaSfLxiaLHTBBRdYAa1jjz3W8bfvvvsuZM/jPmmvA8xwBwqiJZKv1f1Z18CYfbZovNPvLnuAT0tEN2nSJOgS1HZUAAEAIH55m8Xpbh+RCMeexx13nON16WxTTdCfO3euY4zmrnyitLWGnhz08FQMCab9S6S3Yby/X0jcGFlaWppV9ePMM890LNcKNffdd19Q1YbZz8umQoUKfq+H29tvv11i2amnnhrRddBWOLEST9WqCHa///57WJ+PWGpsbJtoxDWTIZbq/mxrsmSyTZhFaJAAAiQoHdi7ZwBr37Fg6ODanZChpUQDZS+HGAgtO+ou2ef+sfRX8ivYjFb3yZ9Qcs82OuKII4K6/5QpU4J+TncZsXC2t4mVfSwRRHI76oGoO2N49OjRQT2XfYZLom0fPYFsP6DWANOPP/4Y1HMF870X6ecri4oVK8oNN9zgWBbKRAKdJaIXu3HjxkkiiuRr1QC/O6AaysSdaP72eKv+oeVmg6luoolv9lmyigQQAADil7fjU3f1w2gde4byWEknwdgTfbOzs60kEHsSh86M9dUaz57coffRmePBJoBEchsm01gh0UUjRhZuOv54+OGH5eKLL3Ys//zzz+WOO+6wqpYEIt6/l6JBE9/c+1SwFajL46OPPpLp06c7lun3bpcuXSSS3DH0Zs2aSbS0bdvWcX3FihVhfT5iqbGxbaIR10yGWKr78+Oe5AUEigQQIIH16dPHcV37IwYzc//DDz90lJLTXm8dOnQI+P56MBxMhunHH39cIrvRX+9CHTDaM7y1nYs7Q9Ifd7/IUHLPNgqm1YzOXirLurkropS1l1087WOJIpLbccSIESVmqASa9ayzrbRkXaJuHy3VqSU7/X0vlRaE+OKLLwK+faSfr6zcSUOhzpIfMmSI4/qbb74ZM/2dQy2Sr3XYsGGO619++aVVejnUovHb404ACbbM7WeffeYIyOrgvFGjRiFbPwAAEDl6nP/DDz+UGI/7iiVE+tgz1MdK7gQNbeFib+PStWtXn4mx9sogevJ0zJgxsnnzZsdJlcMOO6zUdYjkNkymsUIii0aMLFK05ct1111XYuLMjTfeaI3ZAxHv30uR9tNPP5WoCOMr8S0cz/3EE0+UiO3cddddEmn2729PO4poVqiyt7/RE9jhrh5MLDX62yYacc1kiKW6Y176+QLKggQQIIGdf/75juvam/D1118P6L6a5fnBBx84lmmfsmBOsujJlUceecRxwODvh809oNNSiv5m9GrG+tFHH13iBFcgxo8fL9OmTZNwcQdNdFZOoAOqhx56yGu/4tLUqlUrouX2YmEfSxSR3I5nnXWWY7aHJnXoPlfa5zQnJ8fqcRuOEnmxtn3stJzy999/H9Bz6TrZe54GIpLPF+gMJDctp2hXp04dCaVLL73UOgHvoT0zNXgSyG9HvInka73wwgut1ij2tmf6GS7rfhBLvz3uUqR///13wNtQq4W99dZbJcrVBlNBBAAAxI733nuvxEQQTXSoXLlyTBx7hvpYyd3eRSsF2GeV+qvg0a1bN8dY8Lnnnguq+kc0tmEyjRUSWTRiZJGkyR533nlniRnmV199dUCtCeL9eynYcer8+fPLfH+tfPS///2vxAnhfv36STjpyfMXX3xRrrrqqhKJPZdccknEq3+oUI/ty0PjRPaqmho7XLp0aVifk1hq7GybSMZRkyGWOmfOHMd1d8ILECginUAC0/YpvXr1cix74YUXrHKE/mjw4IorrnD8wGm7lYsuuijoddASXJoE4q9Xpw409ADafhstVeb+MQ8kS17L8JXW2mXq1KnWSbBw0sQUe8BJM/cfeOABv2UVdXvrbb799tsyP6fdyJEjw36yPhb2sUQQye2olXM0uGD3zTffyM033+yzOoAeGF9++eVFB6D2k8qJtn06d+5cYuB+7733liiN7KbJZxoMCFYkn+/pp5+W2267TRYvXhzwfXbu3CnPPvusY5nOKgx18El/A9xJetdcc431/IHSIIx+f2rf3VgNEEbytWrZ88suu8yxTBMf9bk0GSQQGiT45JNPYu63x12KVHva6kys0ujr1iCse/1atGgR8nUEAADhtXfvXnnqqafkySefLHEi0F12O5rHnqE+VmrXrp3jRLGO1ewnif0lcWiMwl5G3N0+wJ1cEgvbMJnGCoksGjGySNOxl66vvQrCjBkzrOWlJbvE+/dSMDSudN5551mtc8aOHRtwlRRP4r/Gs7SFuN0JJ5xgtUENB5009dprr8mAAQOsOJR7n9XKm7fffrtEgzvRsbQ4Urj1798/outDLDU546iJHkvVOJx+13k0b968RJswIFBpAd8SQFx67LHH5KSTTrJ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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 368, + "width": 1088 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(11, 3.8))\n", + "az.plot_posterior(idata_partial_bern, var_names=[\"mu\"], ref_val=TRUE_MU_LOGIT, ax=axes[0])\n", + "axes[0].set_title(r\"Population log-odds effect $\\mu$\")\n", + "az.plot_posterior(idata_partial_bern, var_names=[\"tau\"], ref_val=TRUE_TAU_LOGIT, ax=axes[1])\n", + "axes[1].set_title(r\"Between-market SD $\\tau$ (log-odds)\")\n", + "plt.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "f7537167", + "metadata": {}, + "source": [ + "The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. \"Meta-analysis\" is not a binary-outcome technique borrowed from medicine. It is the same Bayesian operation regardless of likelihood.\n", + "\n", + "## Predicting the next experiment\n", + "\n", + "The stakeholder question that occasioned this entire exercise is rarely \"what was the population mean?\" — it is \"what will happen if we run this in Market I next quarter?\". The hierarchical model answers in two posterior-predictive flavours, and the difference between them matters.\n", + "\n", + "**The true-effect predictive** asks for the unknown $\\theta_{\\text{new}}$ of a new market drawn from the same population:\n", + "\n", + "$$\\theta_{\\text{new}} \\mid \\text{data} \\sim \\mathcal{N}(\\mu_{\\text{post}}, \\sqrt{\\tau_{\\text{post}}^2 + \\sigma_{\\mu, \\text{post}}^2}).$$\n", + "\n", + "**The observation predictive** layers experimental noise on top:\n", + "\n", + "$$\\hat d_{\\text{new}} \\mid \\text{data} \\sim \\theta_{\\text{new}} + \\mathcal{N}(0, s_{\\text{new}}).$$\n", + "\n", + "The first is what the team should report when they say \"our best estimate of the effect in a new market\". The second is what they should report when they say \"what we expect to see if we run the experiment again\". The gap between the two is exactly the experimental-noise envelope that single-market estimates conflate with population variation." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6d7d4a17", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:29.152606Z", + "iopub.status.busy": "2026-05-28T17:00:29.152498Z", + "iopub.status.idle": "2026-05-28T17:00:29.408148Z", + "shell.execute_reply": "2026-05-28T17:00:29.407738Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 461, + "width": 761 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "mu_samples = idata_partial.posterior[\"mu\"].values.flatten()\n", + "tau_samples = idata_partial.posterior[\"tau\"].values.flatten()\n", + "rng_pp = np.random.default_rng(RANDOM_SEED + 1)\n", + "theta_new_samples = rng_pp.normal(mu_samples, tau_samples)\n", + "s_new = np.median(se_obs)\n", + "d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples))\n", + "\n", + "fig, ax = plt.subplots(figsize=(7.5, 4.5))\n", + "bins = np.linspace(\n", + " min(d_hat_new_samples.min(), theta_new_samples.min()) - 0.2,\n", + " max(d_hat_new_samples.max(), theta_new_samples.max()) + 0.2,\n", + " 80,\n", + ")\n", + "ax.hist(\n", + " theta_new_samples,\n", + " bins=bins,\n", + " alpha=0.55,\n", + " label=r\"True effect $\\theta_{\\text{new}}$\",\n", + " color=\"C0\",\n", + " density=True,\n", + ")\n", + "ax.hist(\n", + " d_hat_new_samples,\n", + " bins=bins,\n", + " alpha=0.45,\n", + " label=r\"Observed estimate $\\hat d_{\\text{new}}$\",\n", + " color=\"C3\",\n", + " density=True,\n", + ")\n", + "ax.axvline(0.0, color=\"black\", linestyle=\":\")\n", + "ax.axvline(mu_samples.mean(), color=\"C0\", linestyle=\"--\", alpha=0.8, label=r\"$\\mu_{\\text{post}}$\")\n", + "ax.set_xlabel(\"Predicted effect in a new market\")\n", + "ax.set_ylabel(\"Posterior predictive density\")\n", + "ax.set_title(\"Two predictive distributions for the ninth market\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "645d5c5e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:29.409530Z", + "iopub.status.busy": "2026-05-28T17:00:29.409426Z", + "iopub.status.idle": "2026-05-28T17:00:29.413573Z", + "shell.execute_reply": "2026-05-28T17:00:29.413075Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " posterior probability > 0\n", + "$\\theta_{\\text{new}}$ (true effect) 0.935\n", + "$\\hat d_{\\text{new}}$ (observed estimate) 0.886" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prob_theta_new_positive = float((theta_new_samples > 0).mean())\n", + "prob_d_hat_new_positive = float((d_hat_new_samples > 0).mean())\n", + "pd.DataFrame(\n", + " {\"posterior probability > 0\": [prob_theta_new_positive, prob_d_hat_new_positive]},\n", + " index=[r\"$\\theta_{\\text{new}}$ (true effect)\", r\"$\\hat d_{\\text{new}}$ (observed estimate)\"],\n", + ").round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "cb419ca8", + "metadata": {}, + "source": [ + "The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\\theta_{\\text{new}}$ as the planning input for the next market — which is what {ref}`assurance_planning` will then consume as its prior — is the way to feed accumulating evidence forward without losing track of the noise.\n", + "\n", + "## Replication as the data-generating process\n", + "\n", + "A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation rather than as eight unrelated noises, and the posterior on the population is not a number that \"summarises the experiments\". It is the population our experiments were drawn from, made audible by being listened to as a chorus.\n", + "\n", + "This is the deeper reframing that the meta-analytic posture earns. Replication is often described as a verification protocol: a check we run on a result, after the fact, to see if it survives. The Bayesian version is more substantive than that. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The posterior on $\\mu$ and on $\\tau$ are the two halves of what we have inferred about the unobserved-but-real distribution of effects across the worlds the team operates in.\n", + "\n", + "The triptych closes here, though \"closes\" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three questions, one machinery, and in each the same discipline: the quantity a conventional analysis leaves implicit, whether the believed effect, the unmeasured confounder, or the between-study variance, is made an explicit parameter with a posterior, and so becomes something a team can argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The posterior is the constant; the question changes.\n", + "\n", + "And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\\theta_{\\text{new}} \\sim \\mathcal{N}(\\mu, \\tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it.\n", + "\n", + "## Authors\n", + "\n", + "- Authored by [Nathaniel Forde](https://nathanielf.github.io/) in May 2026.\n", + "\n", + "## References\n", + "\n", + ":::{bibliography}\n", + ":filter: docname in docnames\n", + ":::\n", + "\n", + "## Watermark" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2e2e3295", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T17:00:29.414902Z", + "iopub.status.busy": "2026-05-28T17:00:29.414815Z", + "iopub.status.idle": "2026-05-28T17:00:29.430081Z", + "shell.execute_reply": "2026-05-28T17:00:29.429589Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Thu May 28 2026\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.12.12\n", + "IPython version : 9.8.0\n", + "\n", + "pytensor: 2.38.2\n", + "xarray : 2025.12.0\n", + "\n", + "pymc : 5.28.5\n", + "matplotlib: 3.10.8\n", + "numpy : 2.3.5\n", + "arviz : 0.23.0\n", + "scipy : 1.16.3\n", + "seaborn : 0.13.2\n", + "pandas : 2.3.3\n", + "\n", + "Watermark: 2.5.0\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w -p pytensor,xarray" + ] + }, + { + "cell_type": "markdown", + "id": "964065d7", + "metadata": {}, + "source": [ + ":::{include} ../page_footer.md\n", + ":::" + ] + } + ], + "metadata": { + "jupytext": { + "default_lexer": "ipython3" + }, + "kernelspec": { + "display_name": "pymc-marketing-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md new file mode 100644 index 000000000..239bdb756 --- /dev/null +++ b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md @@ -0,0 +1,601 @@ +--- +jupytext: + default_lexer: ipython3 + text_representation: + extension: .md + format_name: myst + format_version: 0.13 +kernelspec: + display_name: pymc-marketing-dev + language: python + name: python3 +--- + +(meta_analysis_experiments)= +# Multiple Experiments and Bayesian Meta-analysis + +:::{post} May 2026 +:tags: experimentation, meta-analysis, hierarchical models, partial pooling, replication +:category: intermediate, reference +:author: Nathaniel Forde +::: + +## The replication-as-evidence problem + +Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question — "did it work?" — and gets, depending on which colleague answers, two incompatible defaults: vote-counting ("four out of eight worked, so it's a wash"), or pool-everything ("the combined estimate is positive, so it works"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that lets the markets be different, and lets that difference be part of what we are estimating. + +This notebook builds that model. The hierarchical Bayesian meta-analysis treats each experiment as a noisy estimate of its own market's true effect, and treats the per-market effects as draws from a population whose mean and variance are themselves the quantities of substantive interest {cite:p}`borenstein2009meta`, {cite:p}`higgins2009meta`. The structure is the one Rubin used for the 8-schools problem in 1981 {cite:p}`rubin1981estimation`, {cite:p}`gelman2013bayesian`, transposed to product experimentation. We develop it on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the third of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`sensitivity_confounding` for the interpretation counterpart. Readers wanting a deeper view of the partial-pooling vocabulary should also consult the existing PyMC notebooks on {ref}`multilevel_modeling` and {ref}`hierarchical_partial_pooling`, which we treat as predecessors rather than re-derive. + +:::{admonition} Where this lands in regulatory practice +:class: note + +The hierarchical model here is the borrowing mechanism a regulator now describes by name. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials presents subgroup analysis through a *one-way Bayesian hierarchical model* whose subgroup estimate is "a weighted average of its raw estimated treatment effect ... and the overall estimated treatment effect" {cite:p}`fda2026bayesian`, the shrinkage picture this notebook builds. The same guidance treats hierarchical models as the main way to borrow information across related trials by assuming the group parameters are drawn from a common distribution, which is the $\theta_k \sim \mathcal{N}(\mu, \tau)$ structure below. Borrowing across studies, and the use of one trial's synthesis as the next trial's prior, is the regulatory form of the lifecycle loop these three notebooks trace. +::: + +```{code-cell} ipython3 +import warnings + +import arviz as az +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pymc as pm +import seaborn as sns + +from scipy import stats + +warnings.filterwarnings("ignore", category=RuntimeWarning) +warnings.filterwarnings("ignore", category=UserWarning) +``` + +```{code-cell} ipython3 +%config InlineBackend.figure_format = 'retina' +az.style.use("arviz-darkgrid") +rng = np.random.default_rng(11) +RANDOM_SEED = 11 +``` + +## Heterogeneity is not new: groups within a single study + +The across-study problem looks novel, but its structure appears inside a single experiment whenever the treatment effect varies across user segments. It is worth meeting the problem on this familiar ground first, because the tool that solves it here is the tool we will carry across studies, and its classical name is the analysis of variance. + +Consider one market's experiment broken out across six user segments. The redesign helps some segments more than others, and the per-segment treatment effects are themselves draws from a population. We give the section its own random generator so the across-study results later in the notebook are unaffected. + +```{code-cell} ipython3 +seg_rng = np.random.default_rng(2024) + +SEG_NAMES = [ + "New / mobile", + "New / desktop", + "Returning / mobile", + "Returning / desktop", + "High-value", + "Reactivated", +] +N_PER_SEGMENT = np.array([600, 600, 900, 900, 1400, 1400]) # visitors per segment +SEG_MU, SEG_TAU = 0.30, 0.50 +BASELINE, SIGMA_OBS = 10.0, 4.0 + + +def simulate_within_study_segments(seg_names, N_per_seg, seg_mu, seg_tau, baseline, sigma_obs, rng): + true_effects = rng.normal(seg_mu, seg_tau, size=len(seg_names)) + rows = [] + for name, N_seg, eff in zip(seg_names, N_per_seg, true_effects): + treat = rng.integers(0, 2, size=int(N_seg)) + revenue = baseline + eff * treat + rng.normal(0, sigma_obs, size=int(N_seg)) + rows.append(pd.DataFrame({"segment": name, "treatment": treat, "revenue": revenue})) + return pd.concat(rows, ignore_index=True), true_effects + + +study_df, seg_true_effects = simulate_within_study_segments( + SEG_NAMES, N_PER_SEGMENT, SEG_MU, SEG_TAU, BASELINE, SIGMA_OBS, seg_rng +) +study_df.head() +``` + +The per-segment treatment effect is a difference of arm means; its standard error follows from the within-arm variances. + +```{code-cell} ipython3 +def segment_effect_estimates(df): + recs = [] + for name, g in df.groupby("segment", sort=False): + t = g.loc[g.treatment == 1, "revenue"] + c = g.loc[g.treatment == 0, "revenue"] + recs.append( + { + "segment": name, + "d": t.mean() - c.mean(), + "se": np.sqrt(t.var(ddof=1) / len(t) + c.var(ddof=1) / len(c)), + } + ) + return pd.DataFrame(recs) + + +seg_est = segment_effect_estimates(study_df) +seg_est.round(3) +``` + +The classical question "does the effect differ across segments?" is a test for the treatment-by-segment interaction, and the two-way analysis of variance answers it with an $F$-test. We compute it directly, as a comparison of nested least-squares fits, which keeps the dependency surface small and makes the variance decomposition explicit. The interaction row is the one to read. + +```{code-cell} ipython3 +def anova_two_way(df, outcome, factor, treatment): + """Type-II two-way ANOVA via nested least-squares fits (treatment is binary).""" + y = df[outcome].to_numpy(dtype=float) + n = len(y) + ones = np.ones((n, 1)) + A = pd.get_dummies(df[factor], drop_first=True).to_numpy(dtype=float) # factor dummies + B = pd.get_dummies(df[treatment], drop_first=True).to_numpy(dtype=float) # treatment dummy + AB = A * B # interaction columns + + def fit(*blocks): + X = np.hstack([ones, *blocks]) + beta, *_ = np.linalg.lstsq(X, y, rcond=None) + resid = y - X @ beta + return float(resid @ resid), np.linalg.matrix_rank(X) + + rss_full, k_full = fit(A, B, AB) + rss_add, k_add = fit(A, B) + rss_A, k_A = fit(A) + rss_B, k_B = fit(B) + df_resid = n - k_full + mse = rss_full / df_resid + + terms = { + f"C({factor})": (rss_B - rss_add, k_add - k_B), + f"C({treatment})": (rss_A - rss_add, k_add - k_A), + f"C({factor}):C({treatment})": (rss_add - rss_full, k_full - k_add), + } + rows = [ + { + "sum_sq": ss, + "df": float(dof), + "F": (ss / dof) / mse, + "PR(>F)": stats.f.sf((ss / dof) / mse, dof, df_resid), + } + for ss, dof in terms.values() + ] + rows.append({"sum_sq": rss_full, "df": float(df_resid), "F": np.nan, "PR(>F)": np.nan}) + return pd.DataFrame(rows, index=list(terms) + ["Residual"]) + + +anova_tbl = anova_two_way(study_df, "revenue", "segment", "treatment") +anova_tbl.round(3) +``` + +The interaction $F$-test reports whether heterogeneity is detectable; it does not estimate how large it is. The meta-analytic siblings of the same variance decomposition do, and because they recur through the rest of the notebook it is worth stating plainly what each one measures. + +:::{admonition} Three numbers for heterogeneity +:class: note + +Given per-group effect estimates $\hat d_k$ with standard errors $s_k$ and inverse-variance weights $w_k = 1/s_k^2$: + +- **Cochran's $Q$** measures how far the estimates spread beyond what sampling noise alone would produce. It is $Q = \sum_k w_k (\hat d_k - \bar d)^2$, where $\bar d$ is the precision-weighted mean, and it is simply the inverse-variance-weighted version of the between-groups sum of squares from ANOVA. If every group shared one true effect, $Q$ would follow a $\chi^2$ distribution with $K-1$ degrees of freedom, so a $Q$ much larger than $K-1$ is evidence of real heterogeneity. +- **$I^2 = \max\!\big(0,\, (Q - (K-1))/Q\big)$** rescales $Q$ onto the unit interval: the share of the total variation in the estimates due to genuine between-group differences rather than sampling error. $I^2 = 0$ means the spread is all noise; $I^2 = 0.9$ means most of it is real. +- **The DerSimonian–Laird estimator** is the classical, non-Bayesian way to turn $Q$ into a point estimate of the between-group variance $\tau^2$. It is a method-of-moments calculation, and once $\hat\tau^2$ is in hand the random-effects pooled mean re-weights each group by $1/(s_k^2 + \hat\tau^2)$ instead of $1/s_k^2$, so noisy groups count for less and no single precise group dominates. + +See {cite:p}`borenstein2009meta` for the full treatment and {cite:p}`higgins2009meta` for the random-effects model these statistics serve. +::: + +```{code-cell} ipython3 +d = seg_est["d"].values +s = seg_est["se"].values +w = 1.0 / s**2 +d_fixed = np.sum(w * d) / np.sum(w) +se_fixed = np.sqrt(1.0 / np.sum(w)) +df_q = len(d) - 1 +Q = np.sum(w * (d - d_fixed) ** 2) +p_Q = stats.chi2.sf(Q, df_q) +I2 = max(0.0, (Q - df_q) / Q) +C_dl = np.sum(w) - np.sum(w**2) / np.sum(w) +tau2_DL = max(0.0, (Q - df_q) / C_dl) +w_re = 1.0 / (s**2 + tau2_DL) +mu_DL = np.sum(w_re * d) / np.sum(w_re) +se_DL = np.sqrt(1.0 / np.sum(w_re)) + +print(f"Cochran's Q = {Q:.2f} (df = {df_q}, p = {p_Q:.3f})") +print(f"I² = {I2:.2f} DerSimonian–Laird between-segment SD τ = {np.sqrt(tau2_DL):.3f}") +``` + +The hierarchical Bayesian model is the same random-effects analysis of variance, with one difference that matters when the number of groups is small: it returns a posterior over $\tau$ rather than a single number. With only six segments $\tau$ is weakly identified, and the DerSimonian–Laird point estimate can collapse toward zero even when real heterogeneity is present; the posterior shows that uncertainty honestly instead of hiding it in a point. + +```{code-cell} ipython3 +coords_seg = {"segment": SEG_NAMES} +with pm.Model(coords=coords_seg) as segment_model: + mu = pm.Normal("mu", mu=0.0, sigma=1.0) + tau = pm.HalfNormal("tau", sigma=1.0) + offset = pm.Normal("offset", mu=0.0, sigma=1.0, dims="segment") + theta = pm.Deterministic("theta", mu + tau * offset, dims="segment") + pm.Normal("d_obs", mu=theta, sigma=s, observed=d, dims="segment") + idata_seg = pm.sample( + draws=2000, + tune=2000, + chains=2, + target_accept=0.95, + random_seed=RANDOM_SEED, + progressbar=False, + ) +``` + +```{code-cell} ipython3 +mu_post = idata_seg.posterior["mu"] +tau_post = idata_seg.posterior["tau"] +comparison = pd.DataFrame( + { + "pooled effect": [d_fixed, mu_DL, float(mu_post.mean())], + "se / sd": [se_fixed, se_DL, float(mu_post.std())], + "between-segment τ": [0.0, np.sqrt(tau2_DL), float(tau_post.mean())], + }, + index=[ + "Fixed-effect ANOVA (complete pooling)", + "Random-effects ANOVA (DerSimonian–Laird)", + "Hierarchical Bayes (partial pooling)", + ], +) +comparison.round(3) +``` + +```{code-cell} ipython3 +fig, ax = plt.subplots(figsize=(6.5, 3.8)) +az.plot_posterior(idata_seg, var_names=["tau"], ax=ax) +ax.axvline( + np.sqrt(tau2_DL), + color="C1", + linestyle="--", + label=f"DerSimonian–Laird τ = {np.sqrt(tau2_DL):.2f}", +) +ax.set_title(r"Posterior of between-segment SD $\tau$ (one study, six segments)") +ax.legend(); +``` + +Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor throughout was the segment, and that is the only thing about to change. Replace "segment" with "study" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution. + +## The hierarchical re-framing + +The model is the one we just fit, with markets in place of segments. Let $\theta_k$ be the true treatment effect in market $k$, and let $\hat d_k$ be the observed estimate from market $k$'s experiment with standard error $s_k$. The single-experiment view treats each $\hat d_k$ as the answer to its own question; vote-counting and pool-everything are degenerate cases of that view. The hierarchical view writes: + +$$ +\theta_k \sim \mathcal{N}(\mu, \tau), \qquad \hat d_k \mid \theta_k \sim \mathcal{N}(\theta_k, s_k), +$$ + +where $\mu$ is the population mean effect across markets and $\tau$ is the between-market standard deviation. The substantive story lives in both: $\mu$ tells the team what to expect *on average* from the redesign; $\tau$ tells them how variable that expectation is when the next market is queried. Neither quantity is recoverable from any single experiment. Both are recoverable from the joint. + +```{code-cell} ipython3 +K = 8 +TRUE_MU = 0.30 +TRUE_TAU = 0.25 +SIGMA_OBS = 4.0 +MARKET_NAMES = [f"Market {chr(65 + i)}" for i in range(K)] +# Per-market sample sizes vary; smaller markets have noisier estimates, which +# is the regime where partial pooling does substantively visible work. +N_PER_MARKET = np.array([200, 250, 300, 350, 400, 500, 700, 1200]) + + +def simulate_meta_dataset_gaussian(K, N_per_market, true_mu, true_tau, sigma_obs, rng): + theta = rng.normal(true_mu, true_tau, size=K) + d_hat = np.zeros(K) + s = np.zeros(K) + for k in range(K): + N_k = int(N_per_market[k]) + y_A = rng.normal(10.0, sigma_obs, size=N_k) + y_B = rng.normal(10.0 + theta[k], sigma_obs, size=N_k) + d_hat[k] = y_B.mean() - y_A.mean() + s[k] = np.sqrt(y_A.var(ddof=1) / N_k + y_B.var(ddof=1) / N_k) + return theta, d_hat, s + + +true_theta, d_hat_obs, se_obs = simulate_meta_dataset_gaussian( + K, N_PER_MARKET, TRUE_MU, TRUE_TAU, SIGMA_OBS, rng +) +markets_df = pd.DataFrame( + { + "market": MARKET_NAMES, + "N_per_arm": N_PER_MARKET, + "true_theta": true_theta, + "d_hat": d_hat_obs, + "se": se_obs, + "z_score": d_hat_obs / se_obs, + } +) +markets_df.round(3) +``` + +The per-market `z_score` column is what a frequentist replication exercise would consult: anything above 1.96 in absolute value counts as "significant", anything below does not. The columns disagree about how many markets "worked"; the underlying true effects disagree less. This is the gap the hierarchical model is built to close. + +## No pooling, complete pooling, partial pooling + +Three estimators reflect three commitments about how much the markets share. *No pooling* fits each market in isolation; the per-market estimate is $\hat d_k$. *Complete pooling* fits a single mean across all markets, treating them as draws from one distribution with no between-market variance. *Partial pooling* fits the hierarchical model above and lets the data weigh how exchangeable the markets are. The PyMC code below makes all three explicit so the shrinkage that distinguishes them becomes visible. + +```{code-cell} ipython3 +coords = {"market": MARKET_NAMES} + +with pm.Model(coords=coords) as complete_model: + mu_complete = pm.Normal("mu", mu=0.0, sigma=1.0) + pm.Normal("d_hat", mu=mu_complete, sigma=se_obs, observed=d_hat_obs, dims="market") + +with complete_model: + idata_complete = pm.sample( + draws=1000, + tune=1000, + chains=2, + target_accept=0.95, + random_seed=RANDOM_SEED, + progressbar=False, + ) + +with pm.Model(coords=coords) as partial_model: + mu = pm.Normal("mu", mu=0.0, sigma=1.0) + tau = pm.HalfNormal("tau", sigma=1.0) + theta_offset = pm.Normal("theta_offset", mu=0.0, sigma=1.0, dims="market") + theta = pm.Deterministic("theta", mu + tau * theta_offset, dims="market") + pm.Normal("d_hat", mu=theta, sigma=se_obs, observed=d_hat_obs, dims="market") + +with partial_model: + idata_partial = pm.sample( + draws=2000, + tune=2000, + chains=2, + target_accept=0.95, + random_seed=RANDOM_SEED, + progressbar=False, + ) +``` + +```{code-cell} ipython3 +no_pool_mean = d_hat_obs +no_pool_se = se_obs +complete_pool_mean = idata_complete.posterior["mu"].mean().item() +complete_pool_se = idata_complete.posterior["mu"].std().item() +partial_pool_summary = az.summary(idata_partial, var_names=["theta"], kind="stats") +partial_pool_mean = partial_pool_summary["mean"].values +partial_pool_sd = partial_pool_summary["sd"].values + +forest = pd.DataFrame( + { + "market": MARKET_NAMES, + "no_pool_mean": no_pool_mean, + "no_pool_lo": no_pool_mean - 1.96 * no_pool_se, + "no_pool_hi": no_pool_mean + 1.96 * no_pool_se, + "partial_mean": partial_pool_mean, + "partial_lo": partial_pool_mean - 1.96 * partial_pool_sd, + "partial_hi": partial_pool_mean + 1.96 * partial_pool_sd, + } +).round(3) + +fig, ax = plt.subplots(figsize=(7.5, 5.5)) +y_pos = np.arange(K) +ax.errorbar( + forest["no_pool_mean"], + y_pos - 0.18, + xerr=[ + forest["no_pool_mean"] - forest["no_pool_lo"], + forest["no_pool_hi"] - forest["no_pool_mean"], + ], + fmt="o", + color="C0", + label="No pooling", + capsize=3, +) +ax.errorbar( + forest["partial_mean"], + y_pos + 0.18, + xerr=[ + forest["partial_mean"] - forest["partial_lo"], + forest["partial_hi"] - forest["partial_mean"], + ], + fmt="s", + color="C3", + label="Partial pooling", + capsize=3, +) +ax.axvline( + complete_pool_mean, + color="black", + linestyle="--", + alpha=0.7, + label=f"Complete pooling (mean = {complete_pool_mean:.3f})", +) +ax.axvline(0.0, color="grey", linestyle=":", alpha=0.5) +ax.set_yticks(y_pos) +ax.set_yticklabels(MARKET_NAMES) +ax.set_xlabel("Estimated treatment effect") +ax.set_title("Forest plot: three pooling strategies on eight markets") +ax.legend(loc="upper right", bbox_to_anchor=(1.0, 1.0), framealpha=0.95) +plt.tight_layout(); +``` + +The partial-pooling estimates are pulled toward the population mean: the canonical *shrinkage* picture {cite:p}`gelman2006multilevel`, {cite:p}`gelman2020regression`. The pull is strongest for the markets whose individual estimates are noisiest (widest no-pooling intervals) or most extreme; it is weakest for markets whose estimates are tight and central. This is the data-driven version of "borrowing strength" that vote-counting cannot do and complete-pooling does only by force. + +```{code-cell} ipython3 +fig, ax = plt.subplots(figsize=(8, 5)) +for k in range(K): + ax.plot([0, 1], [no_pool_mean[k], partial_pool_mean[k]], color="grey", alpha=0.5, zorder=1) + ax.scatter(0, no_pool_mean[k], color="C0", zorder=3, s=55) + ax.scatter(1, partial_pool_mean[k], color="C3", zorder=3, s=55) + ax.text(1.04, partial_pool_mean[k], MARKET_NAMES[k], ha="left", va="center", fontsize=9) +ax.axhline( + complete_pool_mean, + color="black", + linestyle="--", + alpha=0.7, + label=f"Complete-pooling mean = {complete_pool_mean:.3f}", +) +ax.set_xticks([0, 1]) +ax.set_xticklabels(["No pooling", "Partial pooling"]) +ax.set_xlim(-0.15, 1.25) +ax.set_ylabel("Estimated treatment effect") +ax.set_title("Shrinkage: per-market estimates pulled toward the population mean") +ax.legend(loc="lower right"); +``` + +### The substance lives in $\tau$ + +The hierarchical model returns two population-level quantities, and the conventional reporting habit of leading with $\mu$ obscures the more important one. The posterior of $\tau$ — the between-market standard deviation of true effects — is what tells the team how transportable any single result is to a new context. A small $\tau$ means the markets are nearly exchangeable, and the experiment generalises cleanly; a large $\tau$ means the markets are heterogeneous, and the next market is a meaningfully new experiment. Reporting only $\mu$ collapses this into a point and hides the variability that the next stakeholder will live with. + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 2, figsize=(11, 3.8)) +az.plot_posterior(idata_partial, var_names=["mu"], ref_val=TRUE_MU, ax=axes[0]) +axes[0].set_title(r"Population mean $\mu$") +az.plot_posterior(idata_partial, var_names=["tau"], ref_val=TRUE_TAU, ax=axes[1]) +axes[1].set_title(r"Between-market SD $\tau$") +plt.tight_layout(); +``` + +The posterior of $\tau$ is concentrated well away from zero, which is itself the result of substantive interest: the eight markets disagree about the size of the treatment effect in a way the data demand be respected. The next section turns this into a prediction. + +## The same machinery on a binary outcome + +The conversion-rate version repeats the structure on the log-odds scale. Each market $k$ has its own baseline rate and its own treatment log-odds effect $\theta_k$; the population sits a level above ({cite:p}`carpenter2016hierarchical`). + +```{code-cell} ipython3 +TRUE_MU_LOGIT = 0.20 +TRUE_TAU_LOGIT = 0.25 +BASELINE_RATES = rng.beta(20, 180, size=K) + + +def simulate_meta_dataset_bernoulli( + K, N_per_market, baseline_rates, true_mu_logit, true_tau_logit, rng +): + from scipy.special import expit, logit + + theta = rng.normal(true_mu_logit, true_tau_logit, size=K) + n_A = np.zeros(K, dtype=int) + n_B = np.zeros(K, dtype=int) + for k in range(K): + p_A_k = baseline_rates[k] + p_B_k = expit(logit(p_A_k) + theta[k]) + n_A[k] = rng.binomial(int(N_per_market[k]), p_A_k) + n_B[k] = rng.binomial(int(N_per_market[k]), p_B_k) + return theta, n_A, n_B + + +true_theta_bern, n_A_obs, n_B_obs = simulate_meta_dataset_bernoulli( + K, N_PER_MARKET, BASELINE_RATES, TRUE_MU_LOGIT, TRUE_TAU_LOGIT, rng +) +``` + +```{code-cell} ipython3 +with pm.Model(coords=coords) as partial_bern_model: + mu_logit = pm.Normal("mu", mu=0.0, sigma=0.5) + tau_logit = pm.HalfNormal("tau", sigma=0.5) + theta_offset = pm.Normal("theta_offset", mu=0.0, sigma=1.0, dims="market") + theta = pm.Deterministic("theta", mu_logit + tau_logit * theta_offset, dims="market") + baseline_logit = pm.Normal("baseline_logit", mu=-2.0, sigma=1.0, dims="market") + p_A = pm.Deterministic("p_A", pm.math.invlogit(baseline_logit), dims="market") + p_B = pm.Deterministic("p_B", pm.math.invlogit(baseline_logit + theta), dims="market") + pm.Binomial("obs_A", n=N_PER_MARKET, p=p_A, observed=n_A_obs, dims="market") + pm.Binomial("obs_B", n=N_PER_MARKET, p=p_B, observed=n_B_obs, dims="market") + +with partial_bern_model: + idata_partial_bern = pm.sample( + draws=2000, + tune=2000, + chains=2, + target_accept=0.95, + random_seed=RANDOM_SEED, + progressbar=False, + ) +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 2, figsize=(11, 3.8)) +az.plot_posterior(idata_partial_bern, var_names=["mu"], ref_val=TRUE_MU_LOGIT, ax=axes[0]) +axes[0].set_title(r"Population log-odds effect $\mu$") +az.plot_posterior(idata_partial_bern, var_names=["tau"], ref_val=TRUE_TAU_LOGIT, ax=axes[1]) +axes[1].set_title(r"Between-market SD $\tau$ (log-odds)") +plt.tight_layout(); +``` + +The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. "Meta-analysis" is not a binary-outcome technique borrowed from medicine. It is the same Bayesian operation regardless of likelihood. + +## Predicting the next experiment + +The stakeholder question that occasioned this entire exercise is rarely "what was the population mean?" — it is "what will happen if we run this in Market I next quarter?". The hierarchical model answers in two posterior-predictive flavours, and the difference between them matters. + +**The true-effect predictive** asks for the unknown $\theta_{\text{new}}$ of a new market drawn from the same population: + +$$\theta_{\text{new}} \mid \text{data} \sim \mathcal{N}(\mu_{\text{post}}, \sqrt{\tau_{\text{post}}^2 + \sigma_{\mu, \text{post}}^2}).$$ + +**The observation predictive** layers experimental noise on top: + +$$\hat d_{\text{new}} \mid \text{data} \sim \theta_{\text{new}} + \mathcal{N}(0, s_{\text{new}}).$$ + +The first is what the team should report when they say "our best estimate of the effect in a new market". The second is what they should report when they say "what we expect to see if we run the experiment again". The gap between the two is exactly the experimental-noise envelope that single-market estimates conflate with population variation. + +```{code-cell} ipython3 +mu_samples = idata_partial.posterior["mu"].values.flatten() +tau_samples = idata_partial.posterior["tau"].values.flatten() +rng_pp = np.random.default_rng(RANDOM_SEED + 1) +theta_new_samples = rng_pp.normal(mu_samples, tau_samples) +s_new = np.median(se_obs) +d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples)) + +fig, ax = plt.subplots(figsize=(7.5, 4.5)) +bins = np.linspace( + min(d_hat_new_samples.min(), theta_new_samples.min()) - 0.2, + max(d_hat_new_samples.max(), theta_new_samples.max()) + 0.2, + 80, +) +ax.hist( + theta_new_samples, + bins=bins, + alpha=0.55, + label=r"True effect $\theta_{\text{new}}$", + color="C0", + density=True, +) +ax.hist( + d_hat_new_samples, + bins=bins, + alpha=0.45, + label=r"Observed estimate $\hat d_{\text{new}}$", + color="C3", + density=True, +) +ax.axvline(0.0, color="black", linestyle=":") +ax.axvline(mu_samples.mean(), color="C0", linestyle="--", alpha=0.8, label=r"$\mu_{\text{post}}$") +ax.set_xlabel("Predicted effect in a new market") +ax.set_ylabel("Posterior predictive density") +ax.set_title("Two predictive distributions for the ninth market") +ax.legend(); +``` + +```{code-cell} ipython3 +prob_theta_new_positive = float((theta_new_samples > 0).mean()) +prob_d_hat_new_positive = float((d_hat_new_samples > 0).mean()) +pd.DataFrame( + {"posterior probability > 0": [prob_theta_new_positive, prob_d_hat_new_positive]}, + index=[r"$\theta_{\text{new}}$ (true effect)", r"$\hat d_{\text{new}}$ (observed estimate)"], +).round(3) +``` + +The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\theta_{\text{new}}$ as the planning input for the next market — which is what {ref}`assurance_planning` will then consume as its prior — is the way to feed accumulating evidence forward without losing track of the noise. + +## Replication as the data-generating process + +A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation rather than as eight unrelated noises, and the posterior on the population is not a number that "summarises the experiments". It is the population our experiments were drawn from, made audible by being listened to as a chorus. + +This is the deeper reframing that the meta-analytic posture earns. Replication is often described as a verification protocol: a check we run on a result, after the fact, to see if it survives. The Bayesian version is more substantive than that. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The posterior on $\mu$ and on $\tau$ are the two halves of what we have inferred about the unobserved-but-real distribution of effects across the worlds the team operates in. + +The triptych closes here, though "closes" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three questions, one machinery, and in each the same discipline: the quantity a conventional analysis leaves implicit, whether the believed effect, the unmeasured confounder, or the between-study variance, is made an explicit parameter with a posterior, and so becomes something a team can argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The posterior is the constant; the question changes. + +And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\theta_{\text{new}} \sim \mathcal{N}(\mu, \tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it. + +## Authors + +- Authored by [Nathaniel Forde](https://nathanielf.github.io/) in May 2026. + +## References + +:::{bibliography} +:filter: docname in docnames +::: + +## Watermark + +```{code-cell} ipython3 +%load_ext watermark +%watermark -n -u -v -iv -w -p pytensor,xarray +``` + +:::{include} ../page_footer.md +::: diff --git a/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb b/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb new file mode 100644 index 000000000..a4308af90 --- /dev/null +++ b/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb @@ -0,0 +1,1149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dd69e77a", + "metadata": {}, + "source": [ + "(sensitivity_confounding)=\n", + "# Sensitivity Analysis for Unmeasured Confounding\n", + "\n", + ":::{post} May 2026\n", + ":tags: experimentation, sensitivity analysis, causal inference, confounding, robustness\n", + ":category: intermediate, reference\n", + ":author: Nathaniel Forde\n", + ":::\n", + "\n", + "## The randomisation gap\n", + "\n", + "Randomisation is an assumption you maintain by design, not a fact you assert by intention. The checkout experiment ran, the headline effect on revenue-per-visitor looks positive, and somewhere in the diagnostic notes there is a line about compliance being 73%: users on slow connections were silently routed back to the old flow by a CDN edge case, and the routing was not random in the way it touched the population. The intent-to-treat estimate now depends on a counterfactual the experiment did not produce: what those users would have done under the new flow. Without further assumptions, the data cannot say what the treatment effect would have been if randomisation had held.\n", + "\n", + "This notebook develops the Bayesian response to this situation. Where Rosenbaum's $\\Gamma$ {cite:p}`rosenbaum2002observational` and the E-value {cite:p}`vanderweele2017sensitivity` give point summaries of how strong an unmeasured confounder would need to be to nullify the result, the Bayesian framing makes the same unmeasured confounder a *parameter* in the model, with a prior over its plausible strength and a posterior shaped by both the data and the analyst's commitments {cite:p}`imbens2003sensitivity`, {cite:p}`cinellihazlett2020omitted`. We develop the machinery on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the second of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart.\n", + "\n", + ":::{admonition} Where this lands in regulatory practice\n", + ":class: note\n", + "\n", + "The sensitivity construction here is not a workaround; it is the regulatory recommendation. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials describes sensitivity analysis as varying the prior over a critical assumption, and notes that \"some approaches can build uncertainty about specific assumptions into the prior itself\" {cite:p}`fda2026bayesian`, which is the move made below. The guidance goes further and sanctions modelling a discrepancy between data sources with \"an assumed bias parameter in the model\", the exact object this notebook places a prior on and sweeps. The clinical-trial setting differs from a product quasi-experiment; the bias parameter is the same.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3abbded2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:40.349921Z", + "iopub.status.busy": "2026-05-28T16:43:40.349744Z", + "iopub.status.idle": "2026-05-28T16:43:43.735989Z", + "shell.execute_reply": "2026-05-28T16:43:43.735368Z" + } + }, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "from dataclasses import dataclass\n", + "\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pymc as pm\n", + "import pytensor.tensor as pt\n", + "import seaborn as sns\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f669edc2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:43.737679Z", + "iopub.status.busy": "2026-05-28T16:43:43.737482Z", + "iopub.status.idle": "2026-05-28T16:43:43.741538Z", + "shell.execute_reply": "2026-05-28T16:43:43.740953Z" + } + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "az.style.use(\"arviz-darkgrid\")\n", + "rng = np.random.default_rng(7)\n", + "RANDOM_SEED = 7" + ] + }, + { + "cell_type": "markdown", + "id": "31dda6b3", + "metadata": {}, + "source": [ + "## What the data can and cannot say\n", + "\n", + "The structural picture is small and the notation traditional. Let $T$ be the observed treatment indicator (which flow the visitor actually saw), let $Y$ be the outcome (revenue), and let $U$ be an unmeasured user characteristic: connection quality, engagement disposition, whatever drives the differential compliance. Under randomisation $U$ is independent of $T$ by construction; under compliance failure $U$ is associated with $T$ in a way the analyst cannot directly observe." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dc1a9456", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:43.742885Z", + "iopub.status.busy": "2026-05-28T16:43:43.742784Z", + "iopub.status.idle": "2026-05-28T16:43:44.463213Z", + "shell.execute_reply": "2026-05-28T16:43:44.462579Z" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 361, + "width": 611 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 3.5))\n", + "ax.set_xlim(0, 10)\n", + "ax.set_ylim(0, 5)\n", + "ax.axis(\"off\")\n", + "\n", + "nodes = {\"T\": (2, 1.5), \"Y\": (8, 1.5), \"U\": (5, 4)}\n", + "for name, (x, y) in nodes.items():\n", + " ax.scatter(x, y, s=2200, facecolor=\"white\", edgecolor=\"black\", zorder=3)\n", + " ax.text(x, y, name, ha=\"center\", va=\"center\", fontsize=16, zorder=4)\n", + "\n", + "\n", + "def arrow(a, b, **kw):\n", + " (x0, y0), (x1, y1) = nodes[a], nodes[b]\n", + " ax.annotate(\n", + " \"\",\n", + " xy=(x1, y1),\n", + " xytext=(x0, y0),\n", + " arrowprops=dict(arrowstyle=\"->\", lw=2, shrinkA=22, shrinkB=22, **kw),\n", + " )\n", + "\n", + "\n", + "arrow(\"T\", \"Y\")\n", + "arrow(\"U\", \"T\", color=\"firebrick\")\n", + "arrow(\"U\", \"Y\", color=\"firebrick\")\n", + "\n", + "ax.text(5, 1.8, r\"$\\tau$ (causal effect)\", ha=\"center\", fontsize=11)\n", + "ax.text(3.2, 3.3, r\"$\\lambda_T$\", color=\"firebrick\", fontsize=12)\n", + "ax.text(6.8, 3.3, r\"$\\lambda_Y$\", color=\"firebrick\", fontsize=12)\n", + "ax.set_title(\"The identification gap: $U$ unobserved\", fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "e8f31fe6", + "metadata": {}, + "source": [ + "What we want is $\\tau$, the causal effect of $T$ on $Y$. What the data identify is $\\tau$ *plus* the contribution of any path that flows through $U$ — specifically the product of the two red edges, which we collect into a single bias term $\\beta = \\lambda_T \\cdot \\lambda_Y / \\mathrm{var}(T)$ at the scale of the observed difference in means {cite:p}`hernan2020whatif`, {cite:p}`cunningham2021causal`. The naïve estimator returns $\\tau + \\beta$. The data are silent on the decomposition; the prior is what sets it.\n", + "\n", + "## The bias parameter\n", + "\n", + "The Bayesian sensitivity analysis introduces $\\beta$ as a model parameter with an explicit prior. The data inform the sum $\\tau + \\beta$ through the observed difference; the prior on $\\beta$ controls how much of that sum is attributed to the unmeasured confounder rather than to the treatment. Three commitments are useful to compare:\n", + "\n", + "- **Dismissive prior** ($\\beta \\sim \\mathcal{N}(0, 0.05)$): \"I am confident there is essentially no confounding.\"\n", + "- **Moderate prior** ($\\beta \\sim \\mathcal{N}(0, 0.3)$): \"I will allow that confounding may have shifted the apparent effect by up to a few tenths of a revenue unit.\"\n", + "- **Sceptical prior** ($\\beta \\sim \\mathcal{N}(0, 0.7)$): \"I will not commit to confounding being small; the data must speak loudly to be heard.\"\n", + "\n", + "Each commitment is a separable, auditable claim. The plot we are about to build shows how the posterior on $\\tau$ moves as the analyst slides this commitment." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9d37c3cd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:44.464947Z", + "iopub.status.busy": "2026-05-28T16:43:44.464802Z", + "iopub.status.idle": "2026-05-28T16:43:44.468035Z", + "shell.execute_reply": "2026-05-28T16:43:44.467575Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observed difference d_hat = 0.793 (sampling SE = 0.089)\n", + "True tau = 0.3, true bias = 0.5, true sum = 0.8\n" + ] + } + ], + "source": [ + "N = 4000\n", + "TRUE_TAU = 0.30\n", + "TRUE_BIAS = 0.50\n", + "SIGMA_OBS = 4.0\n", + "\n", + "\n", + "def simulate_quasi_experimental_gaussian(N, true_tau, true_bias, sigma_obs, rng):\n", + " baseline = 10.0\n", + " y_A = rng.normal(baseline, sigma_obs, size=N)\n", + " y_B = rng.normal(baseline + true_tau + true_bias, sigma_obs, size=N)\n", + " return y_A, y_B\n", + "\n", + "\n", + "y_A_obs, y_B_obs = simulate_quasi_experimental_gaussian(N, TRUE_TAU, TRUE_BIAS, SIGMA_OBS, rng)\n", + "d_hat = y_B_obs.mean() - y_A_obs.mean()\n", + "sigma_d = np.sqrt(2 * SIGMA_OBS**2 / N)\n", + "print(f\"Observed difference d_hat = {d_hat:.3f} (sampling SE = {sigma_d:.3f})\")\n", + "print(f\"True tau = {TRUE_TAU}, true bias = {TRUE_BIAS}, true sum = {TRUE_TAU + TRUE_BIAS}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ba7bfe6c", + "metadata": {}, + "source": [ + "The observed difference recovers the *biased* effect, as it must. The treatment effect $\\tau$ is what we want; the bias $\\beta$ is the channel we cannot observe. We now build a PyMC model that asks the data which decomposition is most plausible *given* a prior on the bias." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7d543aca", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:44.469277Z", + "iopub.status.busy": "2026-05-28T16:43:44.469185Z", + "iopub.status.idle": "2026-05-28T16:43:46.724450Z", + "shell.execute_reply": "2026-05-28T16:43:46.723745Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [tau, beta]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 2_000 tune and 2_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 383, + "width": 811 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "def gaussian_sensitivity_model(d_hat, sigma_d, tau_prior=(0.0, 1.0), bias_prior=(0.0, 0.3)):\n", + " with pm.Model() as model:\n", + " tau = pm.Normal(\"tau\", mu=tau_prior[0], sigma=tau_prior[1])\n", + " beta = pm.Normal(\"beta\", mu=bias_prior[0], sigma=bias_prior[1])\n", + " observed_effect = pm.Deterministic(\"observed_effect\", tau + beta)\n", + " pm.Normal(\"d_hat\", mu=observed_effect, sigma=sigma_d, observed=d_hat)\n", + " return model\n", + "\n", + "\n", + "with gaussian_sensitivity_model(d_hat, sigma_d, bias_prior=(0.0, 0.3)):\n", + " idata_moderate = pm.sample(\n", + " draws=2000,\n", + " tune=2000,\n", + " chains=2,\n", + " target_accept=0.95,\n", + " random_seed=RANDOM_SEED,\n", + " progressbar=False,\n", + " )\n", + "\n", + "axes = az.plot_posterior(\n", + " idata_moderate, var_names=[\"tau\", \"beta\"], ref_val=[TRUE_TAU, TRUE_BIAS], figsize=(8, 3.5)\n", + ")\n", + "plt.suptitle(r\"Posterior of $\\tau$ and $\\beta$ under a moderate bias prior\", y=1.05);" + ] + }, + { + "cell_type": "markdown", + "id": "74929da4", + "metadata": {}, + "source": [ + "The posterior of $\\tau$ is centred well above zero but with substantial uncertainty; the posterior of $\\beta$ is also non-zero, reflecting the prior pull. The data have informed the sum but the decomposition is shaped by the prior on $\\beta$. The next move is to vary that prior and see what the data tolerate.\n", + "\n", + "### Tipping-point analysis under a Gaussian outcome\n", + "\n", + "The model with a flat prior on $\\tau$ and a Gaussian prior on $\\beta$ is fully conjugate; the posterior is a closed-form bivariate Normal that we can compute analytically. This lets us trace the tipping point — the bias prior strength at which the posterior probability of a positive treatment effect drops below the team's decision threshold." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2f6ee70f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:46.725894Z", + "iopub.status.busy": "2026-05-28T16:43:46.725793Z", + "iopub.status.idle": "2026-05-28T16:43:46.741266Z", + "shell.execute_reply": "2026-05-28T16:43:46.740704Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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MCMCAnalytical
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tau0.7400.3040.7220.299
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" + ], + "text/plain": [ + " MCMC Analytical \n", + " mean sd mean sd\n", + "tau 0.740 0.304 0.722 0.299\n", + "beta 0.048 0.292 0.065 0.287" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def gaussian_sensitivity_posterior(\n", + " d_hat, sigma_d, tau_prior_sd, bias_prior_sd, tau_prior_mean=0.0, bias_prior_mean=0.0\n", + "):\n", + " \"\"\"Closed-form posterior over (tau, beta) given the observed effect.\"\"\"\n", + " prior_precision = np.diag([1.0 / tau_prior_sd**2, 1.0 / bias_prior_sd**2])\n", + " F = np.array([[1.0, 1.0]])\n", + " data_precision = F.T @ F / sigma_d**2\n", + " post_precision = prior_precision + data_precision\n", + " post_cov = np.linalg.inv(post_precision)\n", + " prior_mean = np.array([tau_prior_mean, bias_prior_mean])\n", + " rhs = prior_precision @ prior_mean + F.flatten() * d_hat / sigma_d**2\n", + " post_mean = post_cov @ rhs\n", + " return post_mean, post_cov\n", + "\n", + "\n", + "# Verify analytical agrees with MCMC under the moderate prior\n", + "post_mean_anly, post_cov_anly = gaussian_sensitivity_posterior(\n", + " d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=0.3\n", + ")\n", + "mcmc_summary = az.summary(idata_moderate, var_names=[\"tau\", \"beta\"], kind=\"stats\")[[\"mean\", \"sd\"]]\n", + "analytical_summary = pd.DataFrame(\n", + " {\"mean\": post_mean_anly, \"sd\": np.sqrt(np.diag(post_cov_anly))},\n", + " index=[\"tau\", \"beta\"],\n", + ")\n", + "pd.concat({\"MCMC\": mcmc_summary, \"Analytical\": analytical_summary}, axis=1).round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "3d2acc0d", + "metadata": {}, + "source": [ + "The two posteriors agree. The analytical form carries the sweep." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e9d91b3f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:46.742773Z", + "iopub.status.busy": "2026-05-28T16:43:46.742660Z", + "iopub.status.idle": "2026-05-28T16:43:46.998802Z", + "shell.execute_reply": "2026-05-28T16:43:46.998294Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 411, + "width": 1111 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from scipy import stats\n", + "\n", + "bias_sd_grid = np.linspace(0.001, 1.0, 40)\n", + "sweep_records = []\n", + "for bias_sd in bias_sd_grid:\n", + " pm_a, pc_a = gaussian_sensitivity_posterior(\n", + " d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=bias_sd\n", + " )\n", + " tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0])\n", + " prob_positive = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd)\n", + " sweep_records.append(\n", + " {\"bias_sd\": bias_sd, \"tau_mean\": tau_mean, \"tau_sd\": tau_sd, \"prob_positive\": prob_positive}\n", + " )\n", + "sweep_df = pd.DataFrame(sweep_records)\n", + "\n", + "decision_threshold = 0.95\n", + "above_threshold = sweep_df[sweep_df[\"prob_positive\"] >= decision_threshold]\n", + "tipping_point = above_threshold[\"bias_sd\"].max() if len(above_threshold) else np.nan\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "axes[0].plot(\n", + " sweep_df[\"bias_sd\"],\n", + " sweep_df[\"tau_mean\"],\n", + " linewidth=2,\n", + " color=\"C0\",\n", + " label=r\"Posterior mean of $\\tau$\",\n", + ")\n", + "axes[0].fill_between(\n", + " sweep_df[\"bias_sd\"],\n", + " sweep_df[\"tau_mean\"] - sweep_df[\"tau_sd\"],\n", + " sweep_df[\"tau_mean\"] + sweep_df[\"tau_sd\"],\n", + " alpha=0.25,\n", + " color=\"C0\",\n", + " label=r\"$\\pm 1$ SD\",\n", + ")\n", + "axes[0].axhline(0.0, color=\"black\", linestyle=\":\")\n", + "axes[0].set_xlabel(r\"Prior SD on bias $\\beta$\")\n", + "axes[0].set_ylabel(r\"Posterior of $\\tau$\")\n", + "axes[0].set_title(\"Posterior shifts as the bias prior loosens\")\n", + "axes[0].legend()\n", + "\n", + "axes[1].plot(sweep_df[\"bias_sd\"], sweep_df[\"prob_positive\"], linewidth=2, color=\"C3\")\n", + "axes[1].axhline(\n", + " decision_threshold,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + " label=f\"Decision threshold = {decision_threshold}\",\n", + ")\n", + "if not np.isnan(tipping_point):\n", + " axes[1].axvline(\n", + " tipping_point,\n", + " color=\"C3\",\n", + " linestyle=\":\",\n", + " alpha=0.7,\n", + " label=f\"Tipping point ≈ {tipping_point:.2f}\",\n", + " )\n", + "axes[1].set_xlabel(r\"Prior SD on bias $\\beta$\")\n", + "axes[1].set_ylabel(r\"$P(\\tau > 0 \\mid d_{\\text{obs}})$\")\n", + "axes[1].set_title(\"Tipping point in the decision rule\")\n", + "axes[1].legend();" + ] + }, + { + "cell_type": "markdown", + "id": "c223dfcb", + "metadata": {}, + "source": [ + "Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number.\n", + "\n", + "### The sensitivity surface\n", + "\n", + "The previous sweep moved one dimension of the bias prior. The bias prior has two, the mean and the standard deviation, and an analyst defending the conclusion ought to be able to point at the region of $(\\mu_\\beta, \\sigma_\\beta)$ where the decision still goes through. The two-dimensional sweep below maps it." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c46490fc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:47.000450Z", + "iopub.status.busy": "2026-05-28T16:43:47.000329Z", + "iopub.status.idle": "2026-05-28T16:43:47.251663Z", + "shell.execute_reply": "2026-05-28T16:43:47.251221Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 511, + "width": 660 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "bias_mu_grid = np.linspace(-0.5, 0.5, 25)\n", + "bias_sd_grid_2d = np.linspace(0.01, 1.0, 25)\n", + "prob_grid = np.zeros((len(bias_sd_grid_2d), len(bias_mu_grid)))\n", + "\n", + "for i, b_sd in enumerate(bias_sd_grid_2d):\n", + " for j, b_mu in enumerate(bias_mu_grid):\n", + " pm_a, pc_a = gaussian_sensitivity_posterior(\n", + " d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=b_sd, bias_prior_mean=b_mu\n", + " )\n", + " tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0])\n", + " prob_grid[i, j] = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd)\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.5, 5))\n", + "im = ax.imshow(\n", + " prob_grid,\n", + " origin=\"lower\",\n", + " aspect=\"auto\",\n", + " extent=[bias_mu_grid.min(), bias_mu_grid.max(), bias_sd_grid_2d.min(), bias_sd_grid_2d.max()],\n", + " cmap=\"RdBu_r\",\n", + " vmin=0.0,\n", + " vmax=1.0,\n", + ")\n", + "contour = ax.contour(\n", + " bias_mu_grid,\n", + " bias_sd_grid_2d,\n", + " prob_grid,\n", + " levels=[0.5, decision_threshold],\n", + " colors=[\"white\", \"black\"],\n", + " linewidths=[1.2, 2.0],\n", + ")\n", + "ax.clabel(contour, fmt={0.5: \"P=0.5\", decision_threshold: f\"P={decision_threshold}\"})\n", + "ax.set_xlabel(r\"Prior mean of bias $\\beta$\")\n", + "ax.set_ylabel(r\"Prior SD of bias $\\beta$\")\n", + "ax.set_title(r\"$P(\\tau > 0)$ under bias-prior commitments\")\n", + "plt.colorbar(im, ax=ax, label=r\"$P(\\tau > 0 \\mid d_{\\text{obs}})$\");" + ] + }, + { + "cell_type": "markdown", + "id": "49fd3d5f", + "metadata": {}, + "source": [ + "The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right; it tells both parties exactly what they are disagreeing about.\n", + "\n", + "## The same machinery on a binary outcome\n", + "\n", + "The conversion-rate version of the experiment uses the same structural model on the log-odds scale. The bias parameter now lives on the logit, but the framing is identical: the observed log-odds difference is the sum of a treatment effect and an unmeasured-confounder contribution, and a prior over the latter is the only thing that separates them." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "97c262c4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:47.253070Z", + "iopub.status.busy": "2026-05-28T16:43:47.252954Z", + "iopub.status.idle": "2026-05-28T16:43:47.256528Z", + "shell.execute_reply": "2026-05-28T16:43:47.255823Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observed conversions: A = 834/8000, B = 1165/8000\n", + "True p_A = 0.1000, p_B (with bias) = 0.1484\n" + ] + } + ], + "source": [ + "def simulate_quasi_experimental_bernoulli(N, baseline_rate, true_tau_logit, true_bias_logit, rng):\n", + " from scipy.special import expit\n", + "\n", + " p_A = expit(np.log(baseline_rate / (1 - baseline_rate)))\n", + " p_B = expit(np.log(baseline_rate / (1 - baseline_rate)) + true_tau_logit + true_bias_logit)\n", + " n_A = rng.binomial(N, p_A)\n", + " n_B = rng.binomial(N, p_B)\n", + " return n_A, n_B, p_A, p_B\n", + "\n", + "\n", + "BASELINE_RATE_B = 0.10\n", + "TRUE_TAU_LOGIT = 0.15\n", + "TRUE_BIAS_LOGIT = 0.30\n", + "\n", + "n_A_obs, n_B_obs, p_A_true, p_B_true = simulate_quasi_experimental_bernoulli(\n", + " N=8000,\n", + " baseline_rate=BASELINE_RATE_B,\n", + " true_tau_logit=TRUE_TAU_LOGIT,\n", + " true_bias_logit=TRUE_BIAS_LOGIT,\n", + " rng=rng,\n", + ")\n", + "print(f\"Observed conversions: A = {n_A_obs}/8000, B = {n_B_obs}/8000\")\n", + "print(f\"True p_A = {p_A_true:.4f}, p_B (with bias) = {p_B_true:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e0f3171d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:47.257811Z", + "iopub.status.busy": "2026-05-28T16:43:47.257686Z", + "iopub.status.idle": "2026-05-28T16:43:58.798096Z", + "shell.execute_reply": "2026-05-28T16:43:58.797584Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [logit_p_A, tau, beta]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 1 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [logit_p_A, tau, beta]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 1 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [logit_p_A, tau, beta]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 2 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [logit_p_A, tau, beta]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 2 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 2 jobs)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NUTS: [logit_p_A, tau, beta]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 3 seconds.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " bias_sd tau_mean tau_sd prob_positive\n", + "0 0.05 0.374 0.067 1.000\n", + "1 0.15 0.351 0.154 0.987\n", + "2 0.30 0.280 0.254 0.870\n", + "3 0.50 0.183 0.338 0.700\n", + "4 0.80 0.110 0.418 0.610" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def bernoulli_sensitivity_model(\n", + " n_A, n_B, N, baseline_logit_prior=(-2.0, 0.5), tau_prior=(0.0, 0.5), bias_prior=(0.0, 0.3)\n", + "):\n", + " with pm.Model() as model:\n", + " logit_p_A = pm.Normal(\n", + " \"logit_p_A\", mu=baseline_logit_prior[0], sigma=baseline_logit_prior[1]\n", + " )\n", + " tau = pm.Normal(\"tau\", mu=tau_prior[0], sigma=tau_prior[1])\n", + " beta = pm.Normal(\"beta\", mu=bias_prior[0], sigma=bias_prior[1])\n", + " logit_p_B = pm.Deterministic(\"logit_p_B\", logit_p_A + tau + beta)\n", + " p_A = pm.Deterministic(\"p_A\", pm.math.invlogit(logit_p_A))\n", + " p_B = pm.Deterministic(\"p_B\", pm.math.invlogit(logit_p_B))\n", + " pm.Binomial(\"obs_A\", n=N, p=p_A, observed=n_A)\n", + " pm.Binomial(\"obs_B\", n=N, p=p_B, observed=n_B)\n", + " return model\n", + "\n", + "\n", + "tipping_records_bern = []\n", + "for bias_sd in [0.05, 0.15, 0.30, 0.50, 0.80]:\n", + " with bernoulli_sensitivity_model(n_A_obs, n_B_obs, N=8000, bias_prior=(0.0, bias_sd)):\n", + " idata = pm.sample(\n", + " draws=1000,\n", + " tune=1000,\n", + " chains=2,\n", + " target_accept=0.95,\n", + " random_seed=RANDOM_SEED,\n", + " progressbar=False,\n", + " )\n", + " tau_samples = idata.posterior[\"tau\"].values.flatten()\n", + " tipping_records_bern.append(\n", + " {\n", + " \"bias_sd\": bias_sd,\n", + " \"tau_mean\": tau_samples.mean(),\n", + " \"tau_sd\": tau_samples.std(),\n", + " \"prob_positive\": float((tau_samples > 0).mean()),\n", + " }\n", + " )\n", + "tipping_bern_df = pd.DataFrame(tipping_records_bern)\n", + "tipping_bern_df.round(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "69190ddc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:58.799637Z", + "iopub.status.busy": "2026-05-28T16:43:58.799522Z", + "iopub.status.idle": "2026-05-28T16:43:59.004406Z", + "shell.execute_reply": "2026-05-28T16:43:59.003909Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 411, + "width": 1111 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "axes[0].errorbar(\n", + " tipping_bern_df[\"bias_sd\"],\n", + " tipping_bern_df[\"tau_mean\"],\n", + " yerr=tipping_bern_df[\"tau_sd\"],\n", + " marker=\"o\",\n", + " linewidth=2,\n", + " capsize=4,\n", + ")\n", + "axes[0].axhline(0.0, color=\"black\", linestyle=\":\")\n", + "axes[0].set_xlabel(r\"Prior SD on bias $\\beta$ (log-odds)\")\n", + "axes[0].set_ylabel(r\"Posterior of $\\tau$ (log-odds)\")\n", + "axes[0].set_title(\"Bernoulli case: posterior of treatment effect\")\n", + "\n", + "axes[1].plot(\n", + " tipping_bern_df[\"bias_sd\"],\n", + " tipping_bern_df[\"prob_positive\"],\n", + " marker=\"o\",\n", + " linewidth=2,\n", + " color=\"C3\",\n", + ")\n", + "axes[1].axhline(\n", + " decision_threshold,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + " label=f\"Decision threshold = {decision_threshold}\",\n", + ")\n", + "axes[1].set_xlabel(r\"Prior SD on bias $\\beta$ (log-odds)\")\n", + "axes[1].set_ylabel(r\"$P(\\tau > 0 \\mid \\text{data})$\")\n", + "axes[1].set_title(\"Tipping point on the log-odds scale\")\n", + "axes[1].legend();" + ] + }, + { + "cell_type": "markdown", + "id": "ac0cfdac", + "metadata": {}, + "source": [ + "The shape of the curve is the one we saw in the Gaussian case. The decision rule loses confidence in the positive treatment effect as the analyst becomes more willing to attribute the observed log-odds shift to an unmeasured confounder. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve here is the full posterior over the bias-prior commitment, and is the object the analyst should actually be reading.\n", + "\n", + "## Robustness as a posterior question\n", + "\n", + "The randomisation gap is not closed by a sensitivity analysis. It is mapped. The analyst who runs the sweep above has not proved the experiment robust; they have produced an audit-able exhibit of which bias commitments the experiment can survive, and which it cannot. The audit is the deliverable.\n", + "\n", + "This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. Two analysts holding the same data and disagreeing about a conclusion are not really disagreeing about the data. They are disagreeing about which side of the tipping point their prior commitments sit on. The Bayesian sensitivity machinery makes the locus of disagreement explicit, named, and graphable.\n", + "\n", + "The deeper move is the one we are tracing across the three notebooks in this sequence. The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In all three the same move recurs: an assumption a conventional analysis would simply make becomes a parameter the analysis estimates, and so turns from something assumed into something contestable. The posterior is the constant; the question changes. We have just seen the second.\n", + "\n", + "## Authors\n", + "\n", + "- Authored by [Nathaniel Forde](https://nathanielf.github.io/) in May 2026.\n", + "\n", + "## References\n", + "\n", + ":::{bibliography}\n", + ":filter: docname in docnames\n", + ":::\n", + "\n", + "## Watermark" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "66eea177", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-28T16:43:59.006072Z", + "iopub.status.busy": "2026-05-28T16:43:59.005931Z", + "iopub.status.idle": "2026-05-28T16:43:59.022107Z", + "shell.execute_reply": "2026-05-28T16:43:59.021456Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Thu May 28 2026\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.12.12\n", + "IPython version : 9.8.0\n", + "\n", + "pytensor: 2.38.2\n", + "xarray : 2025.12.0\n", + "\n", + "matplotlib: 3.10.8\n", + "seaborn : 0.13.2\n", + "pandas : 2.3.3\n", + "scipy : 1.16.3\n", + "numpy : 2.3.5\n", + "pytensor : 2.38.2\n", + "pymc : 5.28.5\n", + "arviz : 0.23.0\n", + "\n", + "Watermark: 2.5.0\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w -p pytensor,xarray" + ] + }, + { + "cell_type": "markdown", + "id": "edf11e5a", + "metadata": {}, + "source": [ + ":::{include} ../page_footer.md\n", + ":::" + ] + } + ], + "metadata": { + "jupytext": { + "default_lexer": "ipython3" + }, + "kernelspec": { + "display_name": "pymc-marketing-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md b/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md new file mode 100644 index 000000000..3ee4e8955 --- /dev/null +++ b/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md @@ -0,0 +1,437 @@ +--- +jupytext: + default_lexer: ipython3 + text_representation: + extension: .md + format_name: myst + format_version: 0.13 +kernelspec: + display_name: pymc-marketing-dev + language: python + name: python3 +--- + +(sensitivity_confounding)= +# Sensitivity Analysis for Unmeasured Confounding + +:::{post} May 2026 +:tags: experimentation, sensitivity analysis, causal inference, confounding, robustness +:category: intermediate, reference +:author: Nathaniel Forde +::: + +## The randomisation gap + +Randomisation is an assumption you maintain by design, not a fact you assert by intention. The checkout experiment ran, the headline effect on revenue-per-visitor looks positive, and somewhere in the diagnostic notes there is a line about compliance being 73%: users on slow connections were silently routed back to the old flow by a CDN edge case, and the routing was not random in the way it touched the population. The intent-to-treat estimate now depends on a counterfactual the experiment did not produce: what those users would have done under the new flow. Without further assumptions, the data cannot say what the treatment effect would have been if randomisation had held. + +This notebook develops the Bayesian response to this situation. Where Rosenbaum's $\Gamma$ {cite:p}`rosenbaum2002observational` and the E-value {cite:p}`vanderweele2017sensitivity` give point summaries of how strong an unmeasured confounder would need to be to nullify the result, the Bayesian framing makes the same unmeasured confounder a *parameter* in the model, with a prior over its plausible strength and a posterior shaped by both the data and the analyst's commitments {cite:p}`imbens2003sensitivity`, {cite:p}`cinellihazlett2020omitted`. We develop the machinery on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the second of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. + +:::{admonition} Where this lands in regulatory practice +:class: note + +The sensitivity construction here is not a workaround; it is the regulatory recommendation. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials describes sensitivity analysis as varying the prior over a critical assumption, and notes that "some approaches can build uncertainty about specific assumptions into the prior itself" {cite:p}`fda2026bayesian`, which is the move made below. The guidance goes further and sanctions modelling a discrepancy between data sources with "an assumed bias parameter in the model", the exact object this notebook places a prior on and sweeps. The clinical-trial setting differs from a product quasi-experiment; the bias parameter is the same. +::: + +```{code-cell} ipython3 +import warnings + +from dataclasses import dataclass + +import arviz as az +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pymc as pm +import pytensor.tensor as pt +import seaborn as sns + +warnings.filterwarnings("ignore", category=RuntimeWarning) +warnings.filterwarnings("ignore", category=UserWarning) +``` + +```{code-cell} ipython3 +%config InlineBackend.figure_format = 'retina' +az.style.use("arviz-darkgrid") +rng = np.random.default_rng(7) +RANDOM_SEED = 7 +``` + +## What the data can and cannot say + +The structural picture is small and the notation traditional. Let $T$ be the observed treatment indicator (which flow the visitor actually saw), let $Y$ be the outcome (revenue), and let $U$ be an unmeasured user characteristic: connection quality, engagement disposition, whatever drives the differential compliance. Under randomisation $U$ is independent of $T$ by construction; under compliance failure $U$ is associated with $T$ in a way the analyst cannot directly observe. + +```{code-cell} ipython3 +:tags: [hide-input] + +fig, ax = plt.subplots(figsize=(6, 3.5)) +ax.set_xlim(0, 10) +ax.set_ylim(0, 5) +ax.axis("off") + +nodes = {"T": (2, 1.5), "Y": (8, 1.5), "U": (5, 4)} +for name, (x, y) in nodes.items(): + ax.scatter(x, y, s=2200, facecolor="white", edgecolor="black", zorder=3) + ax.text(x, y, name, ha="center", va="center", fontsize=16, zorder=4) + + +def arrow(a, b, **kw): + (x0, y0), (x1, y1) = nodes[a], nodes[b] + ax.annotate( + "", + xy=(x1, y1), + xytext=(x0, y0), + arrowprops=dict(arrowstyle="->", lw=2, shrinkA=22, shrinkB=22, **kw), + ) + + +arrow("T", "Y") +arrow("U", "T", color="firebrick") +arrow("U", "Y", color="firebrick") + +ax.text(5, 1.8, r"$\tau$ (causal effect)", ha="center", fontsize=11) +ax.text(3.2, 3.3, r"$\lambda_T$", color="firebrick", fontsize=12) +ax.text(6.8, 3.3, r"$\lambda_Y$", color="firebrick", fontsize=12) +ax.set_title("The identification gap: $U$ unobserved", fontsize=12); +``` + +What we want is $\tau$, the causal effect of $T$ on $Y$. What the data identify is $\tau$ *plus* the contribution of any path that flows through $U$ — specifically the product of the two red edges, which we collect into a single bias term $\beta = \lambda_T \cdot \lambda_Y / \mathrm{var}(T)$ at the scale of the observed difference in means {cite:p}`hernan2020whatif`, {cite:p}`cunningham2021causal`. The naïve estimator returns $\tau + \beta$. The data are silent on the decomposition; the prior is what sets it. + +## The bias parameter + +The Bayesian sensitivity analysis introduces $\beta$ as a model parameter with an explicit prior. The data inform the sum $\tau + \beta$ through the observed difference; the prior on $\beta$ controls how much of that sum is attributed to the unmeasured confounder rather than to the treatment. Three commitments are useful to compare: + +- **Dismissive prior** ($\beta \sim \mathcal{N}(0, 0.05)$): "I am confident there is essentially no confounding." +- **Moderate prior** ($\beta \sim \mathcal{N}(0, 0.3)$): "I will allow that confounding may have shifted the apparent effect by up to a few tenths of a revenue unit." +- **Sceptical prior** ($\beta \sim \mathcal{N}(0, 0.7)$): "I will not commit to confounding being small; the data must speak loudly to be heard." + +Each commitment is a separable, auditable claim. The plot we are about to build shows how the posterior on $\tau$ moves as the analyst slides this commitment. + +```{code-cell} ipython3 +N = 4000 +TRUE_TAU = 0.30 +TRUE_BIAS = 0.50 +SIGMA_OBS = 4.0 + + +def simulate_quasi_experimental_gaussian(N, true_tau, true_bias, sigma_obs, rng): + baseline = 10.0 + y_A = rng.normal(baseline, sigma_obs, size=N) + y_B = rng.normal(baseline + true_tau + true_bias, sigma_obs, size=N) + return y_A, y_B + + +y_A_obs, y_B_obs = simulate_quasi_experimental_gaussian(N, TRUE_TAU, TRUE_BIAS, SIGMA_OBS, rng) +d_hat = y_B_obs.mean() - y_A_obs.mean() +sigma_d = np.sqrt(2 * SIGMA_OBS**2 / N) +print(f"Observed difference d_hat = {d_hat:.3f} (sampling SE = {sigma_d:.3f})") +print(f"True tau = {TRUE_TAU}, true bias = {TRUE_BIAS}, true sum = {TRUE_TAU + TRUE_BIAS}") +``` + +The observed difference recovers the *biased* effect, as it must. The treatment effect $\tau$ is what we want; the bias $\beta$ is the channel we cannot observe. We now build a PyMC model that asks the data which decomposition is most plausible *given* a prior on the bias. + +```{code-cell} ipython3 +def gaussian_sensitivity_model(d_hat, sigma_d, tau_prior=(0.0, 1.0), bias_prior=(0.0, 0.3)): + with pm.Model() as model: + tau = pm.Normal("tau", mu=tau_prior[0], sigma=tau_prior[1]) + beta = pm.Normal("beta", mu=bias_prior[0], sigma=bias_prior[1]) + observed_effect = pm.Deterministic("observed_effect", tau + beta) + pm.Normal("d_hat", mu=observed_effect, sigma=sigma_d, observed=d_hat) + return model + + +with gaussian_sensitivity_model(d_hat, sigma_d, bias_prior=(0.0, 0.3)): + idata_moderate = pm.sample( + draws=2000, + tune=2000, + chains=2, + target_accept=0.95, + random_seed=RANDOM_SEED, + progressbar=False, + ) + +axes = az.plot_posterior( + idata_moderate, var_names=["tau", "beta"], ref_val=[TRUE_TAU, TRUE_BIAS], figsize=(8, 3.5) +) +plt.suptitle(r"Posterior of $\tau$ and $\beta$ under a moderate bias prior", y=1.05); +``` + +The posterior of $\tau$ is centred well above zero but with substantial uncertainty; the posterior of $\beta$ is also non-zero, reflecting the prior pull. The data have informed the sum but the decomposition is shaped by the prior on $\beta$. The next move is to vary that prior and see what the data tolerate. + +### Tipping-point analysis under a Gaussian outcome + +The model with a flat prior on $\tau$ and a Gaussian prior on $\beta$ is fully conjugate; the posterior is a closed-form bivariate Normal that we can compute analytically. This lets us trace the tipping point — the bias prior strength at which the posterior probability of a positive treatment effect drops below the team's decision threshold. + +```{code-cell} ipython3 +def gaussian_sensitivity_posterior( + d_hat, sigma_d, tau_prior_sd, bias_prior_sd, tau_prior_mean=0.0, bias_prior_mean=0.0 +): + """Closed-form posterior over (tau, beta) given the observed effect.""" + prior_precision = np.diag([1.0 / tau_prior_sd**2, 1.0 / bias_prior_sd**2]) + F = np.array([[1.0, 1.0]]) + data_precision = F.T @ F / sigma_d**2 + post_precision = prior_precision + data_precision + post_cov = np.linalg.inv(post_precision) + prior_mean = np.array([tau_prior_mean, bias_prior_mean]) + rhs = prior_precision @ prior_mean + F.flatten() * d_hat / sigma_d**2 + post_mean = post_cov @ rhs + return post_mean, post_cov + + +# Verify analytical agrees with MCMC under the moderate prior +post_mean_anly, post_cov_anly = gaussian_sensitivity_posterior( + d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=0.3 +) +mcmc_summary = az.summary(idata_moderate, var_names=["tau", "beta"], kind="stats")[["mean", "sd"]] +analytical_summary = pd.DataFrame( + {"mean": post_mean_anly, "sd": np.sqrt(np.diag(post_cov_anly))}, + index=["tau", "beta"], +) +pd.concat({"MCMC": mcmc_summary, "Analytical": analytical_summary}, axis=1).round(3) +``` + +The two posteriors agree. The analytical form carries the sweep. + +```{code-cell} ipython3 +from scipy import stats + +bias_sd_grid = np.linspace(0.001, 1.0, 40) +sweep_records = [] +for bias_sd in bias_sd_grid: + pm_a, pc_a = gaussian_sensitivity_posterior( + d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=bias_sd + ) + tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0]) + prob_positive = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd) + sweep_records.append( + {"bias_sd": bias_sd, "tau_mean": tau_mean, "tau_sd": tau_sd, "prob_positive": prob_positive} + ) +sweep_df = pd.DataFrame(sweep_records) + +decision_threshold = 0.95 +above_threshold = sweep_df[sweep_df["prob_positive"] >= decision_threshold] +tipping_point = above_threshold["bias_sd"].max() if len(above_threshold) else np.nan + +fig, axes = plt.subplots(1, 2, figsize=(11, 4)) +axes[0].plot( + sweep_df["bias_sd"], + sweep_df["tau_mean"], + linewidth=2, + color="C0", + label=r"Posterior mean of $\tau$", +) +axes[0].fill_between( + sweep_df["bias_sd"], + sweep_df["tau_mean"] - sweep_df["tau_sd"], + sweep_df["tau_mean"] + sweep_df["tau_sd"], + alpha=0.25, + color="C0", + label=r"$\pm 1$ SD", +) +axes[0].axhline(0.0, color="black", linestyle=":") +axes[0].set_xlabel(r"Prior SD on bias $\beta$") +axes[0].set_ylabel(r"Posterior of $\tau$") +axes[0].set_title("Posterior shifts as the bias prior loosens") +axes[0].legend() + +axes[1].plot(sweep_df["bias_sd"], sweep_df["prob_positive"], linewidth=2, color="C3") +axes[1].axhline( + decision_threshold, + color="black", + linestyle="--", + label=f"Decision threshold = {decision_threshold}", +) +if not np.isnan(tipping_point): + axes[1].axvline( + tipping_point, + color="C3", + linestyle=":", + alpha=0.7, + label=f"Tipping point ≈ {tipping_point:.2f}", + ) +axes[1].set_xlabel(r"Prior SD on bias $\beta$") +axes[1].set_ylabel(r"$P(\tau > 0 \mid d_{\text{obs}})$") +axes[1].set_title("Tipping point in the decision rule") +axes[1].legend(); +``` + +Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. + +### The sensitivity surface + +The previous sweep moved one dimension of the bias prior. The bias prior has two, the mean and the standard deviation, and an analyst defending the conclusion ought to be able to point at the region of $(\mu_\beta, \sigma_\beta)$ where the decision still goes through. The two-dimensional sweep below maps it. + +```{code-cell} ipython3 +bias_mu_grid = np.linspace(-0.5, 0.5, 25) +bias_sd_grid_2d = np.linspace(0.01, 1.0, 25) +prob_grid = np.zeros((len(bias_sd_grid_2d), len(bias_mu_grid))) + +for i, b_sd in enumerate(bias_sd_grid_2d): + for j, b_mu in enumerate(bias_mu_grid): + pm_a, pc_a = gaussian_sensitivity_posterior( + d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=b_sd, bias_prior_mean=b_mu + ) + tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0]) + prob_grid[i, j] = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd) + +fig, ax = plt.subplots(figsize=(6.5, 5)) +im = ax.imshow( + prob_grid, + origin="lower", + aspect="auto", + extent=[bias_mu_grid.min(), bias_mu_grid.max(), bias_sd_grid_2d.min(), bias_sd_grid_2d.max()], + cmap="RdBu_r", + vmin=0.0, + vmax=1.0, +) +contour = ax.contour( + bias_mu_grid, + bias_sd_grid_2d, + prob_grid, + levels=[0.5, decision_threshold], + colors=["white", "black"], + linewidths=[1.2, 2.0], +) +ax.clabel(contour, fmt={0.5: "P=0.5", decision_threshold: f"P={decision_threshold}"}) +ax.set_xlabel(r"Prior mean of bias $\beta$") +ax.set_ylabel(r"Prior SD of bias $\beta$") +ax.set_title(r"$P(\tau > 0)$ under bias-prior commitments") +plt.colorbar(im, ax=ax, label=r"$P(\tau > 0 \mid d_{\text{obs}})$"); +``` + +The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right; it tells both parties exactly what they are disagreeing about. + +## The same machinery on a binary outcome + +The conversion-rate version of the experiment uses the same structural model on the log-odds scale. The bias parameter now lives on the logit, but the framing is identical: the observed log-odds difference is the sum of a treatment effect and an unmeasured-confounder contribution, and a prior over the latter is the only thing that separates them. + +```{code-cell} ipython3 +def simulate_quasi_experimental_bernoulli(N, baseline_rate, true_tau_logit, true_bias_logit, rng): + from scipy.special import expit + + p_A = expit(np.log(baseline_rate / (1 - baseline_rate))) + p_B = expit(np.log(baseline_rate / (1 - baseline_rate)) + true_tau_logit + true_bias_logit) + n_A = rng.binomial(N, p_A) + n_B = rng.binomial(N, p_B) + return n_A, n_B, p_A, p_B + + +BASELINE_RATE_B = 0.10 +TRUE_TAU_LOGIT = 0.15 +TRUE_BIAS_LOGIT = 0.30 + +n_A_obs, n_B_obs, p_A_true, p_B_true = simulate_quasi_experimental_bernoulli( + N=8000, + baseline_rate=BASELINE_RATE_B, + true_tau_logit=TRUE_TAU_LOGIT, + true_bias_logit=TRUE_BIAS_LOGIT, + rng=rng, +) +print(f"Observed conversions: A = {n_A_obs}/8000, B = {n_B_obs}/8000") +print(f"True p_A = {p_A_true:.4f}, p_B (with bias) = {p_B_true:.4f}") +``` + +```{code-cell} ipython3 +def bernoulli_sensitivity_model( + n_A, n_B, N, baseline_logit_prior=(-2.0, 0.5), tau_prior=(0.0, 0.5), bias_prior=(0.0, 0.3) +): + with pm.Model() as model: + logit_p_A = pm.Normal( + "logit_p_A", mu=baseline_logit_prior[0], sigma=baseline_logit_prior[1] + ) + tau = pm.Normal("tau", mu=tau_prior[0], sigma=tau_prior[1]) + beta = pm.Normal("beta", mu=bias_prior[0], sigma=bias_prior[1]) + logit_p_B = pm.Deterministic("logit_p_B", logit_p_A + tau + beta) + p_A = pm.Deterministic("p_A", pm.math.invlogit(logit_p_A)) + p_B = pm.Deterministic("p_B", pm.math.invlogit(logit_p_B)) + pm.Binomial("obs_A", n=N, p=p_A, observed=n_A) + pm.Binomial("obs_B", n=N, p=p_B, observed=n_B) + return model + + +tipping_records_bern = [] +for bias_sd in [0.05, 0.15, 0.30, 0.50, 0.80]: + with bernoulli_sensitivity_model(n_A_obs, n_B_obs, N=8000, bias_prior=(0.0, bias_sd)): + idata = pm.sample( + draws=1000, + tune=1000, + chains=2, + target_accept=0.95, + random_seed=RANDOM_SEED, + progressbar=False, + ) + tau_samples = idata.posterior["tau"].values.flatten() + tipping_records_bern.append( + { + "bias_sd": bias_sd, + "tau_mean": tau_samples.mean(), + "tau_sd": tau_samples.std(), + "prob_positive": float((tau_samples > 0).mean()), + } + ) +tipping_bern_df = pd.DataFrame(tipping_records_bern) +tipping_bern_df.round(3) +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 2, figsize=(11, 4)) +axes[0].errorbar( + tipping_bern_df["bias_sd"], + tipping_bern_df["tau_mean"], + yerr=tipping_bern_df["tau_sd"], + marker="o", + linewidth=2, + capsize=4, +) +axes[0].axhline(0.0, color="black", linestyle=":") +axes[0].set_xlabel(r"Prior SD on bias $\beta$ (log-odds)") +axes[0].set_ylabel(r"Posterior of $\tau$ (log-odds)") +axes[0].set_title("Bernoulli case: posterior of treatment effect") + +axes[1].plot( + tipping_bern_df["bias_sd"], + tipping_bern_df["prob_positive"], + marker="o", + linewidth=2, + color="C3", +) +axes[1].axhline( + decision_threshold, + color="black", + linestyle="--", + label=f"Decision threshold = {decision_threshold}", +) +axes[1].set_xlabel(r"Prior SD on bias $\beta$ (log-odds)") +axes[1].set_ylabel(r"$P(\tau > 0 \mid \text{data})$") +axes[1].set_title("Tipping point on the log-odds scale") +axes[1].legend(); +``` + +The shape of the curve is the one we saw in the Gaussian case. The decision rule loses confidence in the positive treatment effect as the analyst becomes more willing to attribute the observed log-odds shift to an unmeasured confounder. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve here is the full posterior over the bias-prior commitment, and is the object the analyst should actually be reading. + +## Robustness as a posterior question + +The randomisation gap is not closed by a sensitivity analysis. It is mapped. The analyst who runs the sweep above has not proved the experiment robust; they have produced an audit-able exhibit of which bias commitments the experiment can survive, and which it cannot. The audit is the deliverable. + +This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. Two analysts holding the same data and disagreeing about a conclusion are not really disagreeing about the data. They are disagreeing about which side of the tipping point their prior commitments sit on. The Bayesian sensitivity machinery makes the locus of disagreement explicit, named, and graphable. + +The deeper move is the one we are tracing across the three notebooks in this sequence. The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In all three the same move recurs: an assumption a conventional analysis would simply make becomes a parameter the analysis estimates, and so turns from something assumed into something contestable. The posterior is the constant; the question changes. We have just seen the second. + +## Authors + +- Authored by [Nathaniel Forde](https://nathanielf.github.io/) in May 2026. + +## References + +:::{bibliography} +:filter: docname in docnames +::: + +## Watermark + +```{code-cell} ipython3 +%load_ext watermark +%watermark -n -u -v -iv -w -p pytensor,xarray +``` + +:::{include} ../page_footer.md +::: diff --git a/examples/references.bib b/examples/references.bib index b6c73fe95..d9a9853ce 100644 --- a/examples/references.bib +++ b/examples/references.bib @@ -93,6 +93,12 @@ @article{bonilla2007multioutput year = {2007}, url = {https://papers.nips.cc/paper/2007/hash/66368270ffd51418ec58bd793f2d9b1b-Abstract.html} } +@book{borenstein2009meta, + title = {Introduction to Meta-Analysis}, + author = {Borenstein, Michael and Hedges, Larry V. and Higgins, Julian P. T. and Rothstein, Hannah R.}, + year = {2009}, + publisher = {John Wiley \& Sons} +} @book{breen1996regression, title = {Regression models: Censored, sample selected, or truncated data}, author = {Breen, Richard and others}, @@ -155,6 +161,16 @@ @article{ChernozhukovDoubleML year = {2018}, doi = {https://doi.org/10.1111/ectj.12097} } +@article{cinellihazlett2020omitted, + title = {Making sense of sensitivity: extending omitted variable bias}, + author = {Cinelli, Carlos and Hazlett, Chad}, + journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, + volume = {82}, + number = {1}, + pages = {39--67}, + year = {2020}, + doi = {10.1111/rssb.12348} +} @book{coles2001gev, title = "An introduction to statistical modeling of extreme values", author = "Coles, Stuart", @@ -236,6 +252,15 @@ @book{facure2023causal year = {2023}, publisher = {O'Reilly} } +@techreport{fda2026bayesian, + title = {Use of Bayesian Methodology in Clinical Trials of Drug and Biological Products: Guidance for Industry (Draft Guidance)}, + author = {{U.S. Food and Drug Administration}}, + institution = {Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER)}, + type = {Draft Guidance}, + year = {2026}, + month = jan, + note = {Distributed for comment purposes only} +} @book{fox2010bayesian, title = {Bayesian item response modeling: Theory and applications}, author = {Fox, Jean-Paul}, @@ -340,6 +365,16 @@ @book{hernan2020whatif year = {2020}, publisher = {Chapman \& Hall/CRC} } +@article{higgins2009meta, + title = {A re-evaluation of random-effects meta-analysis}, + author = {Higgins, Julian P. T. and Thompson, Simon G. and Spiegelhalter, David J.}, + journal = {Journal of the Royal Statistical Society: Series A (Statistics in Society)}, + volume = {172}, + number = {1}, + pages = {137--159}, + year = {2009}, + doi = {10.1111/j.1467-985X.2008.00552.x} +} @article{hoffman2014nuts, title = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo}, author = {Hoffman, Matthew and Gelman, Andrew}, @@ -391,6 +426,16 @@ @article{iacobucci2017mean year = {2017}, publisher = {Springer} } +@article{imbens2003sensitivity, + title = {Sensitivity to exogeneity assumptions in program evaluation}, + author = {Imbens, Guido W.}, + journal = {American Economic Review}, + volume = {93}, + number = {2}, + pages = {126--132}, + year = {2003}, + doi = {10.1257/000282803321946921} +} @book{ivezić2014astroMLtext, author = {\v{Z}eljko Ivezi\'{c} and Andrew J. Connolly and Jacob T. VanderPlas and Alexander Gray}, doi = {10.1515/9781400848911}, @@ -651,6 +696,17 @@ @online{numpyroBirthdays title = {Example: Hilbert space approximation for Gaussian processes}, url = {https://num.pyro.ai/en/stable/examples/hsgp.html} } +@article{ohagan2005assurance, + title = {Assurance in clinical trial design}, + author = {O'Hagan, Anthony and Stevens, John W. and Campbell, Michael J.}, + journal = {Pharmaceutical Statistics}, + volume = {4}, + number = {3}, + pages = {187--201}, + year = {2005}, + publisher = {Wiley Online Library}, + doi = {10.1002/pst.175} +} @online{orduz2024Birthdays, title = {Time Series Modeling with HSGP: Baby Births Example}, author = {Orduz, Juan}, @@ -757,6 +813,23 @@ @online{rochford2018 author = {Austin Rochford}, url = {https://austinrochford.com/posts/2018-11-10-monotonic-predictors.html} } +@book{rosenbaum2002observational, + title = {Observational Studies}, + author = {Rosenbaum, Paul R.}, + year = {2002}, + publisher = {Springer}, + edition = {2nd} +} +@incollection{rubin1981estimation, + title = {Estimation in parallel randomized experiments}, + author = {Rubin, Donald B.}, + journal = {Journal of Educational Statistics}, + volume = {6}, + number = {4}, + pages = {377--401}, + year = {1981}, + doi = {10.3102/10769986006004377} +} @inproceedings{salakhutdinov2008bayesian, title = {Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo}, author = {Salakhutdinov, Ruslan and Mnih, Andriy}, @@ -791,6 +864,12 @@ @article{solin2020Hilbert volume = {30}, year = {2020} } +@book{spiegelhalter2004bayesian, + title = {Bayesian Approaches to Clinical Trials and Health-Care Evaluation}, + author = {Spiegelhalter, David J. and Abrams, Keith R. and Myles, Jonathan P.}, + year = {2004}, + publisher = {John Wiley \& Sons} +} @article{spiller2013spotlights, title = {Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression}, author = {Spiller, Stephen A and Fitzsimons, Gavan J and Lynch Jr, John G and McClelland, Gary H}, @@ -874,6 +953,16 @@ @inproceedings{vanderplas2012astroML doi = {10.1109/CIDU.2012.6382200}, year = {2012} } +@article{vanderweele2017sensitivity, + title = {Sensitivity analysis in observational research: introducing the {E}-value}, + author = {VanderWeele, Tyler J. and Ding, Peng}, + journal = {Annals of Internal Medicine}, + volume = {167}, + number = {4}, + pages = {268--274}, + year = {2017}, + doi = {10.7326/M16-2607} +} @book{vehkalahti2019multivariate, title = {Multivariate Analysis for the Behavioral Sciences}, author = {Vehkalahti, K. and Everitt, B.S.}, From 8104ade384ec90c271a80ee57d89c3d9a04f7ff1 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Thu, 28 May 2026 18:40:45 +0100 Subject: [PATCH 2/9] Fix references.bib corruption from main merge --- examples/references.bib | 97 +++++++---------------------------------- 1 file changed, 16 insertions(+), 81 deletions(-) diff --git a/examples/references.bib b/examples/references.bib index 26d5c6e14..c3f82f2ec 100644 --- a/examples/references.bib +++ b/examples/references.bib @@ -786,16 +786,6 @@ @article{ohagan2005assurance year = {2005}, publisher = {Wiley Online Library}, doi = {10.1002/pst.175} -@online{numpyroBirthdays, - title = {Example: Hilbert space approximation for Gaussian processes}, - url = {https://num.pyro.ai/en/stable/examples/hsgp.html} -} -@online{orduz2024Birthdays, - title = {Time Series Modeling with HSGP: Baby Births Example}, - author = {Orduz, Juan}, - url = {https://juanitorduz.github.io/birthdays/}, - urldate = {2024-01-02}, - year = {2024} } @online{orduz2024Birthdays, title = {Time Series Modeling with HSGP: Baby Births Example}, @@ -863,21 +853,6 @@ @article{penrose1985 year = {1985}, pages = {189} } -@article{penrose1985, - title = {{GENERALIZED} {BODY} {COMPOSITION} {PREDICTION} {EQUATION} {FOR} {MEN} {USING} {SIMPLE} {MEASUREMENT} {TECHNIQUES}}, - volume = {17}, - issn = {0195-9131}, - url = {https://journals.lww.com/acsm-msse/citation/1985/04000/generalized_body_composition_prediction_equation.37.aspx}, - abstract = {An abstract is unavailable. This article is available as a PDF only.}, - language = {en-US}, - number = {2}, - urldate = {2023-10-17}, - journal = {Medicine \& Science in Sports \& Exercise}, - author = {Penrose, K. W. and Nelson, A. G. and Fisher, A. G.}, - month = apr, - year = {1985}, - pages = {189} -} @misc{quiroga2022bart, title = {Bayesian additive regression trees for probabilistic programming}, author = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A}, @@ -917,17 +892,6 @@ @article{riutort2022PracticalHilbertSpaceApproximate volume = {33}, year = {2022} } -@article{riutort2022PracticalHilbertSpaceApproximate, - author = {Riutort-Mayol, Gabriel and B{\"u}rkner, Paul-Christian and Andersen, Michael R. and Solin, Arno and Vehtari, Aki}, - doi = {10.1007/s11222-022-10167-2}, - journal = {Statistics and Computing}, - number = {1}, - pages = {17}, - title = {Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming}, - url = {https://doi.org/10.1007/s11222-022-10167-2}, - volume = {33}, - year = {2022} -} @book{roback2021beyond, title = {Beyond multiple linear regression: Applied generalized linear models and multilevel models in R}, author = {Roback, P., and Legler, J.}, @@ -946,6 +910,14 @@ @book{rosenbaum2002observational publisher = {Springer}, edition = {2nd} } +@book{rousseeuw2005robust, + author = {Rousseeuw, Peter J. and Leroy, Annick M.}, + title = {Robust Regression and Outlier Detection}, + publisher = {Wiley}, + address = {Hoboken, NJ}, + year = {2005}, + isbn = {978-0-471-48855-2} +} @incollection{rubin1981estimation, title = {Estimation in parallel randomized experiments}, author = {Rubin, Donald B.}, @@ -955,21 +927,6 @@ @incollection{rubin1981estimation pages = {377--401}, year = {1981}, doi = {10.3102/10769986006004377} -@book{rousseeuw2005robust, - author = {Rousseeuw, Peter J. and Leroy, Annick M.}, - title = {Robust Regression and Outlier Detection}, - publisher = {Wiley}, - address = {Hoboken, NJ}, - year = {2005}, - isbn = {978-0-471-48855-2} -} -@misc{säilynoja2025, - title = {Recommendations for visual predictive checks in Bayesian workflow}, - author = {Teemu S\"{a}ilynoja and Andrew R. Johnson and Osvaldo A. Martin and Aki Vehtari}, - year = {2025}, - eprint = {2503.01509}, - archiveprefix = {arXiv}, - primaryclass = {stat.ME} } @inproceedings{salakhutdinov2008bayesian, title = {Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo}, @@ -1016,16 +973,6 @@ @book{spiegelhalter2004bayesian author = {Spiegelhalter, David J. and Abrams, Keith R. and Myles, Jonathan P.}, year = {2004}, publisher = {John Wiley \& Sons} -@article{solin2020Hilbert, - author = {Solin, Arno and S{\"a}rkk{\"a}, Simo}, - doi = {10.1007/s11222-019-09886-w}, - journal = {Statistics and Computing}, - number = {2}, - pages = {419--446}, - title = {Hilbert space methods for reduced-rank Gaussian process regression}, - url = {https://doi.org/10.1007/s11222-019-09886-w}, - volume = {30}, - year = {2020} } @article{spiller2013spotlights, title = {Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression}, @@ -1051,6 +998,14 @@ @misc{szegedy2014going archiveprefix = {arXiv}, primaryclass = {cs.CV} } +@misc{säilynoja2025, + title = {Recommendations for visual predictive checks in Bayesian workflow}, + author = {Teemu S\"{a}ilynoja and Andrew R. Johnson and Osvaldo A. Martin and Aki Vehtari}, + year = {2025}, + eprint = {2503.01509}, + archiveprefix = {arXiv}, + primaryclass = {stat.ME} +} @online{Talebi2022dooperator, author = {Talebi, Shawhin}, title = {Causal Effects via the Do-operator}, @@ -1141,13 +1096,6 @@ @online{vehtari2022Birthdays urldate = {2022-03-07}, year = {2022} } -@online{vehtari2022Birthdays, - title = {Bayesian workflow book - Birthdays}, - author = {Vehtari, Aki}, - url = {https://avehtari.github.io/casestudies/Birthdays/birthdays.html}, - urldate = {2022-03-07}, - year = {2022} -} @book{venables2002mass, author = {Venables, W. N. and Ripley, B. D.}, title = {Modern Applied Statistics with {S}}, @@ -1218,19 +1166,6 @@ @article{Yao_2022 doi = {10.1214/21-BA1287}, url = {https://doi.org/10.1214/21-BA1287} } -@article{Yao_2022, - author = {Yuling Yao and Gregor Pir\v{s} and Aki Vehtari and Andrew Gelman}, - title = {{Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful}}, - volume = {17}, - journal = {Bayesian Analysis}, - number = {4}, - publisher = {International Society for Bayesian Analysis}, - pages = {1043 -- 1071}, - keywords = {Bayesian hierarchical modeling, conditional prediction, covariate shift, model averaging, prior construction, stacking}, - year = {2022}, - doi = {10.1214/21-BA1287}, - url = {https://doi.org/10.1214/21-BA1287} -} @article{yuan2009bayesian, title = {Bayesian mediation analysis.}, author = {Yuan, Ying and MacKinnon, David P}, From 6c6c6d0c4c479185a09ab8970c8cb2950748a380 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Thu, 28 May 2026 18:58:03 +0100 Subject: [PATCH 3/9] sorting references --- examples/references.bib | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/references.bib b/examples/references.bib index c3f82f2ec..b3b0a2598 100644 --- a/examples/references.bib +++ b/examples/references.bib @@ -928,6 +928,14 @@ @incollection{rubin1981estimation year = {1981}, doi = {10.3102/10769986006004377} } +@misc{säilynoja2025, + title = {Recommendations for visual predictive checks in Bayesian workflow}, + author = {Teemu S\"{a}ilynoja and Andrew R. Johnson and Osvaldo A. Martin and Aki Vehtari}, + year = {2025}, + eprint = {2503.01509}, + archiveprefix = {arXiv}, + primaryclass = {stat.ME} +} @inproceedings{salakhutdinov2008bayesian, title = {Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo}, author = {Salakhutdinov, Ruslan and Mnih, Andriy}, @@ -998,14 +1006,6 @@ @misc{szegedy2014going archiveprefix = {arXiv}, primaryclass = {cs.CV} } -@misc{säilynoja2025, - title = {Recommendations for visual predictive checks in Bayesian workflow}, - author = {Teemu S\"{a}ilynoja and Andrew R. Johnson and Osvaldo A. Martin and Aki Vehtari}, - year = {2025}, - eprint = {2503.01509}, - archiveprefix = {arXiv}, - primaryclass = {stat.ME} -} @online{Talebi2022dooperator, author = {Talebi, Shawhin}, title = {Causal Effects via the Do-operator}, From f616291c525b9cffe457f492f75b2266eb0c44f8 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Mon, 1 Jun 2026 21:30:13 +0100 Subject: [PATCH 4/9] updating pymc version and writing Signed-off-by: Nathaniel --- .../assurance_planning_simulation.ipynb | 592 +++++------------ .../assurance_planning_simulation.myst.md | 58 +- .../multiple_experiments_meta_analysis.ipynb | 444 +++++-------- ...multiple_experiments_meta_analysis.myst.md | 58 +- .../sensitivity_unmeasured_confounding.ipynb | 615 +++++++----------- ...sensitivity_unmeasured_confounding.myst.md | 125 ++-- pixi.toml | 6 +- 7 files changed, 710 insertions(+), 1188 deletions(-) diff --git a/examples/causal_inference/assurance_planning_simulation.ipynb b/examples/causal_inference/assurance_planning_simulation.ipynb index f56a8e713..1ab392668 100644 --- a/examples/causal_inference/assurance_planning_simulation.ipynb +++ b/examples/causal_inference/assurance_planning_simulation.ipynb @@ -14,16 +14,18 @@ ":author: Nathaniel Forde\n", ":::\n", "\n", + "Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records — an off-kilter orientation in pursuit of theory, anomalous findings and the next question.\n", + "\n", "## The question planners are actually asking\n", "\n", - "When a team approves an experiment, the question they are silently asking is whether the experiment will be useful. The frequentist quantity called \"power\" answers a different question entirely: given some specific effect size we have not observed, what is the probability the experiment crosses a fixed threshold? The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold.\n", + "When we pursue an experiment, we implicitly assume the experiment will be useful. Our questions are \"operationalised\" as power: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold.\n", "\n", - "Assurance is the Bayesian alternative, and its construction is mechanical: draw an effect from the prior, generate a dataset, fit the model, apply the decision rule, repeat. The result is a single number with a clear meaning: the probability that the experiment is about to conclude what its stakeholders need it to. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the conclusion is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart.\n", + "An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the conclusion is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery answers all three questions; the question being asked of it is what changes.\n", "\n", ":::{admonition} Where this lands in regulatory practice\n", ":class: note\n", "\n", - "The assurance framing here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\\Pr(\\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference, which is exactly the split the generative and inference models below are built on. The setting differs from product experimentation; the machinery is identical.\n", + "The assurance focus here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\\Pr(\\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference, which is exactly the split the generative and inference models below are built on. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical.\n", ":::" ] }, @@ -33,10 +35,10 @@ "id": "1d31774d", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:26.957214Z", - "iopub.status.busy": "2026-05-28T16:43:26.957092Z", - "iopub.status.idle": "2026-05-28T16:43:30.823177Z", - "shell.execute_reply": "2026-05-28T16:43:30.822559Z" + "iopub.execute_input": "2026-06-01T17:15:59.450449Z", + "iopub.status.busy": "2026-06-01T17:15:59.450345Z", + "iopub.status.idle": "2026-06-01T17:16:02.392965Z", + "shell.execute_reply": "2026-06-01T17:16:02.392082Z" } }, "outputs": [], @@ -64,16 +66,15 @@ "id": "7b97cc23", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:30.824907Z", - "iopub.status.busy": "2026-05-28T16:43:30.824712Z", - "iopub.status.idle": "2026-05-28T16:43:30.828472Z", - "shell.execute_reply": "2026-05-28T16:43:30.827952Z" + "iopub.execute_input": "2026-06-01T17:16:02.394961Z", + "iopub.status.busy": "2026-06-01T17:16:02.394718Z", + "iopub.status.idle": "2026-06-01T17:16:02.734659Z", + "shell.execute_reply": "2026-06-01T17:16:02.733931Z" } }, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina'\n", - "az.style.use(\"arviz-darkgrid\")\n", "rng = np.random.default_rng(42)\n", "RANDOM_SEED = 42" ] @@ -85,7 +86,7 @@ "source": [ "### The scenario\n", "\n", - "The running scenario across this notebook and its two successors is a product-experimentation question: an e-commerce team is evaluating a redesigned checkout flow. The continuous outcome is revenue per visitor; the binary outcome is conversion. We will not commit to a particular vertical or product; the structure is generic to any A/B test with one of these outcome types. What matters is that the same prior beliefs and the same decision rule will be asked of two different likelihoods. The Gaussian case will carry the argument; the Bernoulli case will confirm it travels." + "An e-commerce team is evaluating a redesigned checkout flow. We cover two outcomes: the continuous outcome is revenue per visitor, the binary outcome is conversion. We will not commit to a particular vertical or product; the structure is generic to any A/B test with one of these outcome types. What matters is that the same prior beliefs and the same decision rule will be asked of two different likelihoods. The Gaussian case will carry the argument; the Bernoulli case will confirm it travels." ] }, { @@ -94,10 +95,10 @@ "id": "85d2d6a4", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:30.829790Z", - "iopub.status.busy": "2026-05-28T16:43:30.829698Z", - "iopub.status.idle": "2026-05-28T16:43:30.832014Z", - "shell.execute_reply": "2026-05-28T16:43:30.831593Z" + "iopub.execute_input": "2026-06-01T17:16:02.736865Z", + "iopub.status.busy": "2026-06-01T17:16:02.736627Z", + "iopub.status.idle": "2026-06-01T17:16:02.739489Z", + "shell.execute_reply": "2026-06-01T17:16:02.738866Z" } }, "outputs": [], @@ -118,7 +119,7 @@ "id": "541c0d41", "metadata": {}, "source": [ - "## Power and assurance: two questions in the same wardrobe\n", + "## Power and assurance: two questions, different focus.\n", "\n", "Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null:\n", "\n", @@ -134,8 +135,6 @@ "\n", "This is the marginal probability of a successful decision, where \"successful\" is whatever decision rule the team has agreed in advance and $\\pi(\\theta)$ is the team's prior over the effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in.\n", "\n", - "A planning question is not a hypothesis test. When stakeholders ask \"will the experiment work?\", they are not committing to a specific effect size and asking after its detectability; they are asking after the experiment in the world they actually inhabit, with all of its uncertainty intact. The frequentist answer requires them to invent a specific world before the calculation will proceed. The Bayesian answer integrates over the worlds they already hold open.\n", - "\n", "## The simulation engine\n", "\n", "Assurance is an expectation over the *prior predictive distribution*: the joint distribution of effect and data that the model generates before any real data arrive. As a Monte Carlo estimator the integral becomes a procedure in three steps, repeated for each candidate sample size:\n", @@ -144,7 +143,7 @@ "2. **Fit the analysis model** to each synthetic dataset and apply the decision rule.\n", "3. **Record** the fraction of synthetic experiments in which the decision succeeded.\n", "\n", - "The first step is the one worth dwelling on. Drawing a hypothetical truth and then generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive." + "Drawing a truth and generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive." ] }, { @@ -153,10 +152,10 @@ "id": "8f2c40f5", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:30.833154Z", - "iopub.status.busy": "2026-05-28T16:43:30.833072Z", - "iopub.status.idle": "2026-05-28T16:43:30.835317Z", - "shell.execute_reply": "2026-05-28T16:43:30.834629Z" + "iopub.execute_input": "2026-06-01T17:16:02.741148Z", + "iopub.status.busy": "2026-06-01T17:16:02.741022Z", + "iopub.status.idle": "2026-06-01T17:16:02.743743Z", + "shell.execute_reply": "2026-06-01T17:16:02.743193Z" } }, "outputs": [], @@ -188,10 +187,10 @@ "id": "4f485b3b", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:30.837015Z", - "iopub.status.busy": "2026-05-28T16:43:30.836894Z", - "iopub.status.idle": "2026-05-28T16:43:30.840087Z", - "shell.execute_reply": "2026-05-28T16:43:30.839672Z" + "iopub.execute_input": "2026-06-01T17:16:02.744949Z", + "iopub.status.busy": "2026-06-01T17:16:02.744852Z", + "iopub.status.idle": "2026-06-01T17:16:02.748080Z", + "shell.execute_reply": "2026-06-01T17:16:02.747587Z" } }, "outputs": [], @@ -236,10 +235,10 @@ "id": "0a894159", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:30.841402Z", - "iopub.status.busy": "2026-05-28T16:43:30.841299Z", - "iopub.status.idle": "2026-05-28T16:43:32.422867Z", - "shell.execute_reply": "2026-05-28T16:43:32.422377Z" + "iopub.execute_input": "2026-06-01T17:16:02.749699Z", + "iopub.status.busy": "2026-06-01T17:16:02.749608Z", + "iopub.status.idle": "2026-06-01T17:16:05.230742Z", + "shell.execute_reply": "2026-06-01T17:16:05.230108Z" } }, "outputs": [ @@ -247,49 +246,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling: [y_A, y_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [mu_A, delta]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 0 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + "Sampling: [y_A, y_B]\n", + "NUTS[nutpie]: [mu_A, delta]\n" ] } ], "source": [ "demo_true_delta = 0.6\n", "demo_model = pm.do(gaussian_generative_model(N=200, prior=EFFECT_PRIOR), {\"delta\": demo_true_delta})\n", - "demo_draw = pm.sample_prior_predictive(draws=1, model=demo_model, random_seed=RANDOM_SEED)\n", + "demo_draw = pm.sample_prior_predictive(1, model=demo_model, random_seed=RANDOM_SEED)\n", "y_A_demo = demo_draw.prior[\"y_A\"].values.reshape(-1)\n", "y_B_demo = demo_draw.prior[\"y_B\"].values.reshape(-1)\n", "\n", @@ -310,32 +275,45 @@ "id": "0b654f32", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:32.424324Z", - "iopub.status.busy": "2026-05-28T16:43:32.424204Z", - "iopub.status.idle": "2026-05-28T16:43:33.188552Z", - "shell.execute_reply": "2026-05-28T16:43:33.188066Z" + "iopub.execute_input": "2026-06-01T17:16:05.232117Z", + "iopub.status.busy": "2026-06-01T17:16:05.232021Z", + "iopub.status.idle": "2026-06-01T17:16:06.028378Z", + "shell.execute_reply": "2026-06-01T17:16:06.027943Z" } }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "Text(0.5, 1.0, 'Posterior on $\\\\delta$ at $N=200$ (one simulated dataset)')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": { "image/png": { - "height": 361, - "width": 611 + "height": 262, + "width": 515 } }, "output_type": "display_data" } ], "source": [ - "ax = az.plot_posterior(idata_gauss_demo, var_names=[\"delta\"], ref_val=0.0, figsize=(6, 3.5))\n", - "ax.set_title(r\"Posterior on $\\delta$ at $N=200$ (one simulated dataset)\");" + "pc = az.plot_dist(idata_gauss_demo, var_names=[\"delta\"])\n", + "ax = plt.gcf().axes[0]\n", + "ax.axvline(0.0, color=\"C2\", linestyle=\"--\", label=\"ref = 0.0\")\n", + "ax.legend()\n", + "ax.set_title(r\"Posterior on $\\delta$ at $N=200$ (one simulated dataset)\")" ] }, { @@ -362,10 +340,10 @@ "id": "42e775d9", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:33.189849Z", - "iopub.status.busy": "2026-05-28T16:43:33.189765Z", - "iopub.status.idle": "2026-05-28T16:43:33.197403Z", - "shell.execute_reply": "2026-05-28T16:43:33.196916Z" + "iopub.execute_input": "2026-06-01T17:16:06.029977Z", + "iopub.status.busy": "2026-06-01T17:16:06.029758Z", + "iopub.status.idle": "2026-06-01T17:16:06.037195Z", + "shell.execute_reply": "2026-06-01T17:16:06.036727Z" } }, "outputs": [ @@ -398,12 +376,12 @@ " \n", " posterior mean of delta\n", " 0.745\n", - " 0.751\n", + " 0.728\n", " \n", " \n", " posterior sd of delta\n", " 0.312\n", - " 0.312\n", + " 0.303\n", " \n", " \n", "\n", @@ -411,8 +389,8 @@ ], "text/plain": [ " analytical MCMC\n", - "posterior mean of delta 0.745 0.751\n", - "posterior sd of delta 0.312 0.312" + "posterior mean of delta 0.745 0.728\n", + "posterior sd of delta 0.312 0.303" ] }, "execution_count": 8, @@ -455,10 +433,10 @@ "id": "62b0f123", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:33.198644Z", - "iopub.status.busy": "2026-05-28T16:43:33.198556Z", - "iopub.status.idle": "2026-05-28T16:43:33.808133Z", - "shell.execute_reply": "2026-05-28T16:43:33.807546Z" + "iopub.execute_input": "2026-06-01T17:16:06.038338Z", + "iopub.status.busy": "2026-06-01T17:16:06.038265Z", + "iopub.status.idle": "2026-06-01T17:16:08.642660Z", + "shell.execute_reply": "2026-06-01T17:16:08.642201Z" } }, "outputs": [ @@ -466,48 +444,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling: [delta, y_A, y_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [delta, y_A, y_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [delta, y_A, y_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [delta, y_A, y_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [delta, y_A, y_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [delta, y_A, y_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", "Sampling: [delta, y_A, y_B]\n" ] }, @@ -596,7 +538,7 @@ "def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng):\n", " seed = int(rng.integers(2**31 - 1))\n", " pp = pm.sample_prior_predictive(\n", - " draws=n_sims, model=gaussian_generative_model(N, prior), random_seed=seed\n", + " n_sims, model=gaussian_generative_model(N, prior), random_seed=seed\n", " )\n", " y_A = pp.prior[\"y_A\"].values.reshape(n_sims, N)\n", " y_B = pp.prior[\"y_B\"].values.reshape(n_sims, N)\n", @@ -635,24 +577,24 @@ "id": "87ca67e9", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:33.809679Z", - "iopub.status.busy": "2026-05-28T16:43:33.809545Z", - "iopub.status.idle": "2026-05-28T16:43:34.041546Z", - "shell.execute_reply": "2026-05-28T16:43:34.041113Z" + "iopub.execute_input": "2026-06-01T17:16:08.644059Z", + "iopub.status.busy": "2026-06-01T17:16:08.643954Z", + "iopub.status.idle": "2026-06-01T17:16:08.957767Z", + "shell.execute_reply": "2026-06-01T17:16:08.957314Z" } }, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": { "image/png": { - "height": 461, - "width": 711 + "height": 432, + "width": 613 } }, "output_type": "display_data" @@ -723,7 +665,7 @@ "\n", "The story is in the crossing. Between $N=400$ and $N=800$ the assurance curve drops below power-at-the-mean, having sat above it at smaller samples. At small samples assurance is buoyed by the optimistic tail of the prior: it counts the plausible draws above $0.4$, effects of $0.8$ or $1.0$ that even a hundred observations have a real chance of detecting, which the single-point power calculation never sees. The frequentist who quotes power at the mean is being pessimistic about small experiments in a way the prior is not.\n", "\n", - "At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one, because it carries the prior's honest admission that the effect might be zero or negative. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\\delta > 0) \\approx 0.79$. No sample size buys more assurance than the prior's confidence that there is something to find, and that ceiling, rather than any point on a power curve, is the quantity a planner should internalise before paying for a larger experiment.\n", + "At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one: the prior assigns positive probability to a zero or negative effect. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\\delta > 0) \\approx 0.79$. No sample size buys more assurance than $P(\\delta > 0)$. That ceiling, not any point on a power curve, is what a planner should know before committing to more data.\n", "\n", ":::{admonition} The minimum detectable effect leads a double life\n", ":class: note\n", @@ -733,7 +675,7 @@ "\n", "## The same machinery on a binary outcome\n", "\n", - "The conversion-rate version of this experiment is the canonical Bayesian A/B test setup, and the same machinery transfers without conceptual modification. The generative model fixes the baseline conversion rate, places the planning prior on the lift, and emits binomial counts per arm. The inference step places Beta priors on the per-arm rates and reads off the posterior probability that the lift is positive ({cite:p}`stucchio2015bayesian`, {cite:p}`kruschke2014doing`)." + "The generative model fixes the baseline conversion rate, places the planning prior on the lift, and emits binomial counts per arm. The inference step places Beta priors on the per-arm rates and reads off the posterior probability that the lift is positive ({cite:p}`stucchio2015bayesian`, {cite:p}`kruschke2014doing`)." ] }, { @@ -742,10 +684,10 @@ "id": "d1c18825", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:34.042994Z", - "iopub.status.busy": "2026-05-28T16:43:34.042888Z", - "iopub.status.idle": "2026-05-28T16:43:34.047171Z", - "shell.execute_reply": "2026-05-28T16:43:34.046475Z" + "iopub.execute_input": "2026-06-01T17:16:08.959292Z", + "iopub.status.busy": "2026-06-01T17:16:08.959195Z", + "iopub.status.idle": "2026-06-01T17:16:08.963301Z", + "shell.execute_reply": "2026-06-01T17:16:08.962866Z" } }, "outputs": [], @@ -774,15 +716,16 @@ "):\n", " seed = int(rng.integers(2**31 - 1))\n", " pp = pm.sample_prior_predictive(\n", - " draws=n_sims,\n", + " n_sims,\n", " model=bernoulli_generative_model(N, rate_prior, baseline_rate),\n", " random_seed=seed,\n", " )\n", " n_A = pp.prior[\"n_A\"].values.reshape(-1).astype(int)\n", " n_B = pp.prior[\"n_B\"].values.reshape(-1).astype(int)\n", + " actual_sims = len(n_A) # PyMC v6 may return fewer draws than requested\n", " alpha0, beta0 = beta_prior_params(baseline_rate, kappa)\n", - " p_A_post = rng.beta(alpha0 + n_A[:, None], beta0 + N - n_A[:, None], size=(n_sims, n_post))\n", - " p_B_post = rng.beta(alpha0 + n_B[:, None], beta0 + N - n_B[:, None], size=(n_sims, n_post))\n", + " p_A_post = rng.beta(alpha0 + n_A[:, None], beta0 + N - n_A[:, None], size=(actual_sims, n_post))\n", + " p_B_post = rng.beta(alpha0 + n_B[:, None], beta0 + N - n_B[:, None], size=(actual_sims, n_post))\n", " prob_positive = (p_B_post > p_A_post).mean(axis=1)\n", " return float((prob_positive > threshold).mean())" ] @@ -793,10 +736,10 @@ "id": "bc3152a4", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:34.048727Z", - "iopub.status.busy": "2026-05-28T16:43:34.048622Z", - "iopub.status.idle": "2026-05-28T16:43:35.166470Z", - "shell.execute_reply": "2026-05-28T16:43:35.166026Z" + "iopub.execute_input": "2026-06-01T17:16:08.964993Z", + "iopub.status.busy": "2026-06-01T17:16:08.964890Z", + "iopub.status.idle": "2026-06-01T17:16:13.436502Z", + "shell.execute_reply": "2026-06-01T17:16:13.436021Z" } }, "outputs": [ @@ -804,62 +747,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling: [lift, n_A, n_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [lift, n_A, n_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [lift, n_A, n_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [lift, n_A, n_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [lift, n_A, n_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [lift, n_A, n_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "Sampling: [lift, n_A, n_B]\n", + "Sampling: [lift, n_A, n_B]\n", + "Sampling: [lift, n_A, n_B]\n", + "Sampling: [lift, n_A, n_B]\n", + "Sampling: [lift, n_A, n_B]\n", + "Sampling: [lift, n_A, n_B]\n", "Sampling: [lift, n_A, n_B]\n" ] }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 461, - "width": 711 + "height": 432, + "width": 613 } }, "output_type": "display_data" @@ -948,7 +855,7 @@ "id": "45306fae", "metadata": {}, "source": [ - "The picture is the same. Here the generative model produced the synthetic experiments and the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that the conjugate decision matches a full PyMC inference fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably." + "The conversion-rate version runs the same loop, and the loop discloses something the Gaussian case conceals. The lift is bounded above: how large a positive effect can even be depends on how much room sits above the baseline conversion rate, a constraint the Normal likelihood never faces. The Beta prior on the per-arm rates is parameterised here by a concentration $\\kappa$ — the number of pseudo-observations at the baseline rate that the prior is equivalent to. Setting $\\kappa = 200$ is saying: I am as confident about the baseline as if I had watched 200 trials at that rate. This is a different handle on prior calibration than the Normal's mean and standard deviation pair, and it makes the ceiling claim more precise: the assurance curve asymptotes toward the prior probability that the lift is positive, and what that probability is depends not only on the prior's location and scale but on the scale on which the lift is defined. At a 10\\% baseline with a planning prior mean of 1\\%, most of the prior mass sits in a narrow positive strip; the ceiling is lower than the Gaussian case not because the machinery differs but because the planning prior encodes a more cautious belief about how large a conversion lift can plausibly be. The generative model produced the synthetic experiments; the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that this matches a full PyMC fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably." ] }, { @@ -957,10 +864,10 @@ "id": "67f5d44e", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:35.167955Z", - "iopub.status.busy": "2026-05-28T16:43:35.167846Z", - "iopub.status.idle": "2026-05-28T16:43:36.543870Z", - "shell.execute_reply": "2026-05-28T16:43:36.543462Z" + "iopub.execute_input": "2026-06-01T17:16:13.438114Z", + "iopub.status.busy": "2026-06-01T17:16:13.438020Z", + "iopub.status.idle": "2026-06-01T17:16:16.115505Z", + "shell.execute_reply": "2026-06-01T17:16:16.115030Z" } }, "outputs": [ @@ -968,55 +875,31 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling: [n_A, n_B]\n" + "Sampling: [n_A, n_B]\n", + "NUTS[nutpie]: [p_A, p_B]\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [p_A, p_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 0 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" - ] + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Posterior on conversion lift at $N=4000$')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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9wXDMWCheXjgW75Uqbf/Eu8qqPDaNPp7hmK2IGLr91s/NqgvjLfJLgGg9iCsS7ct0u9xo4VGqXx+QbJhWCVSyf5CtwmJVIaGiNZq0b+pDKyCMfbseecAQyb75t29DQysQbNpPaD+KyA9IoStW2YfNsg6UorEVkewby+pmb4yPPvpo2Hl33XVX1IOZSHYwan8buk9sf9iBcGVYI9+KGvFurpq63xN9O5Pp9trBdkWPI7s+265Qb731lqqDBSYjRowoFYxZw/VYK/asqqG8g9xU3e82BfK4444LO68yUyst6AoNamyqZlnBWjK8JlX2+Zpsr/fJ8DhMxtcAVOz444+vMBiLDFetz56FKqGPGavkTHaRQZZ9GRJZ0WSLDYSGB1YFZSv9RWPBrvX4i2flWG1nlaEWFgdZQB/vVhJ2f0R+/g+t1I1VtO0KrQZOl+sDkhHhGBAje9O1gxjrBRH5zX60g9jIRtp2sGuVBrHYZZddSh1c2Oo79s1YRd/yxLqqWE2zKRGhb472rW7kQXB5dtttN6/aIMgqIGzaQaxsxR2bnlXdaup+T/TtTJbb26FDB5111lkx/a5VHoQejFufqOpYtMAO8INT7oxNZbLQpbJT7tJ1v0dWer3zzjveQXAsJkyYEDa2A+mymq4n+jWpKs/XZHu9T/TjMFlfA1B9bJXYUJGfyZJR5BRIq6CM7KdmXwzYlyhBNg0ttEo0suI0sj9iOq9Uaf0GQ4N1+9LApurGm90nkarS2zFaeBTtdS/Vrw9IRkyrBDYd1NiBUrRKMSuH//rrr70PYJEfRuyg65ZbbolaKmwrzYU69dRTK/UmYw2GbbpMsErADoqs11lkFURkL53QFWKSSWgfiODtqyzrHxJaRWLLtNuHoFjYNCj78Fndaup+T/TtTJbba1POYu1HZVVbNq1o7ty5YY1iqzJtoDzWMyyUrcRnIUg8pfJ+tymA1nso+Pv2oXry5MkVrmhoFXuhVUy2feVNqUz0a1JVnq/J9nqf6Mdhsr4GoPpYxZVNowwG5vb5LNnFOgXSAvrQ9w+bWhk6PbSssM16PG3OirnGppn+9NNPqm2sl6FVidnndWOfx22GR3UI/dIrqKxFI8oT7TUyWpVgql8fkIwIx4Aq9lqwF//rr78+6pRK+7AdOrUn2BS6MqzHhpXJh/aHsb42kQcN1u/MDgyCBxf2TaP1ILFl7ZNhdUpjAWPoFAA7GLRG15VlU5nKWxihPDXRX6Qm7/ey1GQflWS4vZV9HNn1hR4YV7QiWGVZJYr1JQxVXt+YqkiH/W6h1p133hlWEVZROGbTq4IHSMb6V5W10lYyvCZV5fmaTK/3yfA4TMbXAFSdTUu2x5X157KqTqtkiZzmZUKr/6zZtwUkZVWIJoNYm+fblGe7bcHXMWuQb6cjq47j3W+sNhszZow3vTvo4osvLtUQPl6ifZFR1krU5Yk2/T7aZaf69QHJiHAMqAL7MG7fTFkPk2giv32zb6urshyyTTsJPWiI9q2efVtolQqh3zY+9thjevHFF72qBlsxzBrfBlc/SwT74BLaJ8dW7LPtq6zIZduDzbBjYZUo1a0m7/dE3s5kur220lllRD4PYp2uF6vISh6rcLApNPGUDvvdAsV777235IO5BSb2JUZ5K8zZKoiRU+iS+TWpKs/XZHq9T4bHYTK+BqByLGC15641zbfHQWjAHQt7HtsqpDZ9PRlZ5WvkCoBlhVlWGWqhfnAxFts3n332WanG9fFeqbK2smD0/vvvLxnvsMMOVaoAjlW0WSJV6TMZrYoq2mWn+vUByYhwDCjvCZKV5R3c2gcWK+W3HjNWKVbRwe4///yzWR/ey+onE3m5QVZSbgflweXsja0CZv1g7Mduhx2E2UGTBXv2rXzk9JzqFNnTxcbxWBWprP0RTU18cK7p+z2amjxASIbba6FGZWRmZpYbbmwuq0iKvG3xrmhIh/1u01C7du2qqVOnlpxn0xcvu+yyMsOu0EowC0DKm96YDK9JVX2+JsvrfTI8DpPxNQCxsx5z99xzT6kKxMqyKrNkDcesH1boY8zeDyzEKYtVX4auVGxTK0PDMQsD7TJDpWs4dvPNN5f0jbSKWnsNj2dvz2i9tOz1I7SasSq9tKKFR9G+0Ej16wOSUfLWIAM1yA4g7NvqyB/7FtN6jU2ZMsVbac6WhY6lCiRymkZV3xQiP/SXddBgyya/9NJLZU5psW9i7ZvG0aNHeyXnBx54oLdql30jWRMqe7ATq8q8adfEG3NN3+/R1OQHkGS4vbH2GqopkeFYdYQS6bLfI/uFWXVJWUFGZCP+Y445ptxvqpPhNamq912yvN4nw+MwGV8DEJthw4Z5FfibG4wle8AZWeW13XbblftcsR5joY9pC8dCWRVa5Mp/6Tit0vZL6L6x1WrtC4HqFvl6VZUFPaJVGJcV8qf69QHJhsoxoAbE68N7eZdjlRa2lLV9o2gHitOmTStVyh9kU5WCHyx69uypm266yauQqy5V6VmQCmrifk8m6XZ7k0Wq7ndbbcwapQc/nFs/t9mzZ+vggw8uNe3DpmTFOqUyFV6TkvH1PlUfh4i/Tz/9VA8++GDYedYzzwJf68NnlVVbbrmld0BtvYoim4IfdthhYZWTyayyUyCtX5b1MgxO0bf+a99++63XbzBavzGrmKtq1WbkFEXrcVZT2rZtW2q6aGWEPn6aNm2qyy+/XDW13V9++WXJePHixZW+DHsvC9WyZcsyA9NUvz4g2RCOAdUgslqkqj1NrI9GeZcbjfVBu/rqq70fW4XTqgWsX8/nn3/ufagK7bMTXLHNvuUZMWJEqekm8RI53cGm+YQuu50qEnm/J0K63d5Y2If0UNXR7Dtd9rtNj7HeY88880zY1MrIcMxCn9BqI+tLVlEFQaq8JiXy9T5dHoeIP1tEIpSFYdY7L9aQJ7JyKplVpXm+te8I7V9pr3HBcCwybItX1VhVFqbaHH379t2scCz0MWAV25uzGFHkiqA2m8SmqMcSHkUuwBOLRYsWhY1tBd2ypPr1AcmGaZVANbDGxKGq+g1n5BtMZXtq2Lc11qT52muv9Q4qZ86c6Z2O/ABqU0cjpyXFU+QS41V5s60NkuV+rynpdnurEo7ZbYv3lJ902u+RUyttintkmDJ+/PhKVY2l6mtSTb/ep9PjEPFj/fEswA2ykNaqIGMNxmzacORrQLKyqs3QXoix9geLXAXdXvfKCtvStd9YonTo0CFsbFV9lRW5qrH1NE7X6wOSDeEYUA0iVyCznhpVOfiKXPmuvCausbApSraSj1UPdOnSJez/Xn/9dVWXyG827WDIpgqkmmS936tLut3eWAS/3Q+tppk7d25cryOd9rv1eAzdp3l5eZo0aVLYVKDQ5tV2oN2nT58KLzcdXpOq+/U+nR6HiB97PQz9wsAWOrI+XLGyyqnQhuHJbN68eaVW+4ul0ste90JX5rXL+fXXX6tciYb4iax2s2n/CxYsqFRfysgFFaxyOV2vD0g2TKsEqoGVEFsFSWhzbiuLHzhwYMyXYQdrkeXz8Wo2ao2qL7roIp1zzjkl51V0AL85K3xtu+223k/ogdNbb71Vqf1RGyT7/R5v6XZ7Y2F9cuxAb+HChWFBhE1/i5d02+9WPRb67bVVPfXr16+kSX/ogbJNubT7oCLp8ppU1df7WKTb4zDVJGrVzhUrVoSNt95660r9/fvvv19rbmvkY9uqO61fYKxT/YYPHx723LLg3yrvqqNyzKYl2kJUtcWll15apem1Nn00cgq9XVZoJazdT2WxfnCtWrUK+zLFel7agl2xeOedd8J6XlpPvYMOOihtrw9INlSOAdWka9euYeNx48ZVqgm0rTQW2i/G+rDssccecdu+1q1bh43Xr19f7u9HNtOsbH+Zo446Kmz89NNP16q+Ialyv8dbut3eWNhKiaFefvnlUgc0myud9rs1kbcP2EFff/21V0lhbPpgedMwy5Mur0lVeb2PVTo9DlPN5r6nV1Vkc/3K9GW0bbTHWG25rZVtxl/R1MrIy6tTp47XIyodWT/K0047rdI/tphDpF69eoX9TmQPsoruG3uPt+m+sXjxxRfDxraacHkrK6fD9QHJhHAMqCZnnHFGqW+rnnzyyZj+1j4ARTYDPfnkk70PQpFifcOKFCzRD2rRokW5vx/5bWfw4DRWgwYNCnuDtFLtK6+8slTD6Nqupu73ZJFutzcWAwYMCAtzrFn8FVdcUeXnarrvdwtMIj+sW/WY9S367bffSs6zb/6jHfik0mtSTb3exyqdHoepZnPf06vKVmMMZYtHxBpW3XzzzaUqz5L5tm7OFEgLiUOfp1Y9G1k1Z1OQq2shJZTtrLPOCgt5rQI2dOGYslhLAFs0JdSQIUOU7tcHJBPCMaCaWI+XyFXVHn744VLfqkSy6S42/SX0IMiWM7c3q2juvfde/fvf//aqKWJl02AiV4uqaKWfyG88bTpTZaoP7MPp0KFDS5VfW6l26LScilj/jtdee81b6agqH5JT5X5PFul2e2Pt9TR48OCw82bMmOE91kNXVSyPhQxjx44t8//Tbb9HNtm314CXXnqp1Lf/OTk5Kf2aVFOv97FKt8dhKtnc9/TNud4GDRqUjK1a01ZJLG+qoz1O7HfseVbV66zp22ohe2TvpcqEY47jhIX9dnmRC2nQbywxbEp+5HuSLSrxwQcflPk333zzjRfuRlbe7r333ml/fUAyoecYUI1uv/12r/TbGhUHP9xcd911eu+997xGyfvss493MGfn2zeZ9sHPeiFENnC1D4Vl9dGxD43WcNl+7A37iCOO0F577eV9aLK+ARkZgQzc+vJYU0178x4xYoT++uuvksuwbx5PP/30cm9Lt27dvMsKfoC1ig2bPmbl59ZjKbRSJnjwFbl8sx2IWrXA22+/XXKefRNql3HSSSfp0EMP9Q62QqdAWHNP2zf2IXPWrFmaNm1a0k99qon7PZmk2+2Nxb/+9S9vOfTQZvHTp0/3KqD69+/vHfTYczT47axNRfvll1+8aigLaD755BPvQ7NN8ShLOu13a+gb2gfl77//LjWlMpZVKiPVttekmnq9r4x0ehymkni8p1dFVlaW99wK7fs0ceJE77ltz8d99923pHrQnuf2/LIpz8EqUWtUb883+79kvq3WdzKyIq6y/cFs+1544YWSceSUZVaqTJyLL75YU6dOLXkc2mIxF154off+bq+vbdq08QLOxYsXe9MS7XU4NJC1qmWrVOb6gORCOAZUI/ug/+ijj3qVCKF9Nezgy37sjdOmDNkHvbKmy9ibrfXciYU1l7bS52D5s12+fUNrB0N2HWX1grnkkktKrbIXyQ68jj/++LBvLpcuXaoxY8ZE/f077rij1IdL25677rrL+9eaXwfZto0cOdL7MfbB2H7K2+ZkVtP3e6Kl2+2NhT3nHnzwQS8ks6AryPbPY4895v0Eq2UsNKhKD5x02u92YGuvP8H9ZkKnP9pBYlWqKGrza1J1vt5XRjo9DlNJPN7Tq+q8887zemj98ccfJefZdCz7see6PV4shLbAIZQ9vu119YILLkj62xrZH8xe6yN7/8XypYAF82WF71SOJY5VHluVrE3PD4Ze9voWfN+wLwTssbxx48ZSf2uv0Xfffbfat2/P9QFJhmmVQDWz6g/7ABbtg5Yd3Nk0q2gHDPaB6NZbb/W+nSqPHXiUxS5/zZo13jf60Q6U7JurG264odTUorJYNUBFjUorUrduXe/D7VVXXRU2tSKUfZgoa5uDbH9GftubTvd7skm32xuLxo0be8GFVc9YtUQ09vwsKxgr77mdjvvdmu2XtU8q04i/Nr8m1eTrfWWk0+MwlcTjPb0qbJXTJ554Qttss02p/7PqLnsMRwZj1n/Lqm+quvJvTd/WaP3GYnlND2UBS+SiF6EBi/UcQ+JYXzgLwiL76BmrjI0WjNnngmHDhnlVv1wfkHwIx4Aa0KlTJ28ajPUbqOiDnX0AtIbe9q2qNSeuyOWXX+4t920H4HbZsTRntW/67dsuq5SozPQaO7iyN3U7CLJGzPbBwL49q0oD5bPPPturKrAqho4dO1b4+/ah0m6fbbf1s5k8eXKpFajS6X5PRul2e2Nh0yavvfZabyl0m0q0xRZblPv79vy159U111zj9TCJRbrsd5v+FK0/i+3jeFQd1YbXpJp8va+sdHkcppJ4vqdXlj3HrJLLHgflrWZnC21YD0d7DbUpzrXltkZWjlU11CsrREn2LwjThT0mrRG9Vc6W9/5uX7zY67a9T3Tv3p3rA5KU4ybzskxAirL+L9ac01ZHs29I7UObvalaibV9gKrst4uhrPze+rrYlBu7fJuaEJxuYwck9k2jHWRuznVUh+XLl3urMdm/1kDaqgxsv9i3bNZjxPZNWVUdtUV13u/JKN1ub2WaNFvPG2vebtPQ7ODMphHZ49yenzb9ZnOw39PnNSmZX+95HCJWViVmPRptVVV7TbTpaPZeYQHaLrvswoqMqBWs1+P333+vn376yXt/t/d7e7/o0KGDdtttt0otGMP1AYlBOAYAAAAAAIC0xbRKAAAAAAAApC3CMQAAAAAAAKQtwjEAAAAAAACkLcIxAAAAAAAApC3CMQAAAAAAAKQtwjEAAAAAAACkLcIxAAAAAAAApC3CMQAAAAAAAKQtwjEAAAAAAACkLcIxAAAAAAAApC3CMQAAAAAAAKQtwjEAAAAAAACkLcIxAAAAAAAApK0sJZmVK1fW6PU5jqMmTZp4p1etWiXXdWv0+gFEx3MTSE48N4HkxHMTSE48N4Hq0bRp07heHpVjAAAAAAAASFuEYwAAAAAAAEhbhGMAAAAAAABIW4RjAAAAAAAASFuEYwAAAAAAAEhbhGMAAAAAAABIW4RjAAAAAAAASFuEYwAAAAAAAEhbhGMAAAAAAABIW4RjAAAAAAAASFuEYwAAAAAAAEhbhGMAAAAAAABIW4RjAAAAAAAASFuEYwAAAAAAAEhbhGMAAAAAAABIW4RjAAAAAAAASFuEYwAAAAAAAEhbhGMAAAAAAABIW4RjAAAAAAAASFuEYwAAAAAAAEhbhGMAAAAAAABIW4RjAAAAAAAASFuEYwAAAAAAAEhbhGMAAAAAAABIW1mJ3gAAAAAkP9d19dff0ty50uIl0l9/u1qzRsrLkzIzpObNpRYtHLXZTmrTRmrZQnIcJ9GbDQAAUCHCMQAAAERVWOjqy6+kadNdzZwtLVtW0Y5yS041aybturOrgw921PVgqV49gjIAAJCcCMcAAAAQViH2zbfSpDcDgdjq1VXbOcuXSx9Mtx9XubnSEYe7Ov00R9ttS0gGAACSC+EYAAAAVFTkaup70vPjXP0yr+wdkpkpbbttYNpk0yZSTq5UWCAtWy4tXiwtWSoVF4f/jU29fONNadJkVz0OdzX0HEdbbUlIBgAAkgPhGAAAQJpXik2bLj35tKuFv0f/nXZtpW5dpQP2d9ShvZSTU3awlZfn6tffpG+/kz78yNXnX/hhmetKU94NTNPsf5qrM/s75V4WAABATSAcAwAASFPzF7i6/8FAgBVp662lnsc66t5NlZoKmZvraKcd5f2ccpKjZctdvfqaq5cnSGvXBn4nP18aMTIQkl1zlbRDJwIyAACQOI5rXxcmkZUrV9bo9dkqSk2aNPFOr1q1yvv2FEDi8dwEkhPPzdSwbp2rESNdvTTeplOG/9/OnaUBZzg6YH8pI8OJ63WOHuvqhRek/ILwaZrnD3V06smsbrk5eG4CyYnnJlA9mjZtGtfLo3IMAAAgTdiXgDat8dHHXa9hfqg220n/utDR/vtWT0hVv76joUMc9T7O1T33ufrk08D5Fs49MszVF19K118jNWhAFRkAAKhZGTV8fQAAAEiApUtdXXyZq5tvCw/G6taVLjzf0bPDHR2wn1MtwVioVq0c3Xu3o/9e7qhOHf/82R9K513o6o/FVPEDAICaRTgGAACQ4qZMdXX2YFdffhV+/lFHSGOfc3TaqY6ys2uuYssCuN49HT39hOM1+w+av0A69zxX331PQAYAAGoO4RgAAECKWrvW1S23FeumW1ytXeef37699OhDjq67JkPNmyduGuP2bRw9+Vig6X/QP6ulS//t6qOPCcgAAEDNIBwDAABIQXPmuBo4xNXbU/zzMjKkswdIw59wtFuX5OjtVaeOo5tvcDTgDP+8jRulK652NW0GARkAAKh+hGMAAAAp1nT/1dddnX+RqyVL/fO32lJ6+AFHQwZlKCsrOYKxIFsV89whGbr8Mut55jfqv+EmVzNnE5ABAIDqRTgGAACQIjZscHXrHa7+d5+rggL//CN6yGu4nyzVYmU5vo+jm25wlJkZGBcWStfd4OrTzwjIAABA9SEcAwAASAELf3c19AJXb7/jn5eTLV1xuaPrr3HUoEFyB2NBh3V3dON1jjI3fUq1kO/q61zN/ZmADAAAVA/CMQAAgFru/Q9cDRnq6tff/PNatZIee9RRr542VbF2BGNBh3Z3dM3V/hTLDRuky//ravESAjIAABB/hGMAAAC1VGGhq4cfLdZ1N7pav94//6ADpeFPOtqhU+0KxUId2cPRRRf6279ipXTVta43dRQAACCeCMcAAABqob//dnXRpa5eeCl8Ncqh5zi641ZHjRrW3mAs6JSTHJ12qj+eN0+663+ut+gAAABAvBCOAQAA1DKff+Fq0Lmuvv3OP69pU+mBex2d2d/xVn9MFecPdXTgAf743akKCwQBAAA2F+EYAABALVFc7GrUaFeXXe5q5Ur//C67SiOecrTnHqkTigVZ0Hfd1Y5at/bPG/a46wWEAAAA8UA4BgAAUAusXuPqymtcPfm0q+Ji//x+p0gP3e+oefPUC8aCGjYMTBWtWzcwttt//Y2uli4lIAMAAJuPcAwAACDJzfnJ1eBzXM3+0D+vXj3ptpsd/euCDGVlpW4wFtR2e0fXXuXfzn9Wy1uIoKCAgAwAAGwewjEAAIAkZY3nX5vo6oJ/uVqy1D+/fTvp6Sccdeua+qFYKLu9Z/b3xz/OkZ5+hnAMAABsHsIxAACAJLRxo6vb73R1z72u8gv8848+SnpimKPttk2vYCxoyCBHe+zuj58fJ332OQEZAACoOsIxAACAJPP7IldDL3Q1+W3/vJxs6b+XO7rmSkd16qRnMGYyMwMN+hs1CoxdV7rldlcrVxGQAQCAqiEcAwAASCLTprsaMtTVvHn+ea22koY94qh3T0eOk77BWFDLlo6u/I+/H5Yvl+682/WmoQIAAFQW4RgAAEASKCx09ciwYl1zvat16/zzD9xfGv6kox13IBQL1fUQR8f39sezZksTXqnBOwwAAKQMwjEAAIAE+2Oxq4sudTXuRf+8jAzp3CGO7rzdphASjEVz0YWO2m7vjx993PWmpAIAAFQG4RgAAECC2DTA199wdfYgV99+55/fpIl03z2OBpzhKCODYKwsubmObrre8fqxmfx86a57XBUXE5ABAIDYEY4BAAAkwPLlrq642tXd/3O1YaN//q67SCOecrT3XoRisWjXztHAs/199dXX0quvV8c9BgAAUhXhGAAAQAKa7g8Y6Gr2h/55mZnSkEGOHn7AUYsWBGOVcdqp0g6d/PFjj7tasoTqMQAAEBvCMQAAgBqydq2rW+8INN3/Z7V//vZtpCeGOTp7gKOsLIKxyrJ9dtUVjhcwGqvEu/teVq8EAACxIRwDAACoAV986eqswa7eejv8/FNOYjXKeOjQ3nq0+eNPP5MmTY7LRQMAgBSXlegNAAAASPXeYo89WToUa9lSuuZKR3vtSaVYvNgCBjZl9dffAuNHHnV1wH5Ss2bsYwAAUDYqxwAAAKpBYaGrF15yddqZpYOxo46URg4nGIu37GxHV19hK3wGxmvXScMep/cYAAAoH+EYAABAnH35lauBQ1w9/Kir9ev985s2lW650dF1V2eoYUOqmarDjjs6OvlEf/z2lMCUVgAAgLIQjgEAAMTJ0qWubrylWBdd6uq3+f75mRnSqSdLY59zdGh3QrHqNuhsR82a+eP7HnBVUEBABgAAoiMcAwAA2EyrVrl66JFibwrlu1PD/2/33aQRTzu66MIMNWhAMFYT6td3dPGF/r6ev0B68eUauWoAAFAL0ZAfAACgitavd73Q5flx4dMnTfPm0r/Od3T4YZLjEIrVtMMOlSZOkj77PDAeMdL17outtuS+AAAA4agcAwAAqKR161yNGu3qpH6unn4mPBjLzZXOOF16fpSjHoc7BGMJYoHk/13iKGvTV8EbN0oPPcLUSgAAUBqVYwAAADFas8bVhFelcS+6WrMm/P+sr9hxx0mDznLUvDnVSclgu+0cnd7PgszAePoM6eNPXO23L/cPAADwEY4BAABUYM4cV6+8HugnlpcX/n82Y7J7N+mcQY4XxiC5DDjD0ZR3XS1ZGhhb9djIZ6SsLO4rAAAQQDgGAAAQxcaNrqa+L73yqqs5P5X+fwvFDusunTXAUbu2BC3Jqk4dR/+6QLrm+sCUygULpQmvSKecnOgtAwAAyYJwDAAAIMTCha5efd3Vm29Ja9eW3jU2ffLQQ6WzznTUdntCsdqg6yHSnntIX3wZGD/zrKsjjpCaNuH+AwAAhGMAAAAqLHQ1c5b0ymuuPv8i+g5p0Vzq3ctRr+NsJUpCldrWnP+Si6SBQ1wVF0tr10lPD3f1n39zPwIAAMIxAACQplw3MF3ynSmupr4nrVgZ/ff23Uc6vrejAw+gT1Vt1r6doz69Xb3yamD8+ht2v7rq2JGADACAdMe0SgAAkFb++MPVO+9K77zr6vffo/9Oo0bSccdIfXo5at2a8CRVDBno6N2pgZVGXVd68BFXDz8QqCwDAADpi3AMAACkvLVrA831J7/l6rvvy/69nTtLx/dxvEb7ubkEJqmmcWNHQwZK9z8UaM7/1dfSB9OkQ7snessAAEAiEY4BAICUVFzs6suvpDcnu/pgupSXF/33WraUjuxhP47atSMQS3V9ekuvTZR+/S0wfuIpVwcfJGVnc98DAJCuCMcAAEBK+X2R61WIvT1F+vPP6L/ToH5gxcmjjnDUZVcpI4NgJF1kZTm68Hzp3/8NVI8t+kN6faJ04gmJ3jIAAJAohGMAAKDWW7MmMG3yrbfLnjaZmSHtt5907NGODtifaZPpzBZZ2GtPlaxMOmKkq6OPkurXJyQFACAdEY4BAIBa64cfXY1/xdX770v5BdF/Z/s20rHHODryCKl5M8IPBBrwX3CeNPjcQPXYqn+kMWNdnTuExwcAAOmIcAwAANS6XmKzZgfCjLKqxBo2lHocHqgS23EHViNEaTt0cnRkj8DKpeaFl6QTjnfVvDkBGQAA6YZwDAAA1AoFBa6mvCs9P87V/AXRp03uv790zFGODjxAyskh5ED5zhns6P1prgoKAgs2DB/h6or/8LgBACDdEI4BAICktn69q4mTpBdedPXX39FXm+zbx9Fxx0hbbEGwgdi1auXoxL6uxr0YGE+aLJ18kqt2bXkcAQCQTgjHAABAUsrLs35i0pjnXf2zuvT/d2gv9T/d0aHdAisQAlUx4AxHb7zpau1am7IrPfW0qztu4/EEAEA6IRwDAABJN33SKsVGPudq+fLS/7/H7lL/0xztty+9xLD5GjVydGZ/6bEnAs35Z8ySfpzjaqcdCcgAAEgXhGMAACBpzJzt6sGHXS1ZUvr/uh4inXG6o847EVogvk7sK734krR8RWD81HBX993D4wwAgHRBOAYAABJuyRJXDz7iauas0v938EHSkEGOOrQnrED1qFPH0YAzpPsfClSPffKp9NXXrnbfjcccAADpICPRGwAAANJXYaGrUaNdnXF26WBs772kJ4Y5uvO2DIIxVLtePaUtt/THVj3muoGwDAAApDbCMQAAkBALFrgaeoGrJ592lZfnn7/11tI9dzp64N4M7dyZyh3UjJwcRwPP8h9vX38TqCADAACpj3AMAADUOFsdcNC5rn6a65+Xky0NPEt6boSjA/YnFEPNO/pIqXVrf0z1GAAA6YFwDAAA1JiNG13dfmex7rw7vFrMVqAcNcLR4IEZys0lGENiZGXZY9B//M35SZoxk3sDAIBURzgGAABqxMLfA9Mo33zLPy8rS7rwfEcP3ueodWtCMSTe4YdK7dr646efcVVURO8xAABSGatVAgCAajd9hqtb73C1fr1/3lZbSrfe5GjHHQnF4m3atGl655139OOPP2r58uXKzc1Vy5Yttf/++6tXr15q06ZN0l/3unXrNHfuXM2ZM0c//fST97Nw4UIVFRV5/z948GCdc845cdlmuw67vOBlm8yc8XIyWunX36Sp70tH9ojLVQEAgCREOAYAAKqNrfb34svSI8Ns5T///AMPkK69ylGjRgRj8WRh1I033qhPPw3vJJ+Xl6fVq1frl19+0QsvvOCFSgMGDEja6549e7b+/e9/18hqkfn5+br55pvDgjHTvr28YMw8M8LVYd0D0y4BAEDqIRwDAADVwqaiPfSIq/Gv+OdlZEjnDHbU/zQ7TdAQT1Zpddlll3nVViYzM1MHH3ywOnbsqA0bNuijjz7SvHnzVFBQoGHDhqm4uFhnn312Ul63/V5kMJadne39rA8tP4yDp556Sr/++qt32Xa9Qf1OcXT7XYHTi/6Q3npb6nlcXK8aAAAkCcIxAAAQdxs2uLrxFlezZvvn1a0r3XKjo/33IxSrDo888khJONWsWTPdd9992mGHHUr+/6KLLtKoUaO8cMo88cQT3lTHHXfcMemuOycnRzvvvLN3GcGf9u3b64477tCbb76pePnuu+/0/PPPe6cHDRrkbVfQ7rtJu3WRvv4mMB4xytWRR9i28fgFACDV0JAfAADE1dq1ri75v/BgrEVzadjDBGPVxXpxvf766yXj66+/PiycCrLpjEceeaR32iqzLNRKxus+4IADNHz4cP33v/9Vnz59vBDNKrviaePGjbrlllu86ZSdOnXSmWeeWep3rMox6M8/pYlvxHUTAABAkqByDACANGGVOmarrbbSq6++6p2ePn263njjDa/qZ+XKldpiiy3UuXNnLyiIrOpZsWKFXnvtNc2cOVN//PGHFy5ss802OvTQQ9W/f3/VrVvXC8Yu+4+rH3/0/65De+meOx21aBEIGgoLC/Xuu+9qxowZXtN2u14LS5o2bapdd91VRxxxhA455JAKb49Nr/vwww/1+eefe83abZvWrl3rVR01btxYO+20k7p27aoePXooy5bFrGBqnYUx5tprr1XPnj31zz//eLf3/fff9y7bemfZ/tlzzz3Vr18/b8pgsnj77bdLembZPtxvv/3K/F2rkLKG+eazzz7T0qVLvcdEbbzuzfH4449rwYIF3mPjuuuui/oY2X03R/vu4+qTTW3URo12ddyxUp06VI8BAJBKCMcAAEhDwaqZqVOnhp2/ZMkS7+e9997TlVde6VXtmFmzZumGG27wwqdQ1kfKfqZMmaK7735Ut965RVgwtvde0u23OKpXzymZxnbTTTfp999/L7VNweu28GSPPfbQ7bff7gVm0dh1WtBigVUkC98sOAvejpEjR+quu+7SdtttF/P++eqrr7zA5O+//y61jZMmTdLkyZO9qqbjjz9eycBWiAw6/PDDy/3d7bff3puiaPsw+Lennnpqrbzuqvryyy/14osveqfPOuuscoNOqx775NNA/7PlK+T10LOeeQAAIHUQjgEAkIYseLJgrFGjRl6VVuvWrb3G6RaCWXBhlVx33nmn2rVr51UFXXHFFV7o1KVLF68CrX79+l61ll2GBVRWgTN4yK3aWHBfyXXsu490x62OcnMDwZg1ZbfLCQZazZs317777utVnzmOo0WLFnlVabayoYUX5513np555hnvuiLZttrl2N916NDBCzesAqlOnTre/82fP18ff/yxF5L99ttvuvDCC72eV2WFbaHs9lvPLPtbu9y9995bTZo08VZjtEo7q3ayhvJ33323d9277LKLEslWW7TbGLTbbrtV+De77757SUA1Z86cWnndVWWPj1tvvdW7Dy2oq2hRgp12dHTIwa5mzAyMnx/rqm8flQS+AACg9iMcAwAgzVi4Yz/dunXzqqMaNGhQ8n8WSFmVlU0ntIDs0Ucf9X43NzfXC4MOOuggLygyq1at8qZTnnvuUK1bt1Zr13ykzJwf5GR0LhWMWcWV9aKyQMtWMrzgggu8iqHIqWy26qEFFzaV0QK3+++/35vmGMkCM9tWm/5oIVs0a9as8W6LTeG0CjBrtm7VcBUZO3asF7JZZZ1N8QxlIdvVV1/thYgWrlh4Z0FaNDZd1W5LPFnAGMn2U3Bao9l2220rvBwLQ4NspcaqSuR1V5X1OrNpsvY4tMdWLL3MhgxyNGNmoHrsn9XSaxOl02q+4A0AAFQTGvIDAJCGrAH5bbfdFhaMmYyMDF166aUl1Vo2vdDCMQvRDjzwwFKX07JlO9Vv6M8xKy56v1QwFuzpZRVh5uKLL/ZCtWg9nux6LZQK9juz6Yt2/ZHatm3rVfyUFYyZhg0belM4g1Pm7LKsaigWV111ValgzFhIaIGK/WuC1WmJZBVtQfXq1St1n0bTsmXLsF5ytfG6q+LTTz/VhAkTvNP2GLS+dLFo385R15A2eGPHucrLC4RlAACg9qNyDACANDR48OAym9RbY/299trLm0IYDNK6d+9e6vfWr3f1f/91tXzFQRZ/eec1aji3VDBmje2DTdht6uPJJ59c7rbZdlnD+xtvvNGrSrIqrRNPPLFKt9Oqg6wh/88//+xVrdkCANZQvzw21e6oo44q8/9taqZdhi0GYNv3yy+/eNNNI9nCBhdddJGqW2g4ZxVvsbD7OLRarzZed2XZdVkgbBWRbdq00ZAhQyr19wPOcDR9RiAQW7FSemOSdOIJ1bSxAACgRhGOAQCQZiwwsl5f5bE+YJGrXIYqKnJ14y3Fgeb7jv+7zbZYERaMGesfZv3KjF2vVadVZIcddig5bYFWeSzssOl9NkXPqtMssLHzgmwlziD7vYrCsWgVcpEsXLFwLLJ6KpT1a7Of6ha6KEEsUwSNregZ7e9r03VX1gMPPOBVIdrjzyohQ7cjFjvu4OiA/V19uGlm65ixrnr1tNtD7zEAAGo7wjEAANJM48aNw6p3orEpckFW7RXp7v+t16zZJb9dcv7GjaWnLVrVVtDrr7/u/VTGypUro55vIdjo0aM1ceLEUqtKlsX6kFUk2u2NFLpIQKxTNatLcIqnKSgoiLmRfrS/r03XXRmzZ8/2HifGet1VdRGFs8509OFHgeD1r7+lt96ReveM66YCAIAEoOcYAABpprIVM5G/P3rMBo1+fmPJuFNHv3LGmtRHsmmVmyNa+PTXX395PcesIX6swVislUqVDWyi3eaaFBpkbtzo3y/lCf29aKuB1obrjpUFonfccUfJYgBDhw6t8mXtsrOjvUIKD58b46qwkN5jAADUdlSOAQCAmM2Y5erOe/w+Uy2aS3ff4ahXr7L/JnQ1Q1vtsqJpjeU1cA+yfmQLFy4MbEOLFjrhhBO0xx57aOutt1ajRo28QC84fbM6Vo2MhU3zDE69jBdrIh+pWbNmYdV0a9eurbAxvoWLQVtssUWVtyeR1x0rW6XUAlTHcbzFFGLtjVZe9djnXwQCsSVLpClTpWPKblEHAABqAcIxAAAQk1/mubrxZlfBdl42M9OCsRYtyu+5ZGFV0Lbbbhs14KkM6yH2xRdflEyBHDlypDdVdHOmUlaHH374QQ8//HBcLzPavrP+Z9ZHLhhC/v777xWuwrho0aKwBQiqKpHXHatgGGd96M4777yY/84C1yCbinnZZZd5p/fYXeqyq/TNt4H/GzXa1ZE9rJcfvccAAKitmFYJAAAqtHqNq6uvcxWcEWdFWTffmKGOIVMqy7L99tuXnP7mm282e29/++2mVEJSr169yg3GzLx585TKrEqubdu2ldrHX331VdTFD2rTdSeKVaBZ9VjQ779LH0xL6CYBAIDNROUYAAAol1WK3XKbq8WL/fOu/E89Hbh/XtiqkGWxFSotULDftZUnrUF/x44dq7zXQyvBQqf1ldXfaubMmUqEnj17ej81oVu3bvrll1+801OnTvUqncpi01GDgaHdL/a3tfW6Y2HTeCtagCLo7bffLjlt2xacgrnjjjuG/d6++0g77Sj9OCcwHvmcq0O7W2hM9RgAALUR4RgAACjXzFnShx/54949c3T6aXX0zz8VN7cPBliHHXaYF5xYQHbXXXdp2LBhMS8MYH9jQUq0aZo2xbI8Tz/9tFatWqVUd/TRR+vZZ5/1pjda9dann36qffbZJ+rvDh8+vCTU3GuvvWJanTNZrzsW/fr1i/l3Q8OxSy65xOthV3b1mHTlNYHb8utvgedJ10PisMEAAKDGMa0SAACUa+YsvzqsQwfp+msbhIVVsTj//PNLGrV/9913uuiii0oa6kdjjd0nTZqkAQMGlFrt0hrvB02cOFEzZswo9fcFBQVeI/bRo0dXeltrI+vl1qdPn5LxTTfd5FXoRXruuedKAiDbLxdeeGGZl2kLGey///4lPzV53bXBQQdKHUJapj37nPXjY+VKAABqIyrHAABAuex43+Ily7ZuvyVDdetWPmxq3bq1brvtNl1xxRXeVMevv/7aq+jp0qWLOnfurCZNmigvL08rV670gpU5c+Z4AVc01uPKprxNmzbN+53//Oc/2n333b3Ladiwof7880/NmjXLW6GwXr166tu3r8aMGZPy97KFTdaPzfbfsmXLNHDgQB1yyCHq0KGDNmzYoI8//jgstBo6dGiFzfMTed13332393gIZdNyg6wS0VYEDdWuXTudc845qgkW8A04U7r+xkAgZkWMH30iHbBfjVw9AACII8IxAABQSnFx+NiKr2641tE2W1e9Cmu//fbzptVZZZFNhywuLvaas4c2aI9WlZSdnV3q/Ouuu04rVqwoac4f7XJsOufNN9+sJUuWKB3Ur19fDzzwgG644QZ99tlnKiws1Pvvv+/9hLL9aQGSVeUl83XPnj1bS5cuLfP/58+f7/2EqukptN27Stu3keYvCIxHjnK1/76B4AwAANQehGMAAKCUr78Jnx529gDpgP03/4C/ffv2GjVqlD766COv8st6VFmlkU2jtB5kTZs2VZs2bbTrrrt6U/msGiwam6L52GOPeVMv33nnHa8qaf369V4/sm222UZdu3ZV7969vZUsbXpgurBA8JFHHtEHH3zg7RertLIQ0fZty5YtvX1q+8X2cSpdd6JYA/4z+0u33B54vnz3vfTFl9JeeyZ6ywAAQGU4bpI1R4gsn69u9s2eTeUIftuYZLsDSFs8N4HE+fY7Vxde7JZUj3XZVXr4AUeZmQ7PTSBCYaGr/gNc/bFpNdc9drfnS8239eV9E0hOPDeB6mFfqMYTDfkBAECJdetc3XybH4zVry9dd3UgGANQWlaWVY/5z48vvwoEzAAAoPYgHAMAACUeeMhVaIuu/7vEUatWBGNAeY46UtpyS388ZizhGAAAtQnhGAAA8Lz3gavJb/s7o8fh0pFHsHOAimRnO+p3ih8iz5wl/fobARkAALUF4RgAANCff7m6+3/+wbxVwfz70kCPMQAV63Wc1KSxP35+HOEYAAC1BeEYAABprrjY1W13uFq7NjC2POzaqxw1bEgwBsSqTh1HJ53oP2emvCstXUpABgBAbUA4BgBAmnvhJemLL/3xGafbinsEY0BlnXC8VLdO4HRRkTTuJcIxAABqA8IxAADS2MLfXT013D+A36GTNOhsgjGgKho1ctS7tz+e+Ia0chUBGQAAyY5wDACANJ5OeefdrvLzA+OcHOn6axyvuTiAqjn1JEdZWYHTeXnS+AmEYwAAJDvCMQAA0tQrr0nffOuPBw901KYNwRiwOVq2dHTUkf54/CvS+vUEZAAAJDPCMQAA0pA1Cn/8yfDplKeenNBNAlLG6afaSq+B02vWSK+/kegtAgAA5SEcAwAgzbiuq7vvdbVhQ2CcmSlddYVNBaNqDIgHq8Dseog/HveiTV+megwAgGRFOAYAQJqZ/Jb0yaf+eMAZUof2BGNAPPU/zX9OLVsmvfMu+xcAgGRFOAYAQBqxlfMeHuZXsLTdXjqzP8EYEG+dd3K0157++PmxroqKqB4DACAZEY4BAJBGhj3mej2QjPVEuvK/jnJyCMeA6nDG6f5za+Hv0sxZ7GcAAJIR4RgAAGniy69cTX7bH59wvLRzZ4IxoLrsvZfUqZM/HvsClWMAACQjwjEAANJAQYGre+/3D8ybbSGdM5hgDKhOjuOofz//efbd99I33xKQAQCQbAjHAABIA+NelOYv8McXXeioQQPCMaC6desqtdrKH48dRzgGAECyIRwDACDFLV7i6tlR/gH5PntLhx+W0E0C0kZWlqNTTvaD6JmzpYULCcgAAEgmhGMAAKQw13X1wIOu8vIC45xs6f8udbzpXgBqxnHHSA0bBp+T0riXCMcAAEgmhGMAAKSw6TOl2R/54zP6O9q2NcEYUJPq1XN0fB9//NZb0sqVBGQAACQLwjEAAFLU+vWuHnzIPwBv3Vrqf1pCNwlIWyf2dZSdHTidXyCNf4VwDACAZEE4BgBAihr+rKu//vbH/77UUW4uVWNAIjRv5uioI/zxhFeljRsJyAAASAaEYwAApKBff3X18sv+uMfh1oifYAxIpH6n+s/B1aulSZMTujkAAGATwjEAAFKwCf+Dj7gqKg6M69eXLrqAYAxItO3bODrwAH/8wkuuioqoHgMAINEIxwAASDHTZ0iff+GPB53tqFkzwjEgGZzez38uLl4ceL4CAIDEIhwDACCF5OW5emSYX4nSZjtrBJ7QTQIQYrcu0k47+uPnX3C9ak8AAJA4hGMAAKSQsS9IS5b640sucpSVRdUYkCwcx9FpIdVjP/4offNtQjcJAIC0RzgGAECK+PMvV6Of9ytQDj5I2ncfgjEg2XQ7RGrVyh+PfYHKMQAAEolwDACAFDHscVcbNwZOZ2fThB9IVpmZjvqd7AfXs2ZLixYRkAEAkCiEYwAApIBvvnU19T1/3O8UaZttqBoDktUxR0sNGgROW8uxl8YTjgEAkCiEYwAA1HLWzDu0CX+zZtKZ/QnGgGRWr56j3j398ZuTpTVrCMgAAEgEwjEAAGq59z6QfvjRHw8d4ngH3gCS24knOMrc9Gl8w0Zp4qREbxEAAOmJcAxAUlfDuIV5gfkmAKLKz3f1+JP+c6RDe+moI9lZQG2wZUtH3bv545cnuCos5D0PAICaRjgGIGkVfzJCRRMuVdZXzyd6U4CkNeFVackSf3zh+Y7X7BtA7XBKSGP+v/6Spk1P6OYAAJCWCMcAJKe8NXIXfOqdzFz8hTcGEG71alfPjvKrTPbfT9pnb4IxoDbZubOjXXb2x+Necr3KaQAAUHMIxwAkp6KC8scANPI5V2vXBnZERoZ0wVCCMaC2V4/9+KP03fcJ3RwAANIO4RgAALXQH3+4Gv+KPz7uWKldO8IxoDbqerC01Zb++IWXqBwDAKAmEY4BAFALPfakNe4OnK5bRxo8kGAMqK2yshyddKL/HJ4+w3oJEpABAFBTCMcAAKhlvv3O1QfT/PHppzlq3oxwDKjNeh4r1a0bOF1cLL38CuEYAAA1hXAMAIBaxBp1PzLMP2hu1kzqd0pCNwlAHDRo4HgBWdAbk6T16wnIAACoCYRjAADUIu9Pk77/wR+fM9hR3bpUjQGpwKZWOpuezuvWSW+8megtAgAgPRCOAQBQS+Tnu3r8Sb+SpH076ZijErpJAOJom60dHXKwP35pvKuiIqrHAACoboRjAADUEq+8Ji1e7I8vPN9RZiZVY0AqOfVk/zm9ZIk0c3ZCNwcAgLRAOAYAQC2werWrZ0f5FST77mM/BGNAqumyq7RDJ3/84ktUjgEAUN0IxwAAqAVGjna1Zk3gdEZGoGoMQOpxHCeseuzrb6Q5PxGQAQBQnQjHAABIcn8sdjV+gj8+9mjrN0Y4BqSqQ7tLzZv7Y6rHAACoXoRjAAAkuSeeclVYGDhdp440ZBDBGJDKsrMdndjXf55PfV9atozqMQAAqgvhGAAASey77129974/Pu1UqyghHANSXZ9eUm5u4HRRkfTaRMIxAACqC+EYAABJynVdPfqYf0DcbAsLxwjGgHTQqJGjI3v449delwoKCMgAAKgOhGMAACSpadOlb7/zxzadsl49wjEgXZx4gv98X7FSev+DhG4OAAApi3AMAIAkZBUijz3hV4m0aysde0xCNwlADevQ3tHuu/njlyZQOQYAQHUgHAMAIAm98pqtUumPLzjPUWYmVWNAujkppHrsxx+lH34kIAMAIN4IxwAASDJr1rh6dpR/ALzP3tJ++yZ0kwAkyMEHSS1b+uPxVI8BABB3hGMAACSZ58a4Wr06cNpxAlVjjp0AkHayshz17eM//6e+Ly1fTvUYAADxRDgGAEASWbrU1cvj/fHRR0odOxCMAems13FSTnbgdGGh9Pobid4iAABSC+EYAABJ5KnhrvILAqdzcqRzBhOMAemuSRNHPXr441dfd71FOwAAQHwQjgEAkCR+muvq7Sn++NSTrdcQ4RiA8Mb8y5dL06azVwAAiBfCMQAAkoDruhr2uF8J0qSx1P80gjEAAZ06Ouqyq783XqYxPwAAcUM4BgBAEvjoE+nzL/zx2Wc5atCAcAyA78SQ6rHvvpfm/MTUSgAA4oFwDACABCsqcvVYSNVY622kPr0SukkAklC3Q6QWzf3xeKrHAACIC8IxAAASbPJb0q+/+ePzznWUnU3VGIBwWVmOju/jvza8+560ciXVYwAAbC7CMQAAEmjDBldPj/APbnfZWerWlbsEQHS9e0rZ2YHTBQXS62+wpwAA2FyEYwAAJNALL0nLlvnjC8935DhUjQGIrmlTRz0O88evvuaqsJDqMQAANgfhGAAACbJihasxY/2DWqsY23UXgjEAsTfm/3uZNG0GewwAgM1BOAYAQII8M9LVhg2B05mZ0tBzCMYAVGzHHRxvCnYQjfkBANg8hGMAACTAwoWuJk70x7Y65XbbEo4BiM1JIdVj33wrzZ3L1EoAAKqKcAwAgAR47ElXRcWB0/XqSQPPJhgDELvu3aRmzfzxyxMIxwAAqCrCMQAAatjX37iaMdMfn3G6o6ZNCMcAxC4ry9Hxvf3XjSlTXa1cuSlxBwAAlUI4BgBADXJdV8Me9ys8WjSXTjmJuwBA5dl07KyswOn8fKsey2M3AgBQBYRjAADUoA+mSd//4I+HDHZUpw5VYwAqb4stHB1+qD8e9+JGFRYyvRIAgMoiHAMAoIYUFLh64in/wLV9O+noI9n9AKruxJDG/EuXFuu9D/LZnQAAVBLhGAAANeS1idKiP/zx+UMdZWZSNQag6jrv5KjzTv74+bEb2Z0AAFQS4RgAADVg7VpXz470q8b22lPab192PYDNd1JI9dinnxXql3lMrQQAoDIIxwAAqAFjxrla9Y8/vuA8R45D1RiAzXdod+s/5o9fHk84BgBAZRCOAQBQzf76y9ULL/rjo46QduhEMAYgPrKzHR3f239NeeddV6tXE5ABABArwjEAAKrZ0yNc5W/qkZ2dLZ0zmGAMQHz16e0oKytwOi9PeuNN9jAAALEiHAMAoBrN+9XV5Lf88UknSFttRTgGIL6aN3N05BE5JeNXXnVVVET1GAAAsSAcAwCgGj32hCt30/Fpw4bSmWcQjAGoHv1Pq1NyeslS6cOP2dMAAMSCcAwAgGry2eeuPgo5OD3rTEeNGhKOAageu3XJUuedMkvGE16hcgwAgFgQjgEAUA2Ki10Ne9w/MG21lXTC8exqANXHVsA9vZ9fPfbJp9LChQRkAABUhHAMAIBq8O5Uae7P/vicIY5ycqgaA1C9jjk6V40a+eMJrxKOAQBQEcIxAADiLC/P1RNP+wekO3SSehzGbgZQ/erUcdTrOD+If/Mtaf16AjIAAMpDOAYAQJyNf0X6809/fOH5jjIyqBoDUDP69nHkbHrJWb9eeusd9jwAAOUhHAMAII5Wr3Y1arRfpXHA/tKeexCMAag5rVo5OuhAhTXmd4PL5gIAgFIIxwAAiKORo12tXbvpTTZDOv9cgjEANe/Evv5rz/wF0hdfci8AAFAWwjEAAOJk8RJXE17xx8ceLbVrRzgGoObtvZe03bb+ePwrVI4BAFAWwjEAAOLkqeGuCgoCp3NzpcEDCcYAJIbjOGHVYzNnSUv/JCADACAawjEAAOJgzhxXU971x/1OkVq0IBwDkDhHHyXVrRs4XVwsvfoa4RgAANFkRT0XAACUy1m/XBmLPlfmn98pY/kvavbjX5p0+Co1yvlH9bI2yFlfR3qygdzGrVXcvKMK2xysoraHSJk51btnC/OUNfdtZf42XZl/fS9n/QqpcKPcuk3lNtxKRdsdqMJOR6u4RaeYLi5j8VfK/nqsMhd/IWft35KToYIm2yij7YFydj5F7hbtK76QogLVHXOyMpf9FNjEtl21se8TSiXOqt+VNfctZc6fqYx/fpezYaWUkSm3brPA/d/2EBXucKyU21ApoahAWb9MUeZvM5S59Fs565dJ+evl5jaUW6+ZirfZU4XtDlVR265WwlTuReXMfkQ5Hz262Zu08ajbVbhzX8VbxuIvlfXzu8pc9LGctX/K2fCPlFNPbr3mKmq1mwrbdVNR+8O9+7tCG1Yq58sxyvxtmjJWLZAK7LnZWMUtd1Zh5z4q7HSU9xyrSPYnTyl35n3eaTe7ntYPeF1q0rrU79Wv7+iYo1xNeDUwnviGNPAsV7m5BPcAAIRy3CRbumblypU1XnLepEkT7/SqVatYyQdIEnZg2WDmPd7pgoJ8bex+tVRvi0RvFlCi7piTlPnn95XaI8UNtlJ+t/+qcIdjqmVPZv78jnI/uFMZa5aU+3uuHBV27q28bldKdQPvgdHkzLxfOZ88WfblZGQpr/tVKtz99HKvL/vjx5U768HA3+TU1/qzJspt2EopoWCjcmbdr+yvxsop3jSntgwWUOYddKkKu5yi2ixz3nvKnXqzMtb+WeHvFm25q/J63KDiLXeudeGY888fyn3/VmX9+kGFv1vUrKPyDr9Oxa33KfN3Mv74QnVev0gZG1aU+TuFbQ7Sxt4PSdn1yt6ulQtU77nj5RRu9Mb2HCzYc0CZn2nnL3B1xln+x/1rrnR0zNGEY0BN4XgTqB5NmzaN6+UxrRIAgBqSsXap6kz6P2V980LcLzv7sxGqO/GSCoMx48hV9g+vqe6LZ8pZ+1fU38n66vlSwZib29irUim5nOJC5b53qzLLCQ+cFb8p56PHSsZ5h/w7dYKxvDWqO2GIcr4YVWEwFgz967x7g3Km3SUl13eTMcv+5EnVfe3CmIIxk/nnt6r74gCvwqw2yVg2V3XHnRZTMGYyl/+suuMHK3Pu21H/31nzp+q+ekFYMGbhcnH95mG/l7VglnLfvqbc68p994aSYMwq1wr2OKPc39++jaO99vTHL09w+TIYAIAIhGMAANQwq+7Sxn/idnmZv7yr3Ol3V/7vlv/iVbKouCj8Pyz0+vCRsLPyuv5X6y78SOsv+kwZ+w0MC9pyZj0U/QpcV3WmXCenKN8bFm2zlwq79FOtUVQgFQa2PZo6b12lzD8+r/TF5nz+rLK/el41pmCj5BZv9sVY8JM78/5K/51TsF513rjUq3iqTsUtO8fngvLWqs4r5ylj3d+V+jOnqEB13vyPMv76odT/ZX82XE6e/5y358K682Zq/dAZWn/ySLmZuf7vzn1LGX/PiXodWd++pKzfP/ZOu5nZ2njkrTFNwzzpBL9S7Ke50g8/VuqmAQCQ8ug5BgBAFRQ33lYFjdvr8cm768vFnbQyv6m2bt9E9/yvjjLyVivjrx+V/f0EZf0S0qV/E6v6yPpthgp36rn5+z5vjXLfvSnq1MmCvQepYPfT5NZp4vWFyn3vFmWs+DXs9zKXfqPsL0Z6vxuUYT2krGdW8LY23V4Fe28KxJwMZRx5nYq/etm7bu8y/v7Rq0BzG7QMu+ysb8aVhEd28L/xyFsq7D+VDLweUz9O9EKKDScOV3HLnUr9TtacScqaN7XU+VYJlHfYdSradn85RXnK+vENb3pqZGVZzox7Vdi+u9xG21TPjXCLlbnwQ+/6rTfYuiHvSXUaVf3yigqUaxVvURR07qOCvQapuNHWXuWiPZ6yvxtfOiB75xptOHV0qb/PP+AC5e9/XkybkTnvfdWdeHGp8wu3P0TFLXZQPOTO+F/UCkyr0srrdoWKm3eSs+5v5Xz6dOnbWVyg3Leu0oYzXwkLrbLmh1fOWQWl6jT2Thdvu6/3WhB6WdYzsLjFjuGXve5v5U7/X8k4f9+hcpt1iOk2HXiAtOWW0p+bCv4mvOJq587J/1wEAKCmEI4BAFAFeT3v18jnXD37vT897qpzHTk5jtyceiqy5vftD1XdF85U5h+flfp7J4bpj7Gw6ZEZ1gw9gvW1yu96ecm4aLv9taHvk6r37HFeaBN2GZ+P9HoWKSPwsSAyGChuHh46ODn15DRrK3fxN2G3JzQcs2lkuTMCDcNN/gEXym3aVsnKpn9m/zhRWXPe8BrqVyT7s2einr+x18Mq3np377Q9MrxQ0SrxNjVPL7m+wg1e9Vh+1/8onjL+/N67DRbeVbbyqTyZiz6JHhi13kd5R99ZMi7O7ai8I2/17v+sBTPDL+OPz5Wx5GsVt9ot/EIsRIqh+ilYdRdN/j5DFBcbVirr+1eihp72/AkGjNY7z7uda/8qFXxlLpvrTSMtatet5DxndcRzKiLIK24RHsBmrF5cahtyp94iJ2+1d7qoeScV7HtuzDcrM9NR3z7S408GXq/e+0C68HxXW2xBQAYAgPfey24AAKDyVq50NWasH4x16yrtuku0A80yekuV03C7MrK+mxD1/Pw9ziy9JY23UWH7Q0udn7HuL69SpeT3nIhV94oLS19WUcR5m4K1oNz3bpaTv9Y7XdRyJ7/yLIk465Yp+4tR3kqa9Z89VjkfPxZTMGZT3jKjTJ0r2qpLSTAWqqDLqXIzskudn20hTOSU1io2jrdFD+o921P1xpzkBUjxDMZM5h9fRj2/oNPRUc/3Vl2MIruMx2usFX22amrU/b7tvooHC0iD04BDFe7UJ2rlXcHu/aNfzncvh58RuZJl5PMnorLQ+pGFyvx5ilcB6P2fk+kFc8os/ZgqT89jpZxNf1JQIL3+RqX+HACAlEblGAAAVTBipKv16wOnszOLNHSwHeAWe2GHs/EfZSz7WVnfvVxmT6qi1ntt/n7PWxO1N5FNo3SbtY/6J8Xb7CXNfavU+ZmLPlNR+8MCf99ku1LVSF5AtumA3d2wSlrxm399ToaKQ6YHZv00WVnz3vNXtLQD+YiD/YTJX+dNdbXphjbt0HHLDqfc3IZhCxAEZf7+adTftz5SUdVppOLmHUsFajZ1NWPFPG+aXpUqnOa+7YU5FhpZ77eyFNdrJmVu3v53olQnmsiptBWdn7G48j3agnI+HV69VWPe8+DTSj1fi7bZ05vCHLn/7fkUqrjJdsoMea5mLvlaRW0PKRlnLPWrMEs9B23q9Hu3lAytyrN4q11VWU2aOOpxuKs3Nz39X3vd1RmnS1lZVI8BAJAkn1QBAKg9Fv7u6rWJ/vil4wZr+/HRD6rLqraJ7CdUFZl/fhc1FClu2qbMvyluEv3/rCdZye+02FHFjVsr459FJats5rx/uwoOuNBbobJo8j1SwYaS3y/aZm+pbpPAYOM/3u8GFew1MH6N0ququFCZ82cFphv+MtWb0lgWC8MK23VX4Q7Hqmj7Q6SsnFK/k/Hnt9Gvprz93rRN1GqzjKXfxR6OFeYp89f3vUDMpu2Vt0KmW6exCjv0CNyObfcrXblUSW5m6f1gylrttKzzMyxUzV8n5dSv9LTXzE2Bayjrh1fU4XDFi90f0RQ32T76H1iAWq9ZqfDQ2bhKzj+L5DZu7Y3tvggNx3Kn3amN9bbwno9ZP7+trJ8DVWHByrDCdn6FZ+60u0sqAYsbb6f8Ay+q8u07oa+jN98KvGb8vUyaMVM6tHuVLw4AgJRBOAYAQCU98ZSrok0FR3XrSlttJWlpbH9rzcvzepRuoF8VkX2Mgtx6zcv8G7d+s+iXtSakx5HjeKtT1pl4SUn4lvP1WO/Hu4zQy8vMVr41F9/EmrYHe6BZcGG9xhLF+ltZY32rZMvYsKLM33Oz6qiobVcV7HCMitp2l7LrlH+5Ze736Pu2vPskWm+p8D8sVubvHwca6//8TslU1ai/mtNAhR0OV2GnY1TU5sBKT7srdzM2hTyRbNGCwt1PL3W+VbVF47jF3nRW69lVGTm22mOUIDjfFpKIsV9ZhWzb1v5Zhfu2mRSlss7u26JN+61gjzO9BTqC97ctjGFTYKMp2KO/3Cbbeqczf/9EWSGN+vOOuEnKrquq2nEHRzt3dvX9ppx2/CuuDu1O5RgAAIRjAABUwrffuZrmt+dS/9McZcfwblq05a7K63GDirfcOW77O9icO1rYUxY3K/qBtbNp5cmgoo5HeAfiue/dVqqBf8ll5TbSxmPuVHGrLt7Ypil6fbQ2rZa58YhbpKzcTRdYoMwFM73pZM76lXKzcuU23laFbQ+R27SMqpwqcFbOV7YFSXMmKmPVwjJ/z0K9ojYHq3CHY1Ro00krE9ZE7KsSZezbwP+VcZ+UcR/aaqdesOc11o9ehRVe6XaMirbvGrXSLR7sOnI+uDPK9MFPlfvWld5qpza11sIf6+MW2Yw/8rFW9iTQKL+/7m9vX0Qqrt8i0AssXvLWlD09tbzANDuG+7ZOI2048WnVef0iZS6fV+ZFWX+6kkUaCvOUO+X6km0q2OUkb2GNoIy/f1LmgllyVi/2Kjrd+s1VvEM3OW0PLvdmntjX0fc/BC7zq6+leb+6at+OgAwAkN4IxwAAiJHruhr2uH/w3Ly51O8USa9X/LeZf36rOq+ep/z9zlfhbqd51VmbLWRqY/iVlfP2Xlbvr/xNDdRCFO56soradvMqVzL/+CIwdczJUFbT1nLaHaQ17Y72ArLgtuROucHftN36qbj13oHNmT/TO8iPttphzgeOCjv3Vt7h12/2IgUWEtQfcUyZ/2/9z4q23T8QiHXoEbXBekzXU9Z+L6+vWhn/5xSU3u8Ziz5TvRdLL6gQ5GbmepVu3u1o132zKoliZT2wCnfq6U3pjLZiqv3ErJzpoNFY2BatSb63wmocw8Ay71cTZUGFCu/biOeUrda64YxXvKq6zN+mKWPlAqlwozcluWjLnVW4U6+wKcg5Hz6ijFULSoLAvG7/DVzu+uXKffsaZf02rdR1Fn34qNS8ozKOvkNFLaMH8TaN8pFh0oqVgfGEV1z959+EYwCA9EY4BgBAjKbPsMoxfzxkoKM6dRxtOGWUf2bBBjlrlipz0SeBVQNXzi/5r4x1y1TnvVtU8Of3yjvqts3f72WFIpEr4cUSTOTUK7OxesH+5yv4V47jqEmTTf3FVq2yxDDw57MfKlnpsbhhq5KplrYKZp1Xz/em00VjVTEWrGT884c2nPTM5k0F3LQtkQq33c/rvVXY8QipbtOqX37wasra71FW9axov0dr+B9tX1kfKmvgblMmbepkZXt2xUPeodcq4++5ylz202ZdjvVDi1n+OmV/82Lpy8hpoIIu/TZrO0pdZnkho91/ZfRds6rIqJcX7TmVme2FjPZTHqsczP782ZKxFx7nNpTy1qruiwO8aZllWvaz6ow7Uxv6jVFxy51K/Xd2tqPevVw9u+ll6+0p0tBzXTVqSEAGAEhfcWrSAABAaissdPXYk3740nZ76Zijo/xidl25W7RVYZdTtf6MCSraqkvpX/l+gjKjrBhZWSVVWxHKazhfVnWMrcy4OU3Ms794rmScd/gNgfCmMD8wLSwk7Mk74F9ae/5srT9jvNeTLCjzj8+ihiDxYFP9Mlb/oYwy+klVWln7KkoVmP9/G8u4rNiq12xVTW/63JrFXs+uhLCpgaeM9BaUqIitmFpUxqIT9n+xyv7mhajTh60yUbkNFFfWXF9lBETlVZVZ9VfUy6taZaKteJv7zrXeVEnvqjsdrSKrdLQQ+qNHw4Kxola7ad3Zb2rd0BkqsIrUkNcAe+6VpU8vR5mbjgI2bpQmb/7LEQAAtRrhGAAAMXj9DWlRYPFGzwXnOcrMrKDSIruu8ve/IPp/fT1us/e722jrqOeXF56U9X9uw+iXVaHiQuVOuc4Lb0zBjr1U1K6bd9qbOhYSSBVts5e34qVVb9n0sbzDrgu7qKzvXtZmycyO2lPNKtpyPnlS9Z7rq3ojjlXO7IflLP+lyldTXNZ+X7+8zL+JXM2wvMuyajI3yjS+zGVzlTvrQdUfcbTqjj5R2Z8+LeefP1Sj6jRWXs/7tb7/y8rfvb8XgFlIa1NWrSLM7uO8rv/RukFvRW1iX1y/pVRvi9iuq6ggLHQNXTnTGtzHnZMht+FWlb9vy3hOlfU4qUj25yNKVja1fZp/6DWB/3CLlf1doKdfSV+/o+/0wnjrN5Z/2LVS423CVrO1CrRoWrRw1LWrP7aplcXFlekEBwBAamFaJQAAFVi3ztUzz/oHjnvuIe2/X2y7rbjpdlHPzyzjoLUyrE+RHSBHNhEP9imKJmPlb9Eva6tdq7QN2Z8OV+bfc7zTxXW3UN6hV5X8X+bSb8N+t7DNQeHXud0BXgjkbJpyaA3GrQl5SRP/SrIpoOvOm6GsX971GrhnLvyoJLQLvf05Hw3zfoqadQz07drhmEotClC85S7Sj6UbzYVOoS31fyui7/firXaJet66odOVNXeyt7hAxuIvSzfC/+sH7yd3xr1edaJ3OzodXWa4E2+2sER+eYtL5K9T5qLPSp1d1HqvmK8ja84bylhbehnYwp16e/d1dd2uaL3x7L4t2qJd6T/Y+E/UlVDduk3LXOGzPM6qhcqxvmGb5HW/0gu+vP9buUBO3j/+dTTeNvxxm5Epp0M3uZ8/75+19NuoUyuDjfnf/yDwuPpjsfTxp9IBMb6uAQCQaqgcAwCgAs+Pc732WqFVY9Z7K6Y32lWBPlylWAi0uXIbqjjK1DXHDtiX/Rz1T6IFFqao9T6VvnpnhQVNj5WM8w+9Oqynl7NhU8fvTUpVEjmO3Lr+FDsLgGzbN0tOfRV27qONJz6t9ed+4IULRRZmRZG5/Gflzn7Ia+Jfd/QJyv7kKTn/hJQHlqFo232jX94fn0f/gw0rlbGi9AqFFiYWN+sQ/W/qNvEWbrC+UesHT1HeQZeoeIv20a936TfKnXaX6j11mOq+cIayvxzjrfCYSDmfPRN1ldPCjkfFdgGuq+zPnil9tpOh/H0GqbqU9TzIXPR55Z5P2wQWo6gsW9TC2TRNs9BWU+18fDnPp9IVeM6mIK2svwm1WxepfUjeN34ClWMAgPRF5RgAAOVYttzVCy/54yN6SF3+/J8K63dXcUWBUsEGr0IpGrfhllHPt2ly9Yf3KH1RnY9X3tF3lDq/cJcTlfn+raXOz/5ytPKOuCn8slcuUOb86VGnulmz98qu3OlNp9wUgNiqiYU7Hldug/NowZezcXXsTdErySpuCvY8y/uxIC/7x9eVNWdSycIBkZV89pM7874KK7GKW+zgVe1l/vl9+GXYNLbFX6p46z3Czs/+emxJ/6hQhTv39abyVXg7Gm+jgv3O834y/vzeq4rL+ulNZUQEYBYuWkBnPzkf3O4FPd7t6HhkXBYisGl9svsrJNCMxvrpZX/8RKnzixtvpyJbFCEGmb99oMwoU1+L2h/urfpYGdbAPnPRp6XOX/t/pas3C3bqpZwZ95ZaHTPrx9eUv9/QUiucZn81Oup1Fux6UqW20buOb19W1u8flUytzTvixvBfiHw+RenFpg2ryv2bsL93HJ3QV7rn3kAo9tHH0u+LXG3bmsb8AID0Q+UYAADlGPGs6zWsNllZ0jmDHWUumK16Lw5QvRHHKGfWA96KjNYs3WvIXrBezsrflPXNi6o3+gRlLvk66uUWbh/S8GczFHTureJ64dUiJvvbF5Uz/Z7AdtkUtwWzVfeVoXKirKxXsNdZUkblvi8r/nRUSdWMrRzoNeGP/J1m4ZVOkfvC+iGFVhd5/ag2Y2GA8lhfpvyDLtH6we9ofb/nlb/bad7Ut4oqscqqwCvYO3r1Up3XL1bm3HekvDVeLyqbdhpaXVeyPdn1VLC730C9UtMZu1+p9ee8rw0nPq2Czn3kRlm50hZByPr9Y9V590bVf6Krt8rhZivYoPpPdlOd1/7lPb4zrC+WBZ6FeV6lWuav05T7xmWq+8ZlpaazmrzuV8QUBpqcT4dHPT9/nyGqVnWbBkLLCBnrl6vuhHO88DPwHJ+v3LeuUtbCQJgVqqj5Dira/uBKXa3tv9zp95SM8w++TG4jv3+YKW6yXVgvOseqUteHT+ks/v2L8HEZ1YZBR/aQGoY85ca/QvUYACA9UTkGAEAZFi509cYkf3zC8dLWrZywPkQ5USpkKmJhkhdIxUNuQ+Udfr3qTrw46tQ2+ymPVUlZZVVluKuXqHjKbSXjvK6XR62wKmrbPaynWOavHyjz5ymB6qH8dcqZdlfY7xd2OFw1wSq78u2n+1XKXDAzUIk17/2S6WxBXp+vKBVf3rbucKwK57yprHlTw87PWL9Mdd+4pMJtyD/4/0qFH5WSkamiNgd5P3kFG73tsB5dmfNnlezvktthtyFkxdDNYRVV3nVF3O6K5O81SEXtD4vpdzOWfB11imph631V3Kr06q/xlnfI5V7gHdl7zELTeuNOL/dv7fGed/TtMYeAQbnv3VpSCVa09R4q2D3K9eTUV1GbA5T1W6D60+7n3PdvD1xfZo6yvhwjLf3O35a6Tb0FEspTt66jXse5en7T+iBvTpbOGeSqfn2qxwAA6YVwDACAMjzxtKuiTZlC/frSgDM2/4DRqnw2HndflZp1l8XCpryu/1Xu9Lsr93fNOmhj74e9oKVSfzfxqsD0uk09mgp3PaXMqaO2qmDO54GAzqqJLMSzXltO/pqwKjZbla9g33NVozKzVdTuUO8nL3+dsn6eoqwfX1fm7x97lVcV2Xj0Har76vll9xorQ75N9YwWflRVdh1vSqs3rXXDSmX/9KayfnxDmUu+UjLI33eoV7UXq5xPn456fkF1V40F5TbQxr6Pq874IaWmrpbHzczWxmPu9lZirYxMW0Di53c2XUaONh5xS5nhWv6BFwcWmtg07TP7p0neAhT2GIictpx34EXe+RXp28fRuBdttUpp/Xpp8tvSSSdU6iYAAFDrMa0SAIAovvve1bSQ9lz9T3PUpMmmcKwKqynaqpKF7Q/X+tNfqnR/r1gU7D1QG3o+qOKGrWLaloKdemvDKaMqvepf5k9vyZ3zVuBysnK18Yibvcb6Zck/5DJv6l8oW90vNBizsGxDn2E1ttJimY38dz5eG096xpuymNftChVZyFHewgu5DbXhhKeVv8eZYdPdyuLWaaKNh9/gTYss93I3R92mKti9vzacNlbrBr3jBSTFlezRFS9FzTtpw/HDlH/wpTHfXpuumDnvvSiXtUO1PG/KUmzb3m+sCtt2jTlotsdCUaejK3dFeWuUO/WWkmH+fufJjZiOHLZdW+6sjcf+z5uWG2RTkyODsfx9z/UWdIhFq1aODg5ZSPblCRaUMb0SAJBeqBwDACBKs/nHnvAPDps1k04J6a+94dQxylj6jddzK+PvH5WxcoGctX/JyVsj2ZS2rDpeY3m3wZYq3qKdilvtrsL23TdvGl0MijodqfXtuilr7lvK/G2aMv/8Qc6GFVLhRi+YcRu28qZlFXY6VsUtOlX+Cjb+o5z3/Ob/BQdcJLfp9uX/TUaW8o6+U4U79lT2ty8pY/FXgRX0snK8Hko29TJ/zzPj0zA+TiwwLNjrbO+nQtl1vFU6rULOmuRnzZ/prXjp3caMTG9FweJmHb2QxavsqqaeatG4TbZVwf4XeD9xkVNf686epMzfP/Ea3Ges+E3OxlWBYKa40JvG59Zv4U3ls55bNuWzsiFgzmcjolbtFewzWDXNFkLY2PcJr8+YVRVm/v6RnLV/B25vdj259ZsFFm+w6kObElzJCkxjfcYy1v1VEgDGUh1nlaLrt9xZ2V8+p8z5M5VhfQVt/9drrsy2Byhjv4EqaNTBW/EzVied4Gj6jMDvL1okffyJdMD+lb45AADUWo5rRwBJZOXKspecrg62Uk+TJoFVl1atWuUdEAFIPDuwbDAz0Jy4oCBfG7tfLUVZth6oDjNnu7ryav/94L+XO+rdkx483nOT900gKW3Oc9N+9+whrubNC4z33Ue67x4mmACJfm4CKFvTpvH9YpV3PQAAQhQVuXr8Sf+Da5vtpGMrOVMKAGrbwfvJJ/hfAHzyqbRgAQfwAID0QTgGAEAIa0Y9f74/HnqOo6wsqsYApLYjekiNGvnj8a8QjgEA0gfhGAAAm+TluRr+jH9AuOsu0iEHs3sApL7cXJs+7o8nvyWtXUtABgBID4RjAABs8tJ46e9l/u4471zHm24EAOng+D6OMjcdHWzYKL05OdFbBABAzSAcAwBA0urVrkaP8askDj5I2q0LwRiA9LHVlo4OOcQfv/yK6/VhBAAg1RGOAQAgadRoV2vXbXpzzAj0GgOAdHPyif5r3+LF0ocfJ3RzAACoEYRjAIC0t3Spq/Gv+LvBVqdsuz3hGID002VXqWMHf/zyeCrHAACpj3AMAJD2nh7hqqAgsBtycqTBAwnGAKQn67MYWj322efSb/MJyAAAqY1wDACQ1n6Z5+rtd/zxKSdJLVoQjgFIX4cfJjVp7I/HTyAcAwCkNsIxAEBae/xJV+6m475GjaT+pxGMAUhvubmOevfyx2+9I61eQ0AGAEhdhGMAgLT1xZeuPgppNj3gDEcNGxKOAUDfPo4yNx0pbNwoTXqTfQIASF2EYwCAtOS6rh57wq+E2GpL6YTjE7pJAJA0bHp5927+eMIrroqKqB4DAKQmwjEAQFp6f5r04xx/PGSwo5wcqsYAIOikkMb8S5ZKs2azbwAAqYlwDACQdgoLXT35lF8B0aG9dGSPhG4SACSdXXaWdtzBH79MY34AQIoiHAMApJ3X35AW/eGPzzvXUUYGVWMAEMpxnLDqsS++lOb9ytRKAEDqIRwDAKSV9etdjRjpH9zttae0374J3SQASFqHdZeaNvXHVI8BAFIR4RgAIK2Me1FaudIfn3+u41VHAABKs16Mx/f2x2+/I/3zD9VjAIDUQjgGAEgbK1a4GjvOP6g7/FBpxx0JxgCgPH16O8rMDJzOz5dem8j+AgCkFsIxAEDaeHaUqw0bA6ftQO+cwQRjAFCR5s0c9TjMH4+f4Co/n+oxAEDqIBwDAKSFRYvcsGoHmybUujXhGADEot8p/uvl8hXSlKnsNwBA6iAcAwCkheHPuioqCpyuW1c6ewDBGADEqmNHx1vAJOiFF125LtVjAIDUQDgGAEh5v8xz9W5IlUO/U2z1NcIxAKiMfqf6r5u//iZ98in7DwCQGgjHAAAp76nhVuEQON24Ufj0IABAbPbfV9p+e3887kUqxwAAqYFwDACQ0r773tWs2f74jP6O6tcnHAOAynIcR/1O9l8/P/1M+vkXAjIAQO1HOAYASFnWD+eJp/wDt+bNpROOT+gmAUCtdkQPm5buj194iXAMAFD7EY4BAFLWZ59LX37lj60Jf24uVWMAUFX2GnpiX/911Po5LltGQAYAqN0IxwAAaVE1ts3WUs9jE7pJAJASju9tIVngdGGh9PIEwjEAQO1GOAYASEnTZ0pzfvLHgwc5ysqiagwANleTJo6OOdofv/q6tH49ARkAoPYiHAMApJyiItdboTKofTupx2EJ3SQASCmnnuzI2fR9w9q10qTJid4iAACqjnAMAJBy3nlXmj/fH58z2FFGBlVjABAv27Z2dPCB/vjFl13viwkAAGojwjEAQEopKHD1zAj/AG3nztJBIQdwAID46Heq/6XDkiXS9BnsWQBA7UQ4BgBIKRMnSUuW+uOh59jUH6rGACDeuuwq7bSTPx73IpVjAIDaiXAMAJAyNmxwNXKUf3C2917SnnsQjAFAdbAvHvqd4r/Gfv+D9O13BGQAgNqHcAwAkDLGvyItX+GPzx1CMAYA1anbIdJWW/rjsS8QjgEAah/CMQBASlizxtWYsf5BWddDpM47EY4BQHXKynJ0ykn+a+2MmdKiRQRkAIDahXAMAJASrNfNmjWB09ZibMgggjEAqAk9j5Ma1A+cdt3AypUAANQmhGMAgFpv1SpXL77sj488QmrXlnAMAGpCvXqOevf2x2++Ja1eTUAGAKg9CMcAALWe9bjZsCFwOjNDGnQWwRgA1KST+jrKzAyc3rhRevV19j8AoPYgHAMA1GorV7peI/6gY4+RttmGcAwAalLLlo56HOaPx09wlZ9P9RgAoHYgHAMA1GrWhN+qFExWljTgDIIxAEiEfqf4r7+2cvCUqdwPAIDagXAMAFBrLVvuasKr/rjnsVKrVoRjAJAIHTs62mtPfzx2nKviYqrHAADJj3AMAFBrjX7epu0ETmdnS2dSNQYACXV6P/8LivkLpBmzEro5AADEhHAMAFAr/fWXq9dDGj737ilt2ZKqMQBIpH33kTp19Mejx7hyXarHAADJjXAMAFArPWdVYwWB0zlWNdafYAwAEs1xHJ0R8nr84xzp8y8SukkAAFSIcAwAUOss/dPVxDf88fF9pObNCccAIBl0O0Tadlt//NwYKscAAMmNcAwAUOuMGu2qsDBwOjdXOuN0gjEASBaZmY76h/Qes8qxH34kIAMAJC/CMQBArfLHYleT3vTHJ/aVttiCcAwAkslRR0otmvtjqscAAMmMcAwAUKuMfM5VUVHgdN060mkh1QkAgOSQne2o36n+6/OMmdJv86keAwAkJ8IxAECt8fsiV2+/7Y9POlFq2oRwDACSUa/jpEaN/PGYsYRjAIDkRDgGAKg1nh3lqqg4cLpePanfKQRjAJCs6tVzdNIJ/uv0lHelpUsJyAAAyYdwDABQKyxY4HoHVkGnnCQ1bkw4BgDJ7KQTAlPgjU2JH/sC4RgAIPkQjgEAaoURo1wVb6oaa1BfOuVkgjEASHaNGjnq3dsfT5wkrVxJQAYASC6EYwCApPfrr66mvuePTz3FUaOGhGMAUBv0O9lRVlbgdH6+9OJ4wjEAQHIhHAMAJL1nRrpyNx1LNWwYmFIJAKgdWrRwdMzR/njCK9LatQRkAIDkQTgGAEhqP//i6oNp/vj0fo7q16dqDABqk/79HGVsOvJYt0565bVEbxEAAD7CMQBAUnvmWb+6oElj6cS+Cd0cAEAVtG7t6NBu/vjFl13l5VE9BgBIDoRjAICkNfdnVzNm+uPTT3NUrx5VYwBQG51xuv/6vXKlNGlyQjcHAIAShGMAgKQ1YmRI1VgTqW+fhG4OAGAzdOzoaP/9/PHzY10VFlI9BgBIPMIxAEDS9hoLqxrr56huXarGAKA2O7O//zq+9E/prXcSujkAAHgIxwAASWnkqPBeY1SNAUDtt1sXR7vv5o9HjaZ6DACQeIRjAICkM+9XVx9M98f9TqVqDABSxcCz/OqxxYulKVMTujkAABCOAQCSz7MhVWONG0knHJ/QzQEAxNGee0hddvXHI59zVVRE7zEAQOJQOQYASCq//ubqg2nhVWOsUAkAqcNxnLDqsUWLpKnvJ3STAABpjnAMAJB0vcbcTQUEjRpJJ/ZN9BYBAOJt772knTuHv/ZTPQYASBTCMQBA0vhtvqv3PvDHp55M1RgApGr12Nkh1WMLFiqsahgAgJpEOAYASBrWdyZYNdawoXTSCYneIgBAddl/X2nHHcL7TRYX03sMAFDzCMcAAElhwQJXU98LrxqrX9+vKgAApHbvsd/mS9NnJHSTAABpinAMAJAURo32q8YaNKBqDADSwYEHSJ06+uMRVI8BABKAcAwAkHALf3c1Zao/PuUkRw0aUDUGAGnRe2yA/3o/b540c1ZCNwkAkIYIxwAASVE1VlwcOF2/vnTyiYneIgBATTn4IKl9+/DeY26wlBgAgBpAOAYASKhFi1xNmeKPLRhr2JCqMQBIFxkZjgaGVI/N/Vma/WFCNwkAkGYIxwAACa8aK9pUNVavXmBKJQAgvXQ9RGq7vT+megwAUJMIxwAACfPHYldvv+OPTzpBatSIcAwA0rF6LLT32I9zpI8+SegmAQDSCOEYACBhngupGqtbVzr1ZIIxAEhX3btJbbbzxyOepfcYAKBmEI4BABJiyRJXk98Orxpr3JhwDADSVWamo7PO9N8HfvhR+uTThG4SACBNEI4BABJi1BhXRUWB03XrUDUGAJAOP0zadlt/TwwfQfUYAKD6EY4BAGrc0qWuJr/lj0/oKzVpQtUYAKQ7qx4LXbnSqsc+/CihmwQASAOEYwCAGvfcGFeFhYHTdepI/U4lGAMA+NVj27fx98bTVI8BAKoZ4RgAoEYt/dPVpMn++ITjpaZUjQEAQqvHzva/NJk7V5o5i90DAKg+hGMAgBo1OqJq7DSqxgAAEQ7tJrVrG957rLjYZT8BAKoF4RgAoMb8+ZerN970x337SE2bMqUSABAuI8PR4IH++8Mv86TpM9hLAIDqQTgGAKgxo5/3q8Zyc6kaAwCU7ZCDpY4dwqvHioqoHgMAxB/hGACgRvxlVWOT/PHxfaQttqBqDABQTvXYIP994rf50vsfsLcAAPFHOAYAqLGqsYKCwOmcHOl0eo0BACpw0AHSjjv442eepXoMABB/hGMAgGr399+uJk4K7zXWrBlVYwCA8jlOePXYwt+ld6ey1wAA8UU4BgCodmPGRlSN9SMYAwDEZv99pZ07++MRI61/Jb3HAADxQzgGAKhWy5a5en2iP+7Ti6oxAEDlqseGhFSPLfpDemcKexAAED+EYwCAajV6rKv8kKqx/qdTNQYAqJy995J26+KPR4yiegwAED+EYwCAarNseXjVWO9eUnN6jQEAqtJ7bKD/5cqSJdKbb7EbAQDxQTgGAKg2Y553lZ8fOJ2TLZ1xGlVjAICq2XMPR3vu4Y9HPmfvMfQeAwBsPsIxAEC1VY29FlI11qun1Lw54RgAoOpCq8f+/FOaNJm9CQDYfIRjAIBqMXacXzWWbVVj9BoDAGym3bo42ncffzzqOVd5eVSPAQA2D+EYACDuli939cpr/rjXcVKLFlSNAQA236Cz/feTv5dJEyexVwEAm4dwDAAQd8+/QNUYAKB67LKzowP298fPjXa1cSPVYwCAqiMcAwDE1YoVrl4NqRrreZzUsiVVYwCA+BkcUj22fIXCqpUBAKgswjEAQFw9P876vwROZ2XRawwAEH877ujokIPCV0dev57qMQBA1RCOAQDiZuXK8F5jPY+VtqRqDABQDQaFrFy56h/pxZfZzQCAqiEcAwDEzdgXIqrG+jOdEgBQPTp2cHT4oeHvQatXUz0GAKg8wjEAQNyqxia86o+PO0baakvCMQBA9Rk8yFHmpiOadeukMWMJxwAAlUc4BgCIi7Ev2mphftXYmVSNAQCq2XbbOjr6aH/88gRp+XICMgBA5RCOAQA228pVrl55xR8fc7S01VZUjQEAqt/AAY6yswOnbWr/c2MIxwAAlUM4BgDYbONecLVhU9VYZqY0gKoxAEANsS9j+vTyx6++Li1dSkAGAIgd4RgAYLOsWuVqQkjV2LFHS61aUTUGAKg5NpW/Tp3A6cJCacQowjEAQOwIxwAAm+WFl8Krxs48g2AMAFCzmjVzdNIJ/njyW9LChQRkAIDYEI4BAKrsn39cr/lx0NFHSVtTNQYASIDT+zlqUD9wurhYGj6CcAwAEBvCMQBAlY2zqrENgdOZGdIAqsYAAAnSqJGjfqf61ctT35d+/pmADABQMcIxAECVe429PN4fH3WUtM3WTKkEACTOKSdJTRr746eeIRwDAFSMcAwAUCVjxoZXjZ1F1RgAIMHq1XPCel/O/lD67nsCMgBA+QjHAACVtmyZq/GhK1QeI22zDVVjAIDEO7631KK5P37yacIxAED5CMcAAJU2crSr/PzA6exs6awBBGMAgOSQm+vo7LP896UvvpQ++5yADABQNsIxAEClLFniauIb/rhPb2mrLQnHAADJ4ziraN7aHz/xtCvXJSADAERHOAYAqJQRI10VFgZO16kjDehPMAYASC5ZWY4GD/Tfn378UZo1O6GbBABIYoRjAICYLVjg6q13/PFJJ0hbbEE4BgBIPocfJrXd3h8/NdxVcTHVYwCA0gjHAAAxG/6sHVgETtevL53ej2AMAJCcMjMdnTPYf5+a96s09f2EbhIAIEkRjgEAYvLzL67eCzmoOO1UR40aEY4BAJLXIQdLO+3oj4c/Y60BqB4DAIQjHAMAxOTpZ/yDiSaNpVNOYscBAJKb44RXjy36Q5r8VkI3CQCQhAjHAAAV+u57N6yRcf/THdWrR9UYACD57bO3tPtu4QvL5OVRPQYA8BGOAQAqZE2Mg5o3l044np0GAKg91WPnDvG/0Pnrb+m1iQndJABAkiEcAwCU64svXX3+hT8+60xHublUjQEAao8uuzo6YH9/PGq0q/XrqR4DAAQQjgEAyuS6rp582j94aLWV1PNYdhgAoPYJ7T22apX08oSEbg4AIIkQjgEAyvThR9ZvzB8POttRdjZVYwCA2qdTR0eHdvfHz49ztXoN1WMAAMIxAEAZiovdsF5jbbaTjjyC3QUAqL2GDHSUsak8YO1aaewLhGMAAMIxAEAZPpgu/fyLPx48yFFmJlVjAIDaq00bR0cf6Y9ffllasYKADADSHdMqAQClFBW5Gv6Mf7DQsYPUvSs7CgBQ+w08y1FWVuD0ho3Sc2MIxwAg3RGOAQBKeWeKtGBheBPjjAyqxgAAtV+rVo569/THr74uLf2TgAwA0hnhGAAgTEGBq2dG+gcJu+wsHbA/OwkAkDoGnOkoNzdwuqBAGjmKcAwA0hnhGAAgzMRJ0pIl/vjcIY4ch6oxAEDqaN7M0Yl9/fGbk6XfFxGQAUC6IhwDAJRYv97ViJCqsb32lPbcg2AMAJB6+p/mqF69wOmiYmn4CMIxAEhXhGMAgBIvviytXOmPzz+XYAwAkJoaN3bU7xT/fW7qe9K8XwnIACAdEY4BADwrV7kaM9Y/KDjsUGnHHQnHAACp69STpcaNAqddV3p6OOEYAKQjwjEAgGfkc642bAiczswMrFAJAEAqq1/fUf/T/fe7GbOk738gIAOAdEM4BgDQ4iWuXn3N3xG9ekrbtiYcAwCkPmvM37y5P36K6jEASDuEYwAAbxpJYWFgR9SpIw0cQDAGAEgPubmOzjrTf9/77HPpiy+pHgOAdEI4BgBp7uefXb3zbnj/lWbNCMcAAOmj57FSq1b++MmnXbnWhAwAkBYIxwAgzQ17wv/wb02JT+9HMAYASC/Z2Y4Gne2//333vTT7w4RuEgCgBhGOAUAa+/gTV59+5o9tWok1JwYAIN0c2UPavk1477HiYqrHACAdEI4BQJoqKnI17HH/Q/82W0t9j0/oJgEAkDCZmY6GDPK/IPplnvT+B9whAJAOCMcAIE299Y4071d/PPRcx5tWAgBAuurWVerUyR8/PcIWrKF6DABSHeEYAKShDRvcsKXqd+4sHdotoZsEAEDCOY6jcwf7XxT9/nvgyyQAQGojHAOANPTCS9KyZf74wvMd74AAAIB0t9++Updd/fGIka7y86keA4BURjgGAGlmxQpXY8a6YVNIuuxKMAYAgLEvi4ae478v/vmnNPEN9g0ApDLCMQBIM88862rDhsDpzEzpvHMJxgAACLVbF0f77uOPRz5n751UjwFAqiIcA4A08ss8V6+HfPt9fG9p29aEYwAARDp3iP/+uGKlNP4V9hEApCrCMQBIE67r6qFHXBUXB8YNGkgDzyIYAwAgmh13cLzWA0HWkmDNGqrHACAVZSV6AwAANWP6TOmLL/3xoLMdNWlCOJZONm7cqEmTJmn27NmaO3eu/vnnH+Xk5Kh58+bac889dcwxx2jXXUO6UFfR9OnT9d///jfsvI8++qjCv/vqq6/0/PPP69tvv9WaNWvUtGlTb7sGDBig9u3bx3TdDz/8sMaMGaMmTZpo7Nix3mXEy1NPPaXhw4d7p/fYYw899thjMf/tzTffrDfffNM7feyxx+r6668v9TuLFy/WCSecEPXvMzIyVLduXdWvX18NGzbU9ttvr06dOmmXXXbxtsX+Pxaff/65LrzwwkrdL0A6GzzQ0fQZrlxXWrPGFrRxNWQQ750AkGoIxwAgDeTluXr0Mf/b7jbbSSccn9BNQg37+OOPddNNN2nFihVh5+fn52vt2rWaP3++JkyYoF69eunyyy9Xbm5ula7HArc777yz0n/34osv6v777/cqHIP+/vtvvf3223r//fe9cKl79+7lXsacOXM0btw47/Sll14a12As0YqLi7Vu3Trv56+//tK8efM0depU7/+22mor737r37+/6tSpk+hNBVJKu7aOjjzC1dvv+Ks9n3iCq6Z8uQQAKYVwDADSwIsvW1WKP77oQkdZWXzznS6skuvKK6/0AhZTr149de3aVdttt52Kioq8KjKrJrPTEydO9AK0u+66S1lZlf+YcM8993h/n52drYKCgpj+5ocfftADDzzgBWN2nUceeaS23npr/fzzz5o2bZoX4Fmw17lzZ7Vs2TLqZRQWFuq2227zbsMBBxygo48+WrXdWWedpUaNGpWM8/LyvCBz2bJl3j5btGiRd/7SpUu9qrbJkyfruuuu02677ZbArQZSj1VavzvVVVGRvAVtRj/v6qILeA8FgFRCOAYAKW7ZclejRvvVOAfsL+2/Hx/q08XKlSt14403lgRje++9t2699VZv2mEoqxyzijELXGbNmqUXXnjBq0SqjPfee0/vvvuud3rQoEF64oknYvo7q/ay7bOpgY888oh23333kv974403vO3dsGGDxo8fr/PPPz/qZYwePdoL0yz4syAwFfTp08cLCcti99XLL7/s/Vg4aGOrmHv00Ue9IBFAfGyztaNex7l69fXA+JVXpFNPctWyJe+lAJAqaMgPACnuyadt+fnA6cxM8W13mrHpiuvXr/dOt2jRwqsIiwzGjPWwsqqvTHuQSHrmmWe8KZKxsmqxu+++2zt90EEH6aijjor5b7/8MtAMzyq+QoMx07NnT7Vt27akJ1k0CxYs8LbXXHDBBdpyyy2VDlq3bu2FYRZCNmvWzDvPQsR///vfWr16daI3D0gpZ53pKCcncDq/QBr5HI35ASCVEI4BQAqbM8fVm5P98UknSNttxzfd6cSqwIJ69+7tNXQvi4VQ+++/v3faeltZJVisLHRbtWqV1yy+spVbVt0WDOjK2i4T2S/N2FTM22+/3Zt62aVLF5144olKNzvvvLO3D4LTYG1/2sIGAOKnRQsnrFfnG29Kf/xBQAYAqYJwDABSlIUGDz7if3Bv0lg6ewDBWLqxFRCDdthhhwp/f6eddio5HWs4Zr2urDeYueSSS7wKtcoINv+3FSqjCVZBRWs2b1Mtv/76a2/VzauvvlqOk56PceszFlqtZxWDsfZ8AxCbM053VLdu4LT1H3vmWcIxAEgVhGMAkKKmvid9+50/tqXnGzZMz+AgnVkT96BYVqAM/Z2ffvqpwt+35vC2yqSxqjObBllZwYqxTz75JGx7zfLly73m86G/F2SrNg4bNsw7PXDgwDIrz9LFySefXHLaptJ+//33Cd0eINU0aeKo3yn++J13pV9/IyADgFRAOAYAKWj9elfDHvc/sLdvJ/U8LqGbhAQJ7S/2559/Vvj7ob9jFVsWQJXnjjvu8H5vcxrh9+jRo2TVRav+mjdvntc7y8Kd//znPyU90w4//PBSUznt/zp06KAzzzxT6a5Tp05h02aDvdwAxM+pJ9sXTYHTris9/QzhGACkAlarBIAU9OwoV3/97Y8v/pejrCyqxtKRTZP8++/Ag8GmPlrfsbLYioczZ84sVRnWsmXLqL//+uuvl/Q0u+iii7TVVltVaRv79u2rSZMm6ZdffvEuL7RPWpBVpXXr1q1k/Pbbb3u/ZwsIXHPNNSX9tmqKhYZjxoyJ+fd/++03VTdb7dOCQptmGgwbAcRXgwaO+p8mPf5kIBSbPkP6cY6rnXbkPRYAajPCMQBIMTbF44WX/PHhh0p77cmH9nR1xBFHaPr06d5pC5OmTp1aqgIryFY9jAxUglVbkZYsWaIHHnjAO7333nvr+ONDOlVXkvUSe/jhh3XrrbeWCsYs8DnmmGO8CrJgPzFr/B+87n79+pX0Sdu4caNeeOEFTZkyxeu1Zn3IdtxxR5122mnab7/9FE9//PGHt83JplGjRiWnK7PaKIDYndhXeullafmmNUKeGu7qvnt4nwWA2oxwDABSrAn/fQ+4XqNgY42D/3UBH9jTmU1ZtObs3377rTe+/vrrvdPHHXec2rRpo6KiIs2dO1djx47VBx984AVQVoUVbOZugVO0x5lNf7QVLevWrRuXRvhNmzbVvffeqwULFnjbZ5dtU0J33313bbnllmG/az3ObEXG1q1b65xzzvHOs6mdVr0W2ifNgr2PPvrI+znvvPN09tlnK9XZ9NYg24cA4q9uXUcDzpDufyhQPfbJp9KXX7naY3febwGgtiIcA4AU8s4U6avAjKqSJvy2/DzSl4VWd955py6++GKvl5eFYePGjfN+orn00kv15JNPloRjoT2sgixImz17tnf6/PPP19Zbbx237bXAzn7KYtdrUyrtdl111VUlK1hasGbBmFWLWZWZhYIWmD366KN65513vKq4XXbZxatyi4c99thDjz32WMy/f/PNN+vNN99UdQut9GvQoEG1Xx+Qrnr1lMa+IC3d1KbxyaddDXs48JoLAKh9aMgPAClizRpXjz4W3oTfpn4AzZo10/DhwzVo0KCwaXehtttuO68iy3qShVYcNQx2ng6ZTnjPPfd4p62qK3SFxJoIfu6++27vtG3nXnvt5Z22KZQ2ldJYY/5evXp5FW1WcXbDDTdom2228ardnn32WaW60KmUjRs3Tui2AKksJ8fRwLP9IMxWh/7ok4RuEgBgM1A5BgApYvgIVytW+uP/u5Qm/PBZhdW5557rBWRz5szRwoULtWbNGi/8ateundeby3zzzTclf2PN7i1YCnXfffd5IZVdnjXCr8kqiWHDhnk90Zo3b+5NoQyyaZPFxcXe6T59+oT9jd2Gnj17epVjtnqjTRMNVpulGtsHVh0YVNUFEgDE5qgjpDHPSwt/D4yfetrVfvtYr0SqxwCgtiEcA4AU8NNcVxNe9cfHHi3t1oUP5yjN+onZ9EL7iSbYm8y0b9/eq8AK9eefgTlEFjJVpmrMVpsMnbppjfQrw1ZgHD9+vHfapk2GThm0oM9Y6BVtZc1tt93W+9emlP7+++/q2LGjUpH1jgut+ttzzz0Tuj1AqrNVoAcPkm64KVC1Pfdnadp06dDuid4yAEBlMa0SAGq54mJX997valPhjCwzOH8owRiqxvpzBR1wwAFJsRvz8/N1xx13eFMjbaXNbt26hf1/MBAKbUYfKrRvWlmrb6aCl17yl6m18LBz584J3R4gHRzaTerYwR8//YwtiuO3OAAA1A5UjgFALTfpTemHH/3x0HMcNW1KOIbK+/TTT0tWe7TpiH37lm5ad9BBB5VMwbSm/RZYRWOVZdOmTSsZH3XUUSWnt99++0pt14gRIzR//nyvX9r//d//lRl+rVq1SoWFhV51XKi///675HRZAVptZxV/tlBB0KmnnlpqPwCIP5tCec5g6b9XBV4LFywMLI5zzNHsbQCoTfjUBAC12MpVrh5/0g8ndtxB6t0zoZuEWmrlypW66667SsbW8D5azypbnbJJkyYlYVRZ4Zg1yQ8Nx2666aYqbdfPP/+s5557zjt9ySWXeIsLlDVt0npuWV+xffbZJ+z/P//885LAr3Xr1ko11kPOVu60YNDYPjrttNMSvVlA2jhgf2nXXQJN+c0zz7rqcbiUnc0XVQBQWzCtEgBqsUeGufpndeC09UW//DJHmZl8GEe4jz/+WJ988klJ0/pIP/74oy644AItWrTIG1uAFNrwPlGsR5hNp7TQZ99999Vxxx1XZj+z4MIAzzzzTElIZKxB/XvvvVeyumZkD7XazFYOffjhh3XOOedo2bJlJZVx9957b1hPNgDVy15/zhnsv/cuWSpNnMReB4DahMoxAKilPv3M1dt+eyj17SPtuCPBGEqzqZK20uMWW2yhPfbYQ9ttt53XvN6qxWw63g8//FBSAbb11lvroYceSorph+PGjfO2zQKtK6+8sszfsxU1e/TooSlTpniVYwMHDlT37t21evVqTZo0yZv+ac466yzVJq+99po3lTS099ratWu9IMwCTVtcILRyzyrobrjhhpJprwBqzp57ONp7L1efBQpVNfI511scp04d3pcBoDYgHAOAWmjjRlf33OcfFLdoHug1BpRnxYoVmjp1apn/b83uL7vsMjVv3jwpqqKeeuop7/R5553nhXblufzyy72+ZDYNM/gTyqqrrPqsNhk5cmRMv2fTX20a7Omnn+6FngAS49whjj77PPDevHy5vFWkT6/cwrwAgAQhHAOAWujZUa4WL/bHl17iqH59wjFEZ9MRrU/YF198oV9++cULyayqqmHDhmrRooXXo8uCsWRa3dCmU1pT/5133lknn3xyhb/fuHFjPfnkk3r++ef17rvvej3PsrOzvSoq679lCwnU9mlbVkFniw9YNZktatCxY0d16dLFqwYMTisFkDidd3J0yEGuZswKjEc/76pPL1s0hOcnACQ7xy2rk26C2BSPmmQfJmNpLAygZjkbVqrBzHu80wUF+drY/Wqp3hbcDZJ+medq8Dmuija1jzrkYOmOW2khiRp6bvK+CSQlnpvJYd6vrs4e7Cp4SDHwLGnwQN6j0xnPTaB6NG3aNK6Xxys1ANQiRUWu7vqfH4xZW6jLLuYbaQAAkkH7do63UmXQCy/ZF/B8+Q4AyY5wDABqkZfG28qC/njoEEctWxKOAQCQLAaf7Shz01HW+vXSmLGEYwCQ7AjHAKCWWLDA1ZNP+x+wO+8kHd8noZsEAAAitG7t6Nhj/fH4V6S//yYgA4BkRjgGALVAYaGr2+50lZ8fGOdkS1df6Sgzk6oxAACSzdkDHO+92th79/ARhGMAkMwIxwCgFhj3ovRDyHTKIYMdbd+GYAwAgGS0ZUtHJ57gj998S/ptPgEZACQrwjEASHK//uaGfeO86y7SqScndJMAAEAFzuzvqEH9wOniYumJpwjHACBZEY4BQLJPp7zDVUFBYJybK111BdMpAQBIdo0aOTqjv1/lPXOW9PU3BGQAkIwIxwAgiY0ZK/001x8PPcfRdtsynRIAgNrg5BOlFs398WNPuHJdAjIASDaEYwCQpH6Z52rESP8D9G5dpJNC+pcAAIDklpvraPAg/0ut776XZsxM6CYBAKIgHAOAJJ5OWVgYGNepI119haOMDKrGAACoTY45Smq7vT+23mP2Pg8ASB6EYwCQhJ4d5ernX/zx+UMdbbMNwRgAALVNZqajoef67+ELFgZWrwQAJA/CMQBIMt9862rUaH+85x5S3z6J3CIAALA5Djog0B4hyFah3rCB6jEASBaEYwCQRNatc3XLba635LuxJeCvvpLplAAA1GaO43hV4EHLl0svjU/oJgEAQhCOAUASuf8hV0uW+uP/u8zRVlsynRIAgNpul50ddevqj8eMdbVqFdVjAJAMCMcAIElMfd/VW2/74yN6SEf2IBgDACBVDB3iKHPTEdi6ddKo0YRjAJAMCMcAIAn89Zer/93nf0Deckvp/y4hGAMAIJVst52jnsf54wmvSouXEJABQKIRjgFAgtly7jff5mrNmsDYcaTrrnbUsCHhGAAAqWbg2Y7q1AmcLiyUnh5OOAYAiUY4BgAJ9syzrr762h/3P03afTeCMQAAUlHzZo5OPdkfv/OuNPdnAjIASCTCMQBIoE8/c/XcGH+8c2dpyCCCMQAAUtnp/Rw1aeyPHxnmynUJyAAgUQjHACBBli1zddOt9mE4MG7YULrpekdZWYRjAACksvr1HQ08y3+//+JLaeashG4SAKQ1wjEASICiokAwtmqVf941VzraaiuCMQAA0kGf3tJ22/rjRx93VVBA9RgAJALhGAAkwIiRrr78yh9b75GDDyIYAwAgXVil+IXn++/9ixZJr7ya0E0CgLRFOAYANWzGTFfPjvLHO+0knXcuwRgAAOnmwAOkvffyxyNGuVq9muoxAKhphGMAUIMWLHB1y+3+h95gn7HsbMIxAADSjeM4+tcFjjI2HZWtWSM9M5JwDABqGuEYANSQtWtdXXWtq/XrN70AZwSCsa1bEYwBAJCuOrR3dNyx/timVi5cSEAGADWJcAwAakBxcaBibOHv/nlDz3G07z4EYwAApLshAx3VrRs4XVQUaM4PAKg5hGMAUAOsx9is2f74sEOl0/ux6wEAgNSsmaMBZ/hfmNlnhs8+JyADgJpCOAYA1WzmLFfPPOt/wG3fTrrqv47XZwQAAMCccpK05Zb+vnh4mKuiIgIyAKgJhGMAUM0N+G++LbwB/+232NQJgjEAAODLzXXCVq+eN096czJ7CABqAuEYAFST1WvCG/BbodiN1znaZhuCMQAAUFqPw6SdO/vjJ552vc8TAIDqRTgGANWgoMDVNdeFN+A/d4ij/fYlGAMAANFZy4WL/+V/Vli1SnpmBOEYAFQ3wjEAiDPXdXXPva6+/Mo/7/BDpTNOZ1cDAIDy7dzZ0bHH+OMJr0q/zCMgA4DqRDgGAHH23Bjpzbf88S47S1dfSQN+AAAQm/POcdSgfuB0cbH0wEOu9+UbAKB6EI4BQBxNfc/Vk0/7H15btZLuuM3xmuwCAADEYostHA0e5H92+Opr6d332HcAUF0IxwAgTr762tVtd/jBmH3je8+djpo2IRgDAACV07eP1K6tP370MVvkh+oxAKgOhGMAEAc//+Lqiqtd5RcExpmZ0q03O9q+DcEYAACovKwsR5dd4n+OWLZMevY5wjEAqA6EYwCwmf5Y7Ory/7pat84/74rLHe29F8EYAACouj12d9TjcH/8wovSvF8JyAAg3gjHAGAzrFjh6t//cbV8hX/eBefZKlMEYwAAYPNdeJ6jevUCp4uK5K2IXVxMQAYA8UQ4BgBVtG6dq8uvcLXoD/+8006VTu9HMAYAAOKjRQtH5w7xP1t89730+hvsXQCIJ8IxAKiC/HxXV1/nau7P/nlHHyWdP5RgDAAAxL85/447+OPHn3S1fDnVYwAQL4RjAFBJRUWubr7N1edf+OcduL905X8cZWQQjgEAgPjKzHT0n3/b54zAeO1a6eFhhGMAEC+EYwBQCdbj4867XX0wzT9v112km290vFWlAAAAqsMOnRydfKI/fneq9PEnBGQAEA+EYwBQiWDMmuBOfts/r+320l23O6pTh2AMAABUr8EDHbVs6Y//d7+rjRsJyABgcxGOAUAMXNfVfQ+6mjjJP6/1NtL9/3PUqBHBGAAAqH716jm67GL/c8eSJdLI5wjHAGBzEY4BQAzB2IOPuHr1Nf+8Vq2kB+931Lw5wRgAAKg5hxzs6JCD/fHz46RffyMgA/6/vfuA16l+4Dj+Pde49sgIIRkZDaMiKbPMhrSXyihpoiFJ0yghIyMRokUiTaM0/lFRChWlZG9C1h2//+t3Hs+43L2e9Xm/Xk/3nPOc55xzdX/POed7fgPICsIxAEijKeXLo4xmvedfduqp0qjhjk4tSzAGAABy30MPOCpY0DOdkCC32wc7YBAAIHMIxwAgBfHxRoNeMHrvff+yMqU9wVj58gRjAAAgOOwDuu5d/dciK1dJ783m/wYAZBbhGAAk49gxowHPGH0a0Pl+2TKeppSnnUYwBgAAguuaq6Xatfzz4ycabdhI7TEAyAzCMQA4weHDRo/1M/rq66Sd748d7ahyJYIxAAAQfHnyOOrX11H+fJ75Y8ekQUNoXgkAmUE4BgABDhww6v2I0Q/L/MuqniG9MspRuXIEYwAAIHScUcVRt4DmlatWS+/MDOohAUBYIhwDgOP27jV6oJdx++3wql1bGjPSUalSBGMAACD03HCddFYd//xrk4zW/0PzSgDICMIxAJC0aZNRj3uN/vjT/89xXgNp5DBHxYoRjAEAgNBtXvmEbV6Z3zN/LE4aONi4AwsBANKHcAxA1Pv1N6Me9xlt3uL/p2hykfTiYEeFChGMAQCA0Fa5sqO7u/uvWX77XZoyjXAMANKLcAxAVPvft56mlPv2+Ze1aS0NfNZRbCzBGAAACA/XdpLqnuufnzZd+vkXAjIASA/CMQBRa85co8f7Gx054l/W+Vap/+OO8uYlGAMAAOHVvPLJfo6KFPbMJyZKzw007mBDAIDUEY4BiDpxcUYvjUjUSyOMe+FoxcRID/d2dFe3GDkOwRgAAAg/dmRtez3jtW27NOxlI2MIyAAgNYRjAKJuRMpeDxvNmetfFhsrDXreUccrCcUAAEB4u7SVo7Zt/PMLF0mfLQjmEQFA6CMcAxA1/vjDqFsPoxU/+5eVLSO9MsrRxRcRjAEAgMjQ6wFHFSr454eNMNq4idpjAJASwjEAUWHRF54RKbdv9y8752zptQmOatUkGAMAAJGjcGFHA55wlOf43d7hw1L/p2w/qwRkAJAcwjEAES0hwWjCxEQ99YzR0aP+5Vd0kEYOd3TKKQRjAAAg8px9lqOuXfzXOevWScPpfwwAkkU4BiBi/fuv0SN9jd6Y4V+WJ4/U+yFHjz7sKH9+gjEAABC5br1ZuuhC//zHn0offhTMIwKA0EQ4BiAi/b7GqOtdRt//4F9WvJg04iVHnTo6jEgJAAAiXkyMo/79HJUv5182YqTRmrU0rwSAQIRjACKKHar8w4+Met5n3OHLvc4809O/WIP61BYDAADRo1gxR8897ShfPs/8sThP/2P7DxCQAYAX4RiAiPHff0bPDjQaMtS4F35eHdpL40Y7Kl+eYAwAAESfWrUcPXi//zpo61a5/bHGxxOQAYBFOAYgIvz6m9Gd3Y0WLPQvs09Ibd9ijz8ao9hYgjEAABC9rrpCatPaP//DMmnMWMIxALAIxwCEtcREoxlvGd1zn9GWLf7l5ctLY0c7uvJyQjEAAADHcfRoH0e1avr/LWbNluZ8QEAGAIRjAMLWrt1GvR8xGjfBKCHBv/zSVtLrEx3VrkUwBgAA4GVr0g8Z6Kh06aQd9P/4EwEZgOhGOAYgLC1ZanRHV6Nly/3LChaQ+j3m6Kn+jooUIRgDAAA4UenSjoY87yh/fs+8fcBoO+jftImADED0IhwDEFaOHTMa/UqiHulrtG+ff/mZNaRJrzpq385xmw0AAAAg5Q76n+jrv17av1/q/ajR7t0EZACiE+EYgLCxYaNRj3uN3pmZdPkN10njX3FUuTKhGAAAQHq0aunojs7+edt3a59HjQ4cICADEH0IxwCEPGOkRYuMunY3WvuHf3mJEtLQIY7uvzdG+fMTjAEAAGRE1zsdtW/rn/9zndT3CaOjRwnIAEQXwjEAIS0+3mjVaunl0UaHj/iXn3+eNGWSo8YXEooBAABkegTLhx1d3MS/7OdfpKeeNe41GABEC8IxACFr375Eff2/OG3b5r84y5NHuuduR8OHOipdimAMAAAgK/LmdfTMAEf16vqXffM/adAQOxo4ARmA6EA4BiDkJCYazXovUUu+i9PhQ/6LsgoVpHFjHN1yk6OYGIIxAACA7BAb62jIQEfVq/mXzV9IQAYgehCOAQgpu3Yb9X7EaMo0I5PoX970Eun1iY7q1CYUAwAAyG5Fijga9qKjSpX8yz5bQEAGIDoQjgEIGUuWGt3RxWjZciVpRnnWWY4e6e2ocGGCMQAAgJxSqpSj0SMcVazoX0ZABiAaEI4BCLpjx4xGjUnUI32N9v3rX16suO0gNr8qlHd7jA3mIQIAAESF0qUdjXn55IDsuUFGcXH0QQYgMhGOAQiqDRuM7u5p9O6spMs7XuXoogvzU1sMAAAgCAHZiTXIFi6SHutndCigP1gAiBSEYwCCwhijjz4x6nKX0R9/+peXKCENfcFRty4xiuEbCgAAICjKlPEEZIF9kH3/g/RAL6O9ewnIAEQWbj0B5LqDB42eed5o8AtGR474l19wvjR1kqPGjWhCCQAAEAoB2djRjmrX8i/7fY10z31Gm7cQkAGIHIRjAHLV6l+N7uxu3Kr5gZ3u9+zhGSHJdgQLAACA0FCyhKORwx01auhftmmz3G4xfllJQAYgMhCOAcgViYlGb8ww6nm/0dat/uWnVZDGjXF0842OYmIIxgAAAEJNoUKOXhjkqG0b/7J9+6QHext98ikBGYDwlzfYBwAg8u3abfTcQKPlPyZd3vpSqU8vh073AQAAQlzevI6e6CuVKW0feHqWxcVJA4cY/b3e6O7ujvLk4UEngPBEOAYgRy1ZajRwsNG+f/3LChaQ+vR21LY1F1AAAADhwnEcNwSrXNnoxZeMG45Zb74trV9v1P8JqVhRru8AhB+aVQLIEfHxRmPHJ+qRvkmDsTPPlCa/RjAGAAAQrtq1cTRqhKOSJf3Lvl0qdb3LaM1amlkCCD+EYwCy3c6dxh3m2z5FDHTj9dKEVxxVqsgTRQAAgHB2ztmOJo53VL2af5ntV/aee43mzjMyhpAMQPggHAOQrX5Y5hmN8peV/mUliksvveDovp4xypePYAwAACASlDvVcQdWatPav+xYnDR0mNHzg40OHSIgAxAeCMcAZIuEBKPJU4x6P2Lc0Yu8zjlbmjzR0YWNCMUAAAAiTcGCjvo/7uiRPo7y5fMv/2y+dEc3o5WrCMgAhD7CMQBZtnef0cOPecKxwBr0N90gjX7ZUdmyBGMAAACR3FH/VVd4apGVL+dfvmWLdO8DRq++luj2RwsAoYpwDECW/PyLUZduRj8s8y8rUlga/Lyje++JcYf9BgAAQOSrVdPRpImOWrXwL0tMlKZNl+7uabTuLwIyAKGJcAxApthOVt+dafTAQ0Y7dyUdjdJeFF1yMaEYAABAtClW1NHTAxwN6O+4D0y91qyVunQ3mjgpUceOEZIBCC2EYwAy7OhRo+cHGY16xSgh0b+841XSuNGOTqtAMAYAABDNzSxbX+poymRH9ev5lyckSFPfkO7sZgdvIiADEDoIxwBkyLbtRvfcb/TZAv+yggXkPh18uFeMYmMJxgAAAOAZzXLkcEcPPeC414te/2zw9EU2/OVERrQEEBIIxwCk248/GXW7y2jtWv+y0ypIE8Z6ng4CAAAASW44Yxxd28nRG1McNWroX24HcZo9R7r1dqOvv7GDOlGTDEDwEI4BSJO9WJn5nlGvPkb7/vUvb3iBNHGCo6pVCcYAAACQsnLlHL30gqMn+zkqXsy/fMdO6fH+Ro/0Ndq0iYAMQHAQjgFIs3+xgUOMRo5O2r/YrTdLQ4c4bqerAAAAQHr6ImvT2tH0aY4ubZX0vaXfSbfdafTqa4k6coSQDEDuIhwDkKLtO4x63m/06Wf+ZQUKSM8+7ajHXTHKk4dgDAAAABlTsoSjp5+McWuSVTzNvzwuTpo2Xbr5NqP5C2lqCSD3EI4BSJYdQajb3cYddturQgVp/CuOWjYnFAMAAEDWXNjI0dTJjrp3dRQbm7Sp5bPPG/W41+jX36hFBiDnEY4BOMmHHxk90Mto796k/Yu9Nt5R9WoEYwAAAMgedqTz229zNGOqo2ZNk763+lfprnuMnhuUqJ07CckA5BzCMQA+8fFGL49K1JChRvHx/uU333i8f7FiBGMAAADImQ77Bz4bo1Ej7MPYpO99Nl+66TajKdOM2x8uAGQ3wjEArv37jfo8ajRrtv8fJH8+uSMK9exB/2IAAADIeQ3qO5r0qqNHH3ZUooR/+ZEj0muTjRuSLVxEf2QAshfhGAD99bdR9x5Gy3/0/2OULi2NGeUZUQgAAADILXbQpysvd/T2dMdtwZA3r/+9HTukp5/zDBr12+/UIgOQPQjHgCj3zbeezk43b/Evq13b079YndoEYwAAAAiOIkU8LRimT3F0ycVJ31u5Su7D3YGDE7VrFyEZgKwhHAOilDFGb8wwevwJo0OH/MvbtJbGvOyodGmCMQAAAARfxYqOBj8fo5HDHVWrmvS9Tz6TbrrVaOob9EcGIPMIx4AodOSIcaujT5ho+2vwLIuJkXr2cNT/cTuUNsEYAAAAQst5DRxNnujo4d6OShT3Lz98RJo4yeiWzkaLvqA/MgAZF9B6G8gZv/32mz766CMtW7ZMO3fuVGJiokqXLq1zzz1X7du313nnnRc2+46Pj9eCBQu0cOFCrVu3Tnv27FHhwoVVrlw5NWnSRFdeeaXKli2brm39+++/+v3337VmzRrfa/PmzW6NLqt///66/PLL031s9nMbN250t+Pd7tq1a7V//37fOkuXLtX2HUb9+hutWev/bOHC0tMDHDVuRCgGAAAQabLzGjZY+7bXur/++qt7Xb9y5Urli/lbit+j+Pg42wBTcipp86ZzNeCpDppdr4oeuE+qeWbq17YdO3bUtm3b0v27nH/++RozZky61wcQPhzjvRMPEXv37s3V/TmOHQXFMwzKvn37fMEEsi4hIUEjR47UzJkzU/13bd26tfr166cCBQqE9L43bNigJ5980g2dUmJP9H369HGDt9TMnj1bL774YqrrZDQc69q1q1avXp3qOuMnLFH/p4z27fMvq1RJGvK8o9NPD61gzDm8V0W+GepOx8Ud05Hm/aRCpwT7sICox3kTCE2UTeTGNWyw9v3VV19p6NCh7sPutDly8lypPPkeVPt2BXR3N0elSjlBC8com0DOKFmyZLZuj5pjyDFDhgzRvHnzfPPnnHOOGjRooDx58rghzvfff+8GV/Pnz9fBgwfdsChv4FA0IbTvHTt26P7779f27dvd+djYWDVv3lynn366WzPr66+/dmt9/ffff3ruuefck2C7du1S3N6xY8dOWma3aR09ejRTv3Ny2yxevLhbQ83rwd5GCQn+9xte4KkxVqxoaAVjAAAAyLrsvoYN1r7Xr1+fJBirUKGC2xLE1j6zD7ntPr78con27LFBl5FJmKv4xA366OMR+mJxfnW+Vbr+WnsMKV/z2mNNi90fgMhEOIYcYatOe8MpG0jZJ0Zt27ZNso4NqB577DEdPnxY3377rd59913dfPPNIbnvQYMG+U7s9oQ+YsQI96QceDIdPny43nvvPTd0e+GFF9wmmylVES9YsKDq1aunmjVr+l5VqlTRfffdp59++ilTv/epp57qHpN3e7Vq1XKDtk6dOvnWCQzG7LDYd3VzlDcvwRgAAEAkyu5r2GDuu1ChQm4TzA4dOqhGjRonvf/wwwmaPPl1TZr0mmeB+Ukm4Q0dPtzV7Wf3gw+le3tIzZp6anOd6JZbbsny7wwgfNEhP7Kd7ddr3Lhxvvlbb731pHDKatiwoR588EHf/Ouvv+4+OQq1fS9fvtztq8uytcsGDx6c5MTuDeFsdfC6deu680eOHNHEiRNTPM6rrrpK48ePV69evdwq5NWqVXO3kRUvvfSSe1HRpUsXt/+GUqVKac+ek9ezFdSeftIzLDbBGAAAQGTKiWvYYO27cePGev/99/XQQw8lG4x5t9e9ezddffXVvmWJ8bNlTKI7vXWr3O5F7n/IaO0fdKUDICnCMWQ7W/Npy5Yt7rSt5pzaUxjbp5b36dCBAwf05Zdfhty+bYf+Xi1btlTVqieMH31cTEyM7rzzTt+87XTUnuSDZeUqo779kp74y50qjR/j6NJW1BYDAACIZMG8hs3ufdtAzHYXkh433HBDwNxe1a+3Mcn7K36Wut5lNOTFRCV6cjMAIBxD9gsMmWwNrWLFiqW4rn2S1KxZs2Q/Gwr7tjXRbH8IXq1atUp1/3afRYsWdadtk03bfDMYPvjQ81Rsb0DH+9ZrExzVqEEwBgAAEMmCeQ0b7OvnE2uo3d3toIYPdVSlin+ZHa/rw4+lnbuytCsAEYSaY8h2v//+u2/aW006NbbvreQ+Gwr7tp2E2lpl6d2mffplO/9PbZs5KS7OaOiwRL34klF8/MnvlyhBMAYAABDpgnkNG+zr5xNHtLQ1zhpe4GjKa456Pego8Nn58RaXrh49+rpdnzRt2tSt7WabZ/bt21dz5swJamsQALmDcAzZ7u+///ZNV6pUKc31K1as6Ju2nXZmpd+x7N534PbsE60SJUpkaJt//fWXcsvu3UYP9DKa6x+kU/ny5druAQAAECKCeQ0b7OvnRYsW+aZLly7tuyewfe1ec7Wjt6c7uu4a20dZ0s+tWLHYvR+wI8AfOnRIW7du1eLFizVkyBB17NhRn3/+eZaOC0BoIxxDtrInk8AnRekZ6ebEdXbv3h0y+w6cT++oPYHrZfZ3yagVPxt16W60clXAcZSRnnuammIAAADRJpjXsMHe9/Tp033ztibYiSNTFivm6MH7YzR1sqP8sccXOlXl5OkgJ08XxRbqofPOuylJbbZ9+/apX79+mjFjRqaPDUBoyxvsA0BksU9ZAtlO8dNSsGDBVLcRzH0Hzqdneyeul9nfJb3ssNdvvSNNeNUoIaBaeN1zPcEYNcABAACiTzCvYYO17/j4eD399NPav3+/O1++fHl35PqUVDnd0b09b1KevJdo+lvltOt4/2P2mvrn1VK1atKTA/7U2289pz/++MN9b8yYMapZs6bOP//8TB0jgNBFzTFkq6NHjyaZz5eOdn358+dPMp/ZNv05se/AbaZne1ZsbGyKx5SdDhww6vek0djxSYOxaztJI4c7OuUUao0BAABEo2BewwZr38OGDdMPP/zgu8YfNGjQSQ/CkxvZ8tprKujNaY5uvF7KE3B3vG6dNPjF6qpW4xVVqlTF92D6lVdeydTxAQht1BxDqmyb/yVLlqS6jm2DX7hw4ZNObFZcXFy6mkMGSu8TphPlxL4Dt5me7Z14Qj/xmLLLH38Y9X/KaPMW/zJ77u/7iKNWLQnFAAAAolkwr2GDse9x48bp/fffd6fz5MmjJ598UrVr10735wsVcnRfT0ft2xoNH2m04mf/ews/L6LY/A9K6uXO//bbb9q0aVOSftIAhD/CMaTq119/1ejRo1Ndp0WLFr5wrFChQhmuBXbiOiduI71yYt+B8+mt0RZ4cs/s75IS+7Tqo4/lnrQDcz07NPXAZxydfjrBGAAAQLQL5jVsbu/71Vdf1dSpU5MEY5dddpkyo2pVR6NfluYvkF4ZZ7Rnr2f5kaMX2N7KJHmabK5cuZJwDIgwNKtEtrJVmIsFjI+8Y8eOND9z4jqlSpUKmX0Hzp84LHR6tpnZ3yU5Bw8aPf2c0ZChSYOxyy6VXh1LMAYAAIDgX8Pm5r4nTJigyZMnJwnG2rZtq6ywHfi3ae1oxjRH114jxcTYZfY/FXzrvD9nl9vFCYDIQc0xpOryyy93Xxlxxhln6OefPXWRN27cmOb6tlqyV7ly5Xy10DIju/ddtWpV37Tt3NOOVJPWcNSB26xme/LMBr/9bvTUs0ZbAppR2i4cHrjPUccrPSdxAAAAINjXsLm177Fjx2ratGm+YOypp55S69atlV2KFnX00P2OOrQ1Gvay0Yof/e+tWu3o5s5G990jtb6Ma3EgElBzDNnOjuDi9csvv6S5/ooVK5L9bCjsu0KFCklqo6W1zcTERLeadWrbzIjERKM33zbqcW/SYOy0CtK40Y6uvsohGAMAAEDIXMPmxr5tty+BwdgzzzyTrcFYoBo1HI0ZaVSwQMDFuFNSe/dKzw0yeqCX0fp/qEUGhDvCMWS7Zs2a+aa///57HThwIMV1ExIS9NVXX/nmmzdvHlL7jomJ0cUXX+ybX7RoUar7tyPkeIePtv0lNGzYUJllR7F++LHjo1EmJG1GOXmio1q1qC0GAAAAhdQ1bE7ve+TIkZoxY4Y7nTdvXj333HO69NJLlZOWL1+mw4c9x2g5zlm+6Z9WSHd0NZowMVFHjhCSAeGKcAzZrn79+u4TI+vw4cN68803U1z3ww8/1LZt29zpokWLqmnTpiG37w4dOvimP//8c/39998pPvWaMmWKb75Vq1aZHnnTmjjJ6HvPaNQuu6l+jzka8ISjwoUJxgAAAJCyYF3D5uS+R4wYobfeesudzpcvnwYOHKiWLVsqJ/33339uIOdVo0YNjR1zuqqe4V8nPl56Y4Z02x1G//uWgAwIR4RjyP4/qpgY9ezZ0zdvqzzPnz8/2adEgSeaO++8M8X+xrZs2aILL7zQ91q+fHmu7fu8885T48aNfcNRP/7449q6detJtdCGDx+un376yTcEdffu3ZVRgTXE/vvPP129mjRpgqP27WhGCQAAgLTlxDXsxIkTfdfjHTt2zNV9Dxs2TO+8845vIK7BgwcnaTWSUQ888IDmzp2rgwcPprjOunXr3HuLv/76y7fs3nvvVd1zHbclR88ejgoGZHlbt0mP9TN6/IlEbdtOSAaEE8cYE1Kldq9tvJ2LbEfm3g4ibWeRIfbPEdaef/55t3aWV926dd2aXbZfgNWrV+u7777z/Xvbk+fQoUPdqtEphWOdOnXyzb/yyivuSTc39u0dQceerLdv3+7O2ydathlm5cqV3aabX3/9ta8jUfs3NWDAALVr1y7Vfx97kRDI/un/8stPSkzc51ng1JLjlNNpp0nVqjruSDn2d7722muT3Z4NDGfNmpVkmR0+e8mSJb75Fi1anPQ5+3sFdpwaKpzDe1Xkm6HudFzcMR1p3k8qdEqwDwuIepw3gdBE2URuXMPacGzSpEm+wazmzJmTK/ueOnWqxo0b55u34dwFF1yQrv/p9lo/uWtdG+7ZViS2BlqdOnVUvXp1lSxZ0g3e7H3hqlWr3L7QAu8PbVDWuXPnJNvZvsNo1BijL/29tRz/faU7Oju68XpHZcqUdJdxvwlkH1tesxOjVSLH9O3bVwULFnQDG3tSsaNIekeSDHTZZZepX79+qYZTwd532bJl3Y4/+/fvr7Vr17qh06effnrSerafhD59+qQZjFlffPFF6iuY32XM79q0Ue7Lsr9TSuzTuLS2mdz7KYVtAAAACG85cQ0bjH3/888/SeaXLl3qvtKjePHiqT4ItjXbUrpXCLwJf+SRR5JtwnlqWUcDn3W0ZKnR8JFG3gpyR45I4181+nS+0dNPxumC8/Ol63gBBAfhGHLujytvXvdE1759e82bN8+t2bRz5063b4HSpUvr3HPPdd87//zzw2Lf9inX5MmT3WaaCxcudKtZ79mzxz2hly9fXk2aNNFVV13lXggAAAAAoSCY17ChfP08atQodyRN26rkzz//dFsw/fvvvzp06JDb3YoNxGrXru3WUrOhmK1RlprGFzp6o540bbodbd7TD5m1fr3tsH+/rrwiVnd1NTreaAlAiKFZJc0qEQRbtxq9NMLou++TLi91itSnl6Oml9DhPs0qgdDEeRMITZRNIHRs2GA07GWj5T8mXV6kiNTldkedrrYP87neB0KpWSUd8gO5KD7e6K13jG678+Rg7KorpOlTCcYAAACAcFa5sqOXhzka0N/RKQFd5tq+/0e9YnRHN6Nly+nrGgglNKsEcsl33xuNfsVofdIuE3R6ZenRhx131BsAAAAAkVGbs/Wl0kW2ueWM/HrrnSNKTPQ3tXyoj1Hzpkb33uOofHnuA4Bgo+YYkMM2bDR6tG+i+jyaNBjLl0/qcoej118jGAMAAAAiUdGijvr1LaxZ7xRX/XpJ31v8lXTL7UavTU7UoUPUJAOCiZpjQA7Zvdu4HXLO+UBKSEj6Xr260sO9HVU5nadEAAAAQKSreWZejX45Rp8vNhoz1mjHDs/yY8ekKdOkD+YZdblDurwD/ZEBwUA4BmSz/QeM3nzLaOZ70tGjSd8rX05u1elmTT1VrQEAAABEB3v937K51LiRNOMte88gHYvzvLdnr9wBu96dJd1zt3RxE+4XgNxEOAZkE1sVetZsucHYwf+SvlewgNT5NkfXXyvFxhKKAQAAANGqYEFH3bo4at/OaMJEo0Wf+9/bsFF6vL9R3XOlnj2ks+pw7wDkBsIxIBtCsQ8+9Dz92bs36Xu2X7GrrpQ632JHquHEBgAAAMCjQnlHzwxwdMN1RmPHG6342f8v8/Mv0t09jVo0N7q7m6OKFbmXAHIS4RiQSf/+a2uKGb33vrR/f9L3YmKktm2kLrc7KleOExkAAACA5NWp7Wj0y9K3S6RxE5IO4vXFYumrr43atjHqfKuj0ypwbwHkBMIxIIPW/2P0/hyjjz6Rjhw5+f3mzaTuXRydTmf7AAAAANLZH1mTi6RGDaWPP5UmTTbavcfznh3c66OPpU8/NWrT2hOSUZMMyF6EY0A6xMcbffM/6f25Rst/PPl927d+00vknqhqnsnTHAAAAACZuEHP6+jKy6XLWklvvyu9+bbR4cOe9xISPcHZp/ONLrvU6PbbHFWuxL0HkB0Ix4BU7NptNO9Dz9DKO3ed/H6ePFKb1tItN1JTDAAAAED2ddp/5+1Sp47S2zON3ptt+zr2vJeYKH02X1qw0KhZU6NbbnJUqyYhGZAVhGPACY4cMfr6f9Knnxn9sMxz8jlR8WLS5R2kTlc7OrUsJyIAAAAA2a94ccftkP+m643enWU08z3pv/8879n7FNsn2ReLjc5r4AnJLjjf00QTQMYQjgHuicUzOoytorz4S/9TmRPVqe0JxFo0k2JjOekAAAAAyHnFijnq1sXR9dcZzbQh2Szp4PGQzLJdvyz/0ajK6dLVV3latxQpwv0KkF6EY4haCQlGq1ZLi780+vJraceO5NfLn1+6tJWt0kx1ZQAAAADBU6yoo653OrrxeqO58+TWJtsV0P2LHelyxCij8a9KrS8z7oP9alUJyYC0EI4h6jrW/2mFJxD7+htpz96U161fzz5xcdS8KU9dAAAAAISOwoUd3XyjdN010vyF0ltvGzcY8zp8RG54Nnee0bnnGF15hee+pkABgjIgOYRjiHi7dxt9/4O09Duj75dJBw6kvG7lSlLbNo5aXyqVK8eJAwAAAEDoypfPUYd2Uvu2nqaVs+cYffO/pP0m/7LSvoyGvyy1bG7Urq2jc8+hbzIgEOEYIrJ2mG0u+d33Rku/k/74M/X1K54mNWsqNW9mm01ykgAAAAAQXmwn/OefJ51/nqPtO4w+mGc078OkLWVsv8offmxfRuXLe4Kyli0cnVmDeyCAcAxhzxijfzbIbS65bLnRsuX+EVxSUq2q1PQSTyBW9QxOBgAAAAAiw6llHXXv6uiOzkZffiXN+8i4tcoCbd0qzXjLvoxbWaBFC6OWzR1Vr8a9EaIT4RjCMgzbcDwM+2mFpw+x1PoOs/Lnk+rVky5s6OjCC23zSZpMAgAAAIjsJpd2YLFLWznats3o0/nSJ58abd6SdL1Nm6U3ptuXJyi7sJFRo0aOGtSTYmO5b0J0IBxDyDt61GjNWrlNJVeu8jSZ3JtGGGZVrGjDMKlRQ8ftXJ/OJwEAAABEI9uf8h2dpdtvk1b/Kn3+hdEXi6WdASNdeoOyWbPtyyh/fjtImdGFjRw1aihVqkitMkQuwjGEnF27jVatklau9vy0wVh8fNqfK1JEqldXuuB8xw3FTjuNpxwAAAAAENg32dlnSWef5ei+nkYrV0lfLPYEZbv3JP13OnbM9uPs6cvZKlNaalDfqH59T+WDCuUJyxA5CMcQVPv2Gf2+Ru5rzRrP9IlPL1JSpLBUt659muGoQX1PP2J58hCIAQAAAEBaYmIc1T1Xqnuuo/vv9bTQWbLUM6jZn+tOXt/ep322wL48YVmJ4lLNmsYd1KxWTc/gZqVLE5ghPDnGduAUQvamp71cNifnJUqUcKf37dvn9meF7JeYaNxOH+2X7J/rjP780zO9dVv6t1HuVOnss6VzznLcn7azSMKw4Pnf//6Xo9vPH39QZ2+a6U7Hx8frt9NvVFy+ojm6TwDpO28WsVV1JR08eJDzJhAiKJuRrUmTJsE+BET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" ] }, "metadata": { "image/png": { - "height": 361, - "width": 611 + "height": 262, + "width": 527 } }, "output_type": "display_data" @@ -1038,7 +921,7 @@ " bernoulli_generative_model(N=4000, rate_prior=RATE_LIFT_PRIOR, baseline_rate=BASELINE_RATE),\n", " {\"lift\": 0.015},\n", ")\n", - "demo_draw_bern = pm.sample_prior_predictive(draws=1, model=demo_model_bern, random_seed=RANDOM_SEED)\n", + "demo_draw_bern = pm.sample_prior_predictive(1, model=demo_model_bern, random_seed=RANDOM_SEED)\n", "n_A_demo = int(demo_draw_bern.prior[\"n_A\"].values.reshape(-1)[0])\n", "n_B_demo = int(demo_draw_bern.prior[\"n_B\"].values.reshape(-1)[0])\n", "\n", @@ -1054,8 +937,11 @@ " progressbar=False,\n", " )\n", "\n", - "az.plot_posterior(idata_bern_demo, var_names=[\"lift\"], ref_val=0.0, figsize=(6, 3.5))\n", - "plt.title(r\"Posterior on conversion lift at $N=4000$\");" + "az.plot_dist(idata_bern_demo, var_names=[\"lift\"])\n", + "ax = plt.gcf().axes[0]\n", + "ax.axvline(0.0, color=\"C2\", linestyle=\"--\", label=\"ref = 0.0\")\n", + "ax.legend()\n", + "plt.title(r\"Posterior on conversion lift at $N=4000$\")" ] }, { @@ -1063,7 +949,7 @@ "id": "005827fd", "metadata": {}, "source": [ - "The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The machinery is the same.\n", + "The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The decision rule is the same; the ceiling is lower because the planning prior carries a smaller probability that the lift is positive, and that probability — not the sample size — is what sets the asymptote.\n", "\n", "## The cost of an under-informed prior\n", "\n", @@ -1076,10 +962,10 @@ "id": "279cd463", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:36.545635Z", - "iopub.status.busy": "2026-05-28T16:43:36.545514Z", - "iopub.status.idle": "2026-05-28T16:43:38.412662Z", - "shell.execute_reply": "2026-05-28T16:43:38.412173Z" + "iopub.execute_input": "2026-06-01T17:16:16.117411Z", + "iopub.status.busy": "2026-06-01T17:16:16.117300Z", + "iopub.status.idle": "2026-06-01T17:16:23.592971Z", + "shell.execute_reply": "2026-06-01T17:16:23.592185Z" } }, "outputs": [ @@ -1087,160 +973,40 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling: [delta, y_A, y_B]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [delta, 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"text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 461, - "width": 711 + "height": 432, + "width": 613 } }, "output_type": "display_data" @@ -1289,9 +1055,9 @@ "\n", "The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty, and a fair one, because the lights are the only ones we have.\n", "\n", - "This re-frames the planning conversation. Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. And it does its work by making explicit an assumption the power calculation leaves buried: that there is a believed distribution of effects at all. Naming the assumption a conventional analysis would rather not state, and giving it a posterior, is the move this sequence repeats.\n", + "Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. It makes explicit the assumption power leaves buried: a believed distribution of effects. Naming that assumption, and giving it a posterior, is the move this sequence repeats.\n", "\n", - "In each case here, the Gaussian and the Bernoulli, the spine never changed. What changed was the likelihood. The same is true across this notebook and its successors. Planning, interpretation, synthesis are three questions the same machinery answers in turn; the posterior is the constant, the question changes. We have just seen the first of the three.\n", + "In each case here, the Gaussian and the Bernoulli, the inference machinery was the same. What changed was the likelihood — and with it, the ceiling that the likelihood's geometry sets on how much assurance any sample size can buy. The same is true across this notebook and its successors. Planning, interpretation, synthesis are three questions the same machinery answers in turn; the posterior is the constant, the question changes. We have just seen the first of the three.\n", "\n", "## Authors\n", "\n", @@ -1312,10 +1078,10 @@ "id": "b30e957b", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:38.414030Z", - "iopub.status.busy": "2026-05-28T16:43:38.413924Z", - "iopub.status.idle": "2026-05-28T16:43:38.429931Z", - "shell.execute_reply": "2026-05-28T16:43:38.429362Z" + "iopub.execute_input": "2026-06-01T17:16:23.594569Z", + "iopub.status.busy": "2026-06-01T17:16:23.594445Z", + "iopub.status.idle": "2026-06-01T17:16:23.605251Z", + "shell.execute_reply": "2026-06-01T17:16:23.604855Z" } }, "outputs": [ @@ -1323,24 +1089,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Thu May 28 2026\n", + "Last updated: Mon, 01 Jun 2026\n", "\n", "Python implementation: CPython\n", - "Python version : 3.12.12\n", - "IPython version : 9.8.0\n", + "Python version : 3.13.13\n", + "IPython version : 9.14.0\n", "\n", - "pytensor: 2.38.2\n", - "xarray : 2025.12.0\n", + "pytensor: 3.0.3\n", + "xarray : 2026.4.0\n", "\n", - "pymc : 5.28.5\n", + "arviz : 1.1.0\n", + "matplotlib: 3.10.9\n", "numpy : 2.3.5\n", - "matplotlib: 3.10.8\n", - "seaborn : 0.13.2\n", "pandas : 2.3.3\n", - "arviz : 0.23.0\n", - "scipy : 1.16.3\n", + "pymc : 6.0.1\n", + "scipy : 1.17.1\n", + "seaborn : 0.13.2\n", "\n", - "Watermark: 2.5.0\n", + "Watermark: 2.6.0\n", "\n" ] } @@ -1365,7 +1131,7 @@ "default_lexer": "ipython3" }, "kernelspec": { - "display_name": "pymc-marketing-dev", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1379,7 +1145,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.13.13" } }, "nbformat": 4, diff --git a/examples/causal_inference/assurance_planning_simulation.myst.md b/examples/causal_inference/assurance_planning_simulation.myst.md index e877141f3..c40afae96 100644 --- a/examples/causal_inference/assurance_planning_simulation.myst.md +++ b/examples/causal_inference/assurance_planning_simulation.myst.md @@ -6,7 +6,7 @@ jupytext: format_name: myst format_version: 0.13 kernelspec: - display_name: pymc-marketing-dev + display_name: Python 3 language: python name: python3 --- @@ -20,16 +20,18 @@ kernelspec: :author: Nathaniel Forde ::: +Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records — an off-kilter orientation in pursuit of theory, anomalous findings and the next question. + ## The question planners are actually asking -When a team approves an experiment, the question they are silently asking is whether the experiment will be useful. The frequentist quantity called "power" answers a different question entirely: given some specific effect size we have not observed, what is the probability the experiment crosses a fixed threshold? The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold. +When we pursue an experiment, we implicitly assume the experiment will be useful. Our questions are "operationalised" as power: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold. -Assurance is the Bayesian alternative, and its construction is mechanical: draw an effect from the prior, generate a dataset, fit the model, apply the decision rule, repeat. The result is a single number with a clear meaning: the probability that the experiment is about to conclude what its stakeholders need it to. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the conclusion is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. +An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the conclusion is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery answers all three questions; the question being asked of it is what changes. :::{admonition} Where this lands in regulatory practice :class: note -The assurance framing here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\Pr(\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference, which is exactly the split the generative and inference models below are built on. The setting differs from product experimentation; the machinery is identical. +The assurance focus here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\Pr(\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference, which is exactly the split the generative and inference models below are built on. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical. ::: ```{code-cell} ipython3 @@ -52,14 +54,13 @@ warnings.filterwarnings("ignore", category=UserWarning) ```{code-cell} ipython3 %config InlineBackend.figure_format = 'retina' -az.style.use("arviz-darkgrid") rng = np.random.default_rng(42) RANDOM_SEED = 42 ``` ### The scenario -The running scenario across this notebook and its two successors is a product-experimentation question: an e-commerce team is evaluating a redesigned checkout flow. The continuous outcome is revenue per visitor; the binary outcome is conversion. We will not commit to a particular vertical or product; the structure is generic to any A/B test with one of these outcome types. What matters is that the same prior beliefs and the same decision rule will be asked of two different likelihoods. The Gaussian case will carry the argument; the Bernoulli case will confirm it travels. +An e-commerce team is evaluating a redesigned checkout flow. We cover two outcomes: the continuous outcome is revenue per visitor, the binary outcome is conversion. We will not commit to a particular vertical or product; the structure is generic to any A/B test with one of these outcome types. What matters is that the same prior beliefs and the same decision rule will be asked of two different likelihoods. The Gaussian case will carry the argument; the Bernoulli case will confirm it travels. ```{code-cell} ipython3 @dataclass @@ -73,7 +74,7 @@ class EffectPrior: return rng.normal(self.mu, self.sigma, size=size) ``` -## Power and assurance: two questions in the same wardrobe +## Power and assurance: two questions, different focus. Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null: @@ -89,8 +90,6 @@ $$ This is the marginal probability of a successful decision, where "successful" is whatever decision rule the team has agreed in advance and $\pi(\theta)$ is the team's prior over the effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in. -A planning question is not a hypothesis test. When stakeholders ask "will the experiment work?", they are not committing to a specific effect size and asking after its detectability; they are asking after the experiment in the world they actually inhabit, with all of its uncertainty intact. The frequentist answer requires them to invent a specific world before the calculation will proceed. The Bayesian answer integrates over the worlds they already hold open. - ## The simulation engine Assurance is an expectation over the *prior predictive distribution*: the joint distribution of effect and data that the model generates before any real data arrive. As a Monte Carlo estimator the integral becomes a procedure in three steps, repeated for each candidate sample size: @@ -99,7 +98,7 @@ Assurance is an expectation over the *prior predictive distribution*: the joint 2. **Fit the analysis model** to each synthetic dataset and apply the decision rule. 3. **Record** the fraction of synthetic experiments in which the decision succeeded. -The first step is the one worth dwelling on. Drawing a hypothetical truth and then generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive. +Drawing a truth and generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive. ```{code-cell} ipython3 def assurance_curve(assurance_fn, N_grid, n_sims, rng, **kwargs): @@ -149,7 +148,7 @@ A single synthetic experiment makes the inference model concrete. We use the do- ```{code-cell} ipython3 demo_true_delta = 0.6 demo_model = pm.do(gaussian_generative_model(N=200, prior=EFFECT_PRIOR), {"delta": demo_true_delta}) -demo_draw = pm.sample_prior_predictive(draws=1, model=demo_model, random_seed=RANDOM_SEED) +demo_draw = pm.sample_prior_predictive(1, model=demo_model, random_seed=RANDOM_SEED) y_A_demo = demo_draw.prior["y_A"].values.reshape(-1) y_B_demo = demo_draw.prior["y_B"].values.reshape(-1) @@ -165,8 +164,11 @@ with gaussian_two_arm_model(y_A_demo, y_B_demo, EFFECT_PRIOR): ``` ```{code-cell} ipython3 -ax = az.plot_posterior(idata_gauss_demo, var_names=["delta"], ref_val=0.0, figsize=(6, 3.5)) -ax.set_title(r"Posterior on $\delta$ at $N=200$ (one simulated dataset)"); +pc = az.plot_dist(idata_gauss_demo, var_names=["delta"]) +ax = plt.gcf().axes[0] +ax.axvline(0.0, color="C2", linestyle="--", label="ref = 0.0") +ax.legend() +ax.set_title(r"Posterior on $\delta$ at $N=200$ (one simulated dataset)") ``` The posterior concentrates above zero; under the decision rule the team would conclude the new flow lifts revenue. That is a single draw of the experiment. Assurance asks how often this conclusion would arrive if we re-ran the experiment many times under our actual prior. @@ -208,7 +210,7 @@ The two posteriors agree to within MCMC noise. The closed form will carry the de def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng): seed = int(rng.integers(2**31 - 1)) pp = pm.sample_prior_predictive( - draws=n_sims, model=gaussian_generative_model(N, prior), random_seed=seed + n_sims, model=gaussian_generative_model(N, prior), random_seed=seed ) y_A = pp.prior["y_A"].values.reshape(n_sims, N) y_B = pp.prior["y_B"].values.reshape(n_sims, N) @@ -295,7 +297,7 @@ The three power curves fan out because each conditions on a single assumed world The story is in the crossing. Between $N=400$ and $N=800$ the assurance curve drops below power-at-the-mean, having sat above it at smaller samples. At small samples assurance is buoyed by the optimistic tail of the prior: it counts the plausible draws above $0.4$, effects of $0.8$ or $1.0$ that even a hundred observations have a real chance of detecting, which the single-point power calculation never sees. The frequentist who quotes power at the mean is being pessimistic about small experiments in a way the prior is not. -At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one, because it carries the prior's honest admission that the effect might be zero or negative. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\delta > 0) \approx 0.79$. No sample size buys more assurance than the prior's confidence that there is something to find, and that ceiling, rather than any point on a power curve, is the quantity a planner should internalise before paying for a larger experiment. +At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one: the prior assigns positive probability to a zero or negative effect. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\delta > 0) \approx 0.79$. No sample size buys more assurance than $P(\delta > 0)$. That ceiling, not any point on a power curve, is what a planner should know before committing to more data. :::{admonition} The minimum detectable effect leads a double life :class: note @@ -305,7 +307,7 @@ Power is usually quoted at a "minimum detectable effect" (MDE), and that single ## The same machinery on a binary outcome -The conversion-rate version of this experiment is the canonical Bayesian A/B test setup, and the same machinery transfers without conceptual modification. The generative model fixes the baseline conversion rate, places the planning prior on the lift, and emits binomial counts per arm. The inference step places Beta priors on the per-arm rates and reads off the posterior probability that the lift is positive ({cite:p}`stucchio2015bayesian`, {cite:p}`kruschke2014doing`). +The generative model fixes the baseline conversion rate, places the planning prior on the lift, and emits binomial counts per arm. The inference step places Beta priors on the per-arm rates and reads off the posterior probability that the lift is positive ({cite:p}`stucchio2015bayesian`, {cite:p}`kruschke2014doing`). ```{code-cell} ipython3 BASELINE_RATE = 0.10 @@ -332,15 +334,16 @@ def bernoulli_assurance_at_N( ): seed = int(rng.integers(2**31 - 1)) pp = pm.sample_prior_predictive( - draws=n_sims, + n_sims, model=bernoulli_generative_model(N, rate_prior, baseline_rate), random_seed=seed, ) n_A = pp.prior["n_A"].values.reshape(-1).astype(int) n_B = pp.prior["n_B"].values.reshape(-1).astype(int) + actual_sims = len(n_A) # PyMC v6 may return fewer draws than requested alpha0, beta0 = beta_prior_params(baseline_rate, kappa) - p_A_post = rng.beta(alpha0 + n_A[:, None], beta0 + N - n_A[:, None], size=(n_sims, n_post)) - p_B_post = rng.beta(alpha0 + n_B[:, None], beta0 + N - n_B[:, None], size=(n_sims, n_post)) + p_A_post = rng.beta(alpha0 + n_A[:, None], beta0 + N - n_A[:, None], size=(actual_sims, n_post)) + p_B_post = rng.beta(alpha0 + n_B[:, None], beta0 + N - n_B[:, None], size=(actual_sims, n_post)) prob_positive = (p_B_post > p_A_post).mean(axis=1) return float((prob_positive > threshold).mean()) ``` @@ -423,7 +426,7 @@ ax.set_title("Assurance vs frequentist power, Bernoulli outcome") ax.legend(loc="lower right"); ``` -The picture is the same. Here the generative model produced the synthetic experiments and the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that the conjugate decision matches a full PyMC inference fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably. +The conversion-rate version runs the same loop, and the loop discloses something the Gaussian case conceals. The lift is bounded above: how large a positive effect can even be depends on how much room sits above the baseline conversion rate, a constraint the Normal likelihood never faces. The Beta prior on the per-arm rates is parameterised here by a concentration $\kappa$ — the number of pseudo-observations at the baseline rate that the prior is equivalent to. Setting $\kappa = 200$ is saying: I am as confident about the baseline as if I had watched 200 trials at that rate. This is a different handle on prior calibration than the Normal's mean and standard deviation pair, and it makes the ceiling claim more precise: the assurance curve asymptotes toward the prior probability that the lift is positive, and what that probability is depends not only on the prior's location and scale but on the scale on which the lift is defined. At a 10\% baseline with a planning prior mean of 1\%, most of the prior mass sits in a narrow positive strip; the ceiling is lower than the Gaussian case not because the machinery differs but because the planning prior encodes a more cautious belief about how large a conversion lift can plausibly be. The generative model produced the synthetic experiments; the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that this matches a full PyMC fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably. ```{code-cell} ipython3 def bernoulli_two_arm_model(n_A, n_B, N, baseline_rate, kappa): @@ -441,7 +444,7 @@ demo_model_bern = pm.do( bernoulli_generative_model(N=4000, rate_prior=RATE_LIFT_PRIOR, baseline_rate=BASELINE_RATE), {"lift": 0.015}, ) -demo_draw_bern = pm.sample_prior_predictive(draws=1, model=demo_model_bern, random_seed=RANDOM_SEED) +demo_draw_bern = pm.sample_prior_predictive(1, model=demo_model_bern, random_seed=RANDOM_SEED) n_A_demo = int(demo_draw_bern.prior["n_A"].values.reshape(-1)[0]) n_B_demo = int(demo_draw_bern.prior["n_B"].values.reshape(-1)[0]) @@ -457,11 +460,14 @@ with bernoulli_two_arm_model( progressbar=False, ) -az.plot_posterior(idata_bern_demo, var_names=["lift"], ref_val=0.0, figsize=(6, 3.5)) -plt.title(r"Posterior on conversion lift at $N=4000$"); +az.plot_dist(idata_bern_demo, var_names=["lift"]) +ax = plt.gcf().axes[0] +ax.axvline(0.0, color="C2", linestyle="--", label="ref = 0.0") +ax.legend() +plt.title(r"Posterior on conversion lift at $N=4000$") ``` -The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The machinery is the same. +The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The decision rule is the same; the ceiling is lower because the planning prior carries a smaller probability that the lift is positive, and that probability — not the sample size — is what sets the asymptote. ## The cost of an under-informed prior @@ -505,9 +511,9 @@ The three curves disagree most where they should: at small sample sizes where th The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty, and a fair one, because the lights are the only ones we have. -This re-frames the planning conversation. Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. And it does its work by making explicit an assumption the power calculation leaves buried: that there is a believed distribution of effects at all. Naming the assumption a conventional analysis would rather not state, and giving it a posterior, is the move this sequence repeats. +Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. It makes explicit the assumption power leaves buried: a believed distribution of effects. Naming that assumption, and giving it a posterior, is the move this sequence repeats. -In each case here, the Gaussian and the Bernoulli, the spine never changed. What changed was the likelihood. The same is true across this notebook and its successors. Planning, interpretation, synthesis are three questions the same machinery answers in turn; the posterior is the constant, the question changes. We have just seen the first of the three. +In each case here, the Gaussian and the Bernoulli, the inference machinery was the same. What changed was the likelihood — and with it, the ceiling that the likelihood's geometry sets on how much assurance any sample size can buy. The same is true across this notebook and its successors. Planning, interpretation, synthesis are three questions the same machinery answers in turn; the posterior is the constant, the question changes. We have just seen the first of the three. ## Authors diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.ipynb b/examples/causal_inference/multiple_experiments_meta_analysis.ipynb index f86f4ee53..e44fd51f8 100644 --- a/examples/causal_inference/multiple_experiments_meta_analysis.ipynb +++ b/examples/causal_inference/multiple_experiments_meta_analysis.ipynb @@ -16,7 +16,9 @@ "\n", "## The replication-as-evidence problem\n", "\n", - "Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question — \"did it work?\" — and gets, depending on which colleague answers, two incompatible defaults: vote-counting (\"four out of eight worked, so it's a wash\"), or pool-everything (\"the combined estimate is positive, so it works\"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that lets the markets be different, and lets that difference be part of what we are estimating.\n", + "Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question — \"did it work?\" — and gets, depending on which colleague answers, two incompatible defaults: vote-counting (\"four out of eight worked, so it's a wash\"), or pool-everything (\"the combined estimate is positive, so it works\"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that estimates between-market differences rather than assuming them away.\n", + "\n", + "Each experiment speaks about one market. The hierarchy is what lets us hear all of them at once.\n", "\n", "This notebook builds that model. The hierarchical Bayesian meta-analysis treats each experiment as a noisy estimate of its own market's true effect, and treats the per-market effects as draws from a population whose mean and variance are themselves the quantities of substantive interest {cite:p}`borenstein2009meta`, {cite:p}`higgins2009meta`. The structure is the one Rubin used for the 8-schools problem in 1981 {cite:p}`rubin1981estimation`, {cite:p}`gelman2013bayesian`, transposed to product experimentation. We develop it on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the third of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`sensitivity_confounding` for the interpretation counterpart. Readers wanting a deeper view of the partial-pooling vocabulary should also consult the existing PyMC notebooks on {ref}`multilevel_modeling` and {ref}`hierarchical_partial_pooling`, which we treat as predecessors rather than re-derive.\n", "\n", @@ -33,10 +35,10 @@ "id": "d5b9bd7c", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:13.317886Z", - "iopub.status.busy": "2026-05-28T17:00:13.317662Z", - "iopub.status.idle": "2026-05-28T17:00:17.044240Z", - "shell.execute_reply": "2026-05-28T17:00:17.043619Z" + "iopub.execute_input": "2026-06-01T17:59:26.257378Z", + "iopub.status.busy": "2026-06-01T17:59:26.257190Z", + "iopub.status.idle": "2026-06-01T17:59:28.475465Z", + "shell.execute_reply": "2026-06-01T17:59:28.474917Z" } }, "outputs": [], @@ -62,16 +64,16 @@ "id": "b8567665", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:17.046097Z", - "iopub.status.busy": "2026-05-28T17:00:17.045902Z", - "iopub.status.idle": "2026-05-28T17:00:17.049304Z", - "shell.execute_reply": "2026-05-28T17:00:17.048862Z" + "iopub.execute_input": "2026-06-01T17:59:28.477124Z", + "iopub.status.busy": "2026-06-01T17:59:28.476950Z", + "iopub.status.idle": "2026-06-01T17:59:28.791069Z", + "shell.execute_reply": "2026-06-01T17:59:28.790305Z" } }, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina'\n", - "az.style.use(\"arviz-darkgrid\")\n", + "az.style.use(\"arviz-variat\")\n", "rng = np.random.default_rng(11)\n", "RANDOM_SEED = 11" ] @@ -94,10 +96,10 @@ "id": "409b8357", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:17.050896Z", - "iopub.status.busy": "2026-05-28T17:00:17.050792Z", - "iopub.status.idle": "2026-05-28T17:00:17.060188Z", - "shell.execute_reply": "2026-05-28T17:00:17.059653Z" + "iopub.execute_input": "2026-06-01T17:59:28.792839Z", + "iopub.status.busy": "2026-06-01T17:59:28.792614Z", + "iopub.status.idle": "2026-06-01T17:59:28.801581Z", + "shell.execute_reply": "2026-06-01T17:59:28.801048Z" } }, "outputs": [ @@ -222,10 +224,10 @@ "id": "c85af685", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:17.061723Z", - "iopub.status.busy": "2026-05-28T17:00:17.061611Z", - "iopub.status.idle": "2026-05-28T17:00:17.070132Z", - "shell.execute_reply": "2026-05-28T17:00:17.069622Z" + "iopub.execute_input": "2026-06-01T17:59:28.802786Z", + "iopub.status.busy": "2026-06-01T17:59:28.802709Z", + "iopub.status.idle": "2026-06-01T17:59:28.810985Z", + "shell.execute_reply": "2026-06-01T17:59:28.810545Z" } }, "outputs": [ @@ -345,10 +347,10 @@ "id": "96f71013", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:17.071493Z", - "iopub.status.busy": "2026-05-28T17:00:17.071375Z", - "iopub.status.idle": "2026-05-28T17:00:17.084306Z", - "shell.execute_reply": "2026-05-28T17:00:17.083679Z" + "iopub.execute_input": "2026-06-01T17:59:28.812151Z", + "iopub.status.busy": "2026-06-01T17:59:28.812080Z", + "iopub.status.idle": "2026-06-01T17:59:28.823830Z", + "shell.execute_reply": "2026-06-01T17:59:28.823172Z" } }, "outputs": [ @@ -475,7 +477,7 @@ "id": "398b3030", "metadata": {}, "source": [ - "The interaction $F$-test reports whether heterogeneity is detectable; it does not estimate how large it is. The meta-analytic siblings of the same variance decomposition do, and because they recur through the rest of the notebook it is worth stating plainly what each one measures.\n", + "The interaction $F$-test reports whether heterogeneity is detectable; it does not estimate how large it is.\n", "\n", ":::{admonition} Three numbers for heterogeneity\n", ":class: note\n", @@ -496,10 +498,10 @@ "id": "b06c639a", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:17.085639Z", - "iopub.status.busy": "2026-05-28T17:00:17.085514Z", - "iopub.status.idle": "2026-05-28T17:00:17.089334Z", - "shell.execute_reply": "2026-05-28T17:00:17.088778Z" + "iopub.execute_input": "2026-06-01T17:59:28.825435Z", + "iopub.status.busy": "2026-06-01T17:59:28.825309Z", + "iopub.status.idle": "2026-06-01T17:59:28.829010Z", + "shell.execute_reply": "2026-06-01T17:59:28.828657Z" } }, "outputs": [ @@ -546,10 +548,10 @@ "id": "378cc883", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:17.090690Z", - "iopub.status.busy": "2026-05-28T17:00:17.090583Z", - "iopub.status.idle": "2026-05-28T17:00:20.461589Z", - "shell.execute_reply": "2026-05-28T17:00:20.461059Z" + "iopub.execute_input": "2026-06-01T17:59:28.830087Z", + "iopub.status.busy": "2026-06-01T17:59:28.830019Z", + "iopub.status.idle": "2026-06-01T17:59:31.101593Z", + "shell.execute_reply": "2026-06-01T17:59:31.101024Z" } }, "outputs": [ @@ -557,35 +559,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [mu, tau, offset]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 2_000 tune and 2_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + "NUTS[nutpie]: [mu, tau, offset]\n" ] } ], @@ -613,10 +587,10 @@ "id": "500ed2c0", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:20.463171Z", - "iopub.status.busy": "2026-05-28T17:00:20.463068Z", - "iopub.status.idle": "2026-05-28T17:00:20.468775Z", - "shell.execute_reply": "2026-05-28T17:00:20.468325Z" + "iopub.execute_input": "2026-06-01T17:59:31.103366Z", + "iopub.status.busy": "2026-06-01T17:59:31.103256Z", + "iopub.status.idle": "2026-06-01T17:59:31.111420Z", + "shell.execute_reply": "2026-06-01T17:59:31.110822Z" } }, "outputs": [ @@ -661,9 +635,9 @@ " \n", " \n", " Hierarchical Bayes (partial pooling)\n", - " 0.229\n", - " 0.370\n", - " 0.876\n", + " 0.238\n", + " 0.355\n", + " 0.872\n", " \n", " \n", "\n", @@ -673,12 +647,12 @@ " pooled effect se / sd \\\n", "Fixed-effect ANOVA (complete pooling) 0.148 0.105 \n", "Random-effects ANOVA (DerSimonian–Laird) 0.270 0.348 \n", - "Hierarchical Bayes (partial pooling) 0.229 0.370 \n", + "Hierarchical Bayes (partial pooling) 0.238 0.355 \n", "\n", " between-segment τ \n", "Fixed-effect ANOVA (complete pooling) 0.000 \n", "Random-effects ANOVA (DerSimonian–Laird) 0.807 \n", - "Hierarchical Bayes (partial pooling) 0.876 " + "Hierarchical Bayes (partial pooling) 0.872 " ] }, "execution_count": 8, @@ -710,32 +684,42 @@ "id": "1988dd9b", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:20.470049Z", - "iopub.status.busy": "2026-05-28T17:00:20.469926Z", - "iopub.status.idle": "2026-05-28T17:00:21.211662Z", - "shell.execute_reply": "2026-05-28T17:00:21.211129Z" + "iopub.execute_input": "2026-06-01T17:59:31.112820Z", + "iopub.status.busy": "2026-06-01T17:59:31.112725Z", + "iopub.status.idle": "2026-06-01T17:59:32.155462Z", + "shell.execute_reply": "2026-06-01T17:59:32.154952Z" } }, "outputs": [ { "data": { - "image/png": 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" + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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d7KeD/HRwtg5M1GPSbAqJoAOCdeDbTz/9ZMv6dCBpenp6tgOa9G9k54idZn554oknjMHbZgYLf/nll0aAw4ABA4xB/dGUUS0XX331lRFEEisdTHfDDTcYAVgPP/xw3H9zzU6hgQs6mM7M/p5KMz688MIL8uGHHxrZgK666ipJJJ2l/8knnwwbxBGOZvDo1auXESjy0ksvGTPTu50O9NMZ9vW+9NtvvxkBKnbRe5AGHelHg430umrQoIFt6ze7TzoQ/9133802aCrS8+mHH36Qn3/+2Qjc0UAafV7CnfT31uAkLd+TJk3KNnDHDA380vXqp02bNtK3b18jU48f2V1/yY4G2Tz99NPZBnFEupdpoI5+LrzwQunfv78xiz8A+2nQRLj7abwCqsIFc2TeQ9wazKGZ/CLVr+0OEtUJDpzOBgIAAHAqeksAAAAAwAf+2LxbJmzaHfF7LUsWkqurlnJknwC75EnJIS+eWSumsjt12155cNoyOZLuzVmWAQCA9+jgW51VPZypU6cag0/ssmzZMmO2X804YDWQ41Q66Pzrr7+Wjh07mhqkb5Vu84orrrB1IGS4ATh6vJqdBLHRrBgaUGC1jOiAeB3Ur4PGw9HMBxpkpDP+WwmM0EGsn376qTGwXAcYx8vkyZPlkksuMTJEWA3kOJXO4P74449L9+7dIw48i5fBgwcbM6GbDeQ4PcBGM46Ey+riFnq+u3XrZgQT2RnIcTq9pjToSIP0En2sr732mqlAjtODOjQgpEePHpbXhfhZunSp8RuNHTvW1kCO02nAktZdwg0y9qp41F+yopmp9Po0E8iR1bNc93nx4sW27BuAf4t0bTVs2DAupyxSQKibr/l69eqF/btm3bNbpPtpnTp1bN8mAADAqQjmAAAAAACP23/8hLw0L/IAigI5c8iTzarFJQ01EG85kpLkoTOqyAMNKkW9zNydB6TX1KVy6ETkjDUAAAB2uPbaa8P+XQd066zXdvjxxx/luuuuM2brj5edO3caA7Z1VnWn6CzRt9xyizFg3U6FCxcO+3cnj9EPNHvL/fffb2TXsIMGWGgGC80Mk13gkl3BA5l+//13Y3b4eBg2bJjcfffdcQ220Gvl6quvljVr1oiTnnnmGRk0aJCtASq7du2S2267zfFjcTMNgHjllVfk2WefdXzb27dvN54vs2fPtn0QvwZu2Vl24E0aLKLZvxIZsOSV+svpz0rNovHmm2/afg+++eabZf78+batE8D/y0QWTt26deNyqqpUqSL58uULG8DnVs2aNQv79+nTp8flHh4uU4hfs0kBAAD3IJgDAAAAADzujQXrZc+xExG/9/AZVaRk3tyO7BMQDxqIdFPNsvJsixqSEmVQ0oLdB6Xn1KVy8DgBHQAAIP7atm0bMWhAB5Bb9cUXX8jDDz8saWlp4sSA4oceekh+++23uG9LBxVpgEA8siXogKZwNNvDxo0bbd+uH+lgpyeffDIuA7Kffvrp/wwu09+la9eutma1yaQz0GuWBTt99tln8vzzzzsyYH3Tpk3GuYnHDMVZ0SwNI0aMiMu6NdNFz549ydxwmuHDh8s777wjTtEsT7fffnvcytSkSZPko48+isu64T0asKTZi7wunvWX08/XyJEj4xZgc88998jmzZvjsn4gqFasWBH271WrVo3LdpOTk8O2fzZs2GBbULbdKlasGDZjiQaH2pkhTSdHCJep5OKLL7ZtWwAAANlJyfYvAAAAAADXm7p1r4zbsDPi984tU0QuqlDckX0C4k3LcsGcKfLoX8sl7WTkAVKL9hyS+6YskbfOqSOFcuXkBwIAAHGTM2dOOfPMM8NmeZg7d66lbeigxwEDBkQVCHvGGWdIy5YtpXnz5lKmTBkpUqSIFChQQA4ePGgMjF+1apVMnjzZ+ISbSTo9Pd0IHvnyyy+ldu3aEg8amKIzth8+fDjLv+tsqG3atDEG9ujsqMWLFzeOUY9FZ/PX8zpx4sRs19+qVStj4H52dADmHXfcYQyc1nUj+9m7+/TpYwT5nE7L1jnnnCNnnXWWUd70PObIkcNYRjNq6CDuGTNmZLlsJv2blm8drKq/r5aL++67L8sBW3q96cy9Wi60fGi5yJ8/v5ENY+vWrUYWHN3mgQMHwv6cL730klx44YXG9WHVhAkT5IUXXojqu40aNZL27dtL06ZNjUFrev5OnjxpXIs6qExnHdYMPJGCWHbs2GGU3W+++cZYR7yMGzdOPvzwwyz/VqhQIWndurXx25cqVcr4LXQ2aN133T/93TWQTf890oDHwYMHG7+51+i519m1a9WqZdwnq1WrJgULFjT+u350UKXerzLvWYsWLTIyX+hHZ9sPR4M5mjRpYpzjeNL90Ht9dgNPdcDpeeedZ5Rd/Y31o88H/Z31mPRZor91pAHtejwdOnSQSpWiz7yJxNL7Y7169f5XvvWee2r51uA1Ldt6v9W6xT///GOUBS3n0QTxNWjQQKpXry5eFO/6y6kZsT7++OOI39N1a73v3HPPlTp16kjJkiUlT548xv5pcOSCBQuMZ9W6dev+s6w+fzSA96qrrory6AFEEi5YvESJEpKamhq3k6j1y+yCFPSZr8Fbbr33aqYjvR9lRe9nWid99NFHbQtWzi4IW++pmrkIAAAg3gjmAAAAAACPOnQiXV6ctybi9wrkzCGPNK5qdDwDftGqdGF555y60nvaUjlw4mTE7y/bd1ju/XOJsUzh3AR0AACA+NHBzOGCOXQw886dO43BO7GaP3++MegxnJSUFOnUqZMxs3p2g3OKFi1qfPTvOtOoDkQcOnSoDBkyRA4dOpTlMjpzq86a/8MPP0ju3PZn/NMB3Fu2bPnPf9cBiY899pjUr18/22U1aOXKK6+Up556yhhAmhUNsolEAw46d+4sTzzxhDHInjbUf/Xr188IljiVDui999575aabbjICLE6n5UyDaW644QZjkK+W4b///jvb32HevHnyyy+/GGXzxRdflGXLlv3r7xogct111xmzoGs5Pl3mLMQdO3Y0gkBefvllI9AhOzqgVsu+DmK3OlgvmkFlOiBay5gGcZxOz1+5cuWMjw7+1UFsGmD09ttvG7OmZ0ezKOh18u6770o8bN++3bi+TqeDhB944AHj+tPfJbvfQgfu6wD/Tz/9VN5///2wx6KDlXXQXKQsR26gx68Zmdq1a2cEzmVV/k+VGQChg7t1ucyZsYcNG2ZkXNJgnuwGXOr512dLrly5JF4060pWgz71uaZlUYNVsqP3WL0H6HUwcODAsIPTNWjrzTffNAZPwr10EPAFF1xgBLtpMJEGJIWjAQNat9FAJn2GKr1/6zWtdYfs6L1Bsz1pwKgXxbv+knmfiCaQV58bjzzyiPEbZEWDOy666CLjetaZ7fUZe3pQhwaXhAu6BBA9bVeFyyCh9b14Kl++fMQMb24N5rjkkkuMOrC2f7PLhNeiRQs5//zzLW1Hn1Ea/J0dvUfHK3sKAADAqQjmAAAAAACPemvhetl5NPxsh6pXw8pSIm/8XvYDidKgWAH5sE19eWDq0qiuhZX7j0iPPxcbAR3F8nBNAACAONVRGjSI+B0dLKsD7mKhg581I0K4Gc9Lly4tr7/+ujHoMhY6ALNHjx7GoBmd4V8HhmdFB/y999578uCDD4rdTh8IqYOideBiLLNDa5CJZmrIbjCTDkieNm1a2HVosI0GrdSoUcMIGNBB6GTq+H9OD6zQsqYBBFkFVWRFB4zpgH4tb1OnTs32ezp4S7N7jBo16j+D5z/66CNjQGo0NCBAM2XotREu0OGrr74yskHkzZtXzOrfv3+2wVCZrr/+emPQcqRB/5l04H737t2NbAh33323MaA3OzrTug721zJrt6zuCVdccYVxjer9I9pjufPOO40Bzrfddlu2s9jrvW706NHGd918n9dj0AHrkQa4RzNgXsvEZZddZgQUZTVbfuaASz0v8Zwd+vRADv1tdbB3LGWqQoUKxnNCM9588skn2X5PA7a2bdtmXJtwFw3M0YDQs88+2/K6NIvHK6+8YpRvDWzQTE3ZBfFp9h6rg3ITId71F6XrCxcEp8F0GiSoAVXR0GBVPdcaaKn3n9Mzl4ULLHGjzz//3NLyY8aMkb59+2b79+XLl4sfaValcOXKS5mD4pndwoqsAr1OFe82RqT1a2YOt9L7mgaHajCFTipwOg2A7d27t1HPN1P31UBZDeZ+9dVXs/2OZpsLd28AAACwE8EcAAAAAOBBs7bvk+/X7Yj4vTNLFZaOlWKf8RfwiqoF88lH59WTe/5cIluPHIv4/TUHjhrf1YAOgpwAAEBc6idVqxoD+cLNapzdYN1wNEhDB/OGm+1fZxbVAU1mVapUyZgZ+5ZbbpGVK1dm+R3N4KGDic1kFomWnj+dtV1nBLeTZnKIFMyRSY//ueeeMwYI6cBtHdyqmRT0362cYz/Rgb7vvPNOzAEQOqhfl9OBV1u3bs3yO7NnzzaChjIyMv4VkKOBIDpYPFaaPUIHY2rAQ1b2799vDCTWgCYzdL3hglNU165d5fHHHze1fp1lXQNcbrzxxrD3AS2vmvEhHtlzTqWBFhpcZkbjxo3ljTfeMALHsvPdd9+5MpgjX758RhYNzcJht4YNGxrr7tKlS7YBdRocoYO1ncgaVKhQIfnggw9iDg7MpFlqNFhj3LhxWf49PT1dfvzxx7DlAM7SwJpvv/02bAYWs8455xzj/q3l+/TsTpm0DuPFYI54118mT54c9vmi9wMNntKsbLHS5/egQYOMZ0a4DFbwJ81cpte812UO+HdrwIybgzmyux+7qV2tbQYNAs9qQgMNRurVq5dRh7/rrruyzUp0ujlz5shbb70lM2fODFsP+vDDD6VAgQKWjgEAACBa1qYKAQAAAAA47kj6SXlh7pqI38uXkkP6NqnqyEt+IJHKpuaRD86rJ+VToxuwtO7gUbl78mLZHkXwBwAAQKx0oHqkweY6wDUWGzdulJEjR2b797JlyxqDbu0IMtAMCzoQUQctZ+XYsWNhZ1u3w9NPP217IEfmIPJYZ27VYIIFCxYY51cHHess1hdeeKExcEgHns6aNStiNgY/0sAKLSdmM1nocpEyvJw6W7AOkNXMGmYCOTJp1oNwGTEmTpxoet2aiSAczQqjs9JbobMD63b0HhMus8zXX/9/7N0HmGRlmTfupzpMT0/OOedATiIIigFFDKgoKqiIoivmHL41rO4uplXMOWEARVEExLQiEhUlw+Sccw4d63+d+j727+r0OT0zVaeru+/7uuqSy/NM1TvvqT51quf9vc9Po5LOP//8Iw5yPO7ss88udbXoyLJly2L58uzfe+Qt6VRRiSDH35/j5FrT0fs0+ZlIrjmVlvweKemmcKRBjsclu1kPGDCgw+MdhavoGklIsxJBjsclHa+SYGra4trkfqc7q8T9S9bnS7KA+UiCHH//8550/jjan3fg0GHhNIMGDarotGUFEXbu3BndITyeXAeTbnsdueGGG0r3p0ngNQkMJ/f0yfe3FStWlO4p77vvvlL3uiT4k3SKSurSghzJ52HStaOznQABAMpBZw4AAIBu5ksPr+pUB4K3HDs5Rver7G6cUC3G9GuIrzx5frzpT4/Fqr0HM+vX7jtYCnQkIRA/J0BH2jdkd8GqiLraqBk5/LD/WPuWbRGtbdEVasaOOuw/075jV8TBrgnWFUYMjULKgt5DKe7ZG8W9+6Oa55TqMWrUqNSFyIcb5kgWsHTU6SNZhJfsLJq8Zrkku5omi7U/9rGPHfL49ddfX1qIn7ao/Eiddtpp8eIXvzgqJelckCzsWbhw4RE/R7LYNHkki4ISNTU1MWPGjDj++ONLC72ThftJKKanSv6+yS7gaYu0OyNZdJWcj84sJEs6a8yZM+eoXm/KlCmlBWHJ7r2HcqSL5O++++549NFHOzye/Jx85CMfKc3b0Zo9e3ZcdtllpQX/HUm65yQdPCqxscP48eNLi37LIdll+Te/+U2Hx5PuLMmOzL1N8ndOuiMlYbFD+d3vflfRQEkief0nP/nJR/08yedSslt60nHkUB555JE4cODAEYfC6H6SLlfPetaz4te//vU/HSsWi6WAz6tf/erojipx/3L//ffHAw880OHxJOD4xje+8ahfJwmQJdf2Cy64INrauub7JPREu3fvTj1+tPfSWbKePytsUi2S7k6//OUvSx3uOupUlHyGJKHA5HG0r5V8zxk+/PB/JwcAcDSEOQAAALqR+7fsjp8u35RZd8rIQXHBVAsA6V1GNf7fQMcb//RYrNhzILN+/f6meNPtj5UCHcP7ln8hItD9NV2ZvgtqpRTGjIy+Hzj8RTnN37g2ihu3RFdo/PxHDvvPtPz8N9H+wGPRFRref0UUDjMs0Xr7vdH669uimueU6pHs5plm69atnX6uZJHNzTff3OHx5z//+XHsscdGuSULEr/yla8ccqzJ4vs//vGPce6555b9dT/4wQ9WtLtg0nEkCccki5XXrl1bludMuncsXry49LjuuutK4092uE7CCslu2ZXedTdvT3nKU+KUU0456uepq6sr7WD+s5/9LLVuxIgRcemll0Y5JN0gOgpzJF0tkqDVmDFjDus5f/GLX6QeT8IXkyZNinL5l3/5l9IOxBs2bDjk8SRolCxkO/XUU6Pc3vSmN2Xu8txZyW7HSQhq6dKlhzz+0EMPxUUXXRS90cte9rIOwxxJeKiS+vfvXwpPlUtyDewozNHa2loKQpXjekL3en8fKszx+Pu7u4Y5KnH/8vOf/zz1eNLxqVzB2lmzZpXOzQ9+8IOyPB8QsW/fvtRp6KgTYjk/09MkgcruIuleltwbJWHfJNR81113lb6DlbODY3KfmwS/AQC6wtFvAQMAAEAuDra2xb//bVlmXd/amvg/J0+v6AIoqFZJKCMJdMwY3Ll/DFu992Ap0LGz6dA7XQMAHInBgwenHj94MLuT2ONuuummaGo6dBebZKf/t771rVEJDQ0NpUV9HbnttvKHm5JFNMliwkpLugv85Cc/Ke0QXgnJzrB/+9vf4qMf/WicffbZ8eEPf7jDhffd0SWXXFK25+pMh4EkWFSuxaqnn3566vG0jjqHknTMSToldCQZ92te85oop6SLQdZi57QA2JFKdih+znOeU9bnTH4+OrJsWfbvP3qqZLf9mTNnHvJYEn7Zu3dvxV77RS96UVl3Cj/uuONSOxUd7s8c3V8S3uko5Pjggw9Gd1SJ+5ekQ8Zvf/vbDo8nr/f0pz+9rK+ZhAVra2vL+pzQmyWhxaxgcyUlXXfSdNT5sZolYeUk1JF0cjrnnHOO6rmSfz9LgsNJwPDaa68V5AAAupQwBwAAQDfx7YXrYu2+7EVfbzxmUozr3zeXMUE1GtpQH18+a17MHpK++9jjlu8+EG+5Y0Hsbk7/BzYAgM7KWnjeUTjjUJKFKh05+eSTY9y4cRU7MWeddVaHxyqxO3yyiDgvycL073znO/GRj3wkhgwZUrHXSXa8TRYHPeMZz4jPfOYzhxXkqdZdcc8444yyPd/06dMza17wgheU7fXGjh2bukvx6tWrD+v5HnjggdRdl5NFZpXozJKEKtIWAN55551lf82kC1C5QjWPmz17dofHytU5p7tKWxi+ZMmSir1uEp7K8++SdJKhd0muXdOmTTvksaTzV9IlqbupxP1L0rVmx44dqV1vKtFZLiv0CHReVlii0mGOrHBWdwxzJJ05kg4aSQfEjrrtHU4A/8c//nG84x3vKHX7SD6DAAC6SmXvDAEAACiLpbv2xQ8Wr8+sO374wLhw+hizTq83uKE+vnTWvHjrHQvi0R3Zu5Yu2rkv3nbngvjCk+ZF/3q7EAIAR9/VIk1nF/Tv37+/tGClI+edd15U0vz586Nfv36lcfyjdevWlRYZDh06tGyv95SnPCXylOzGmnQfSRbG/+AHP4gf/vCHsWXLloq8VrJY6mtf+1qpi8PnPve5XDqQVMJJJ51U1i6QHS3ofdyIESNi8uTJUU5TpkwpLZI9lK1btx7Wc/35z39OPf685z0vKhVGOvPMMzvskJOEUjZu3Bhjxowp62765Zb2c7B9+/bSIrve2nU0OccdWbVqVakTQLklwbaOOoIcjeQ577nnnrL8zNE73t+jRo2K7qQS9y9pny/JdbHcnZL+PiRSiUAg1efjH/946UHlJB120lS6E07W82d1DqkmDz30UOn9mnQ/LLfHHnus9Ei+q7385S+PN7zhDWXtUgYA0Bk6cwAAAFS59mIxPn7f8mgrFlPrGmoK8a8nT4+aXrrQAf7RwD518fmz5sZxwwd2anIe3b433nHXgjjYmv4PbQAAWZIFyGlqajr3zzOPPPJI6o6plVhc/Y+7xU6dOrXD4wsXLizbayULN5MdobvCwIEDS4t2kt1dv/CFL8S5554bjY2NFXmt5cuXx0UXXRR33XVXdEcnnHBCWZ8vCQvV19fn9nqJtE4sh7sj74IFC1IX21Zyh/OsDinJorRyqkR4YNiwYakLIPfs2RM9QXIdf7zjQBKES7qOZD3SVKpzQXKOKxGeSTvPu3btKvvrka/m5uZSuHPTpk2dfn+n7UZfqVBlpVTq/iW5B0wLJVaqM1s5u29Bb5cVpsgKexytrOevdGeQcmhvby91zUgC+JUIcvy9ZAODb37zm3HBBReUuu8BAOSp+u/MAAAAerlfrNgcD2/P7izwuvmTYtLAyiw4gu5qQH1dfO5Jc0sdOh7alr0Q6IGte+Lddy+KT58xJxpq7YEBAByZpqamo+rc0ZnF2MnioLSgRR4L39evz+4eeDhdQLpaEipIghzJI+me8pe//CXuvvvu0sKh5FykBWsOd6HQ61//+vjOd75T8UBOuVXiPZcEOjpa0F3urhyPv17aouTDsXjx4g6PTZo0qaK7+s6bNy9zbE996lPLdh1IW5B/pPr3739U19Jqk3QTSa4XDz74YCxdurTUISUJXpQ7lJIsnK+ESn2mpJ3n7naOe7Ok20/y/k52R0+CiY+/vw/Vvetof466k0rdvyxZsqTDY3PmzIlKGT16dKnrWqWuM9CbpAWW8+iMkRXmyBpfV0vG/573vCduuummTl27ki5JJ598csydO7d075o8ku9vyfeMpBNY8vn117/+tdTZLu2za82aNXHxxRfHpz71qXj2s59d5r8VAMChCXMAAABUsa0HmuNLj6zKrJs7tH+8bObYXMYE3U2/utr47Jlz4o1/eiwW7tyXWf+Xzbvi/fcsik88cXbUd3LXbACAw1mc2rdv305NWLIYOG0n6Ertzv73+vTpk7qws1wqsWj/aCTn6Oyzzy49Hl/kn3QiefTRR//nkSy0PNKAR/J8b3zjG+PGG28sncvuIulikmeYY/DgwRV5vY4czvlMOvBs2LChw+PJQrJKSp4/6aLQUSegrO4O5Qp1VTLMcbjhmq6wd+/e0s/xL3/5y9IuzskO0pWWhM0qoRI/b1nnuTuc494sCVZcf/31cfPNN5e920/e7+9KqdT9SxKW6Yowx+PPn4RZgaOT1fmi0mGOrPvaag9zvP/9788MciTh6SuuuCKe85znHPLvk3yXTe5Dkm5Gxx13XFxyySWlbmk/+tGPSl049u3b1+G5efe73106h0nQHwCg0oQ5AAAAqthnH1oZe1vSd1BKlpq//6TpUVso5DYu6I4dOj7/pLlxxZ8ei6W7s3eNvHPjzvjgX5bEv582K+pq/GwBAIcnazfjxsbOddRLWyieHHva057Wpadm9+7dVR0SKKdkIVCyACh5/H1oJ1m8fe+998Ydd9wR999//2E9Z7KQ6EMf+lB89atfje6iEou9a1IC1JV4X6S9XkfBiEPZtm1b6iK5KVOmRCUlczNixIjYsmXLIY+XM+w1aNCgqIS0c3G45yNvyYLzpLvOt771rbJ33shSqW4WXXWeDydYUO4uEIc7P5Wao2qTfL5/5StfiR/+8Ie5d0/pbt1aKvE5ldxHpn2+TJs2reyv+Y/PL8wBlQ+tVvozraOgwuF+J+0KyefPDTfckFpz/vnnx0c/+tHD7oSXhJSTAMh5550Xb3vb20qB/Y4CHUlnkNmzZ1fdxgMAQM8jzAEAAFCl7tq4I36/dltm3Utnjo3ZQ9L/YQCIGNxQH184a1684U+Pxso9BzKn5NZ12+M//rYsPnjK9KgRloJeqeH9V3TNC9fVHtEf63P5SyNa00Og1aT+Bc+MOO8pXfLahRFDD/vP1J11atSeOL8i46Hn6Whx9eNGjhzZqefJo/NGteyefbiLcKpBQ0NDPOEJTyg93vSmN8WaNWtKi46+//3vl4IanXHrrbfGPffcE6effnp0l79znjrbxaYrJAvLu/o9nbxGR9ebrPH1lPPQFZLOPO985ztjxYoVXfL6ler+Ue3n+ZOf/GT8/Oc/77LXT67zb37zm6Onu/POO+N973tfl92D5NHdppwqca1PwoJdGYCt9oAtdBdZAcCssMXRynr+SnXkOlrJd6pPf/rTqTUXXXRRKchxNKZOnRrXXHNNXHzxxR12nzpw4EC8973vLXXyKFc4FQDgUIQ5AAAAqtDB1rb45P3ZiwJGN/aJy+dNzGVM0BMM61sfXzxrXvzLbY/E2n3Zuz3+avWWGNJQF285dnIUBDqg16kZOyq6k5qRw6M7qRlanQsHOlIYOKD0gM7YuHFj6vGxY8d26nkqvcDnaDU3N+e2c213MHHixNJi30svvbS0W//Xvva1aGvLDtkltd0lzEHnw0x5hDnSFtx2t93tu4s//vGPpV2ck8V90NNcd9118eEPf7hTn11U7v4la7f+St8z9YR7MqgGWWGJXbt2VfT1szqHJR0qqtGXv/zl1OtgEqRPuhuWQ79+/UpdEl/4whfG1q1bD1mTdF/87W9/G8961rPK8poAAIciNgoAAFCFvrlgbWzYn73w4t0nTI1+R7h7N/RWIxv7xBfPmh9j+vXpVP2PlmyIqxevr/i4AICeIVl4sm7dutSaMWPG5N75ohKKxWLZnqu2tud8r0kW8b/1rW+Nq6++OoYOze4EdPvtt3e4eIjuG2bq6jBHtV8/uqO//e1v8Za3vEWQgx7plltuiQ9+8IOCHFVw/9LVny86c0B5DBs27Ki68FS6W2RnvqfkLZmTm266qcPjSXeM5LOqrq58e1ePHj06rrgivTPv9773vbK9HgDAoejMAQAAUGWW7NoXP1qSvXD8nPHD4qxx6f8gABza2P4N/69Dx6Ox9WBL5jR9+ZHVMbhPXVwwdbQpBQBSLVmyJDPkMH369E7Nop2xu7dTTjmltLPsq171qtSFmcn75bbbbosXvehFuY6Pympvb6/4FKddI5LFbpTP7t274x3veEenOp4ki7uPOeaY0iO53k+YMCFGjBhRWjSZ7ACdPJLzk7YQ8Qtf+EJ88YtfdArJxZo1a+IDH/hAp0Kaffr0ieOPPz7mz58fU6dOLb2/hw8f/j/v7759+5Z+BtJCDu973/vi5z//eZn/Fr1Hpa/vPj+gPMaNG5d6vNJh7qznHz9+fFSbX//616nfm84777yYOXNm2V/3JS95SXzjG9+IDRs2HPL4fffdFytXrowpU6aU/bUBABLCHAAAAFWkvViMK+9bHm0Z/3aadON4x/F+cQxHY+KAxlKg4w1/ejR2NLVm1n/ivuWlQMc544ebeACgQ/fff3/q7BQKhdICyM5IFkzSvZ100knx+te/vrQwO80DDzwgzNHNNDQ0pB7fu3dvxceQ9hquH+WVBCs2btyYWpMsLrz00kvjmc985lHvbF/OzkeQ5eMf/3ips1iaE088MV75ylfGOeecE42NjUc1qd7f6bKu35X+fNmzZ09Fn5/qsH379syf++4gCZL1798/qlHSRWfIkCGxc+fOQx5fu3ZtRV8/6/mTMF61uffee1OPn3vuuRV53fr6+tLn249+9KPUsQlzAACVIswBAABQRa5fvike3Z79D3JXzJ8YoxrTF44A2aYO6hdfOGteXHHbo7G7peNdbRPJvrof/MuSuOrMujhl1GDTCwAc0p133pk6M5MmTer0It+0xZIveMELSosvqX7J4u7vfve7qYsjFy9enOuYOHrJDvRp9u3bV/FpTlvQe7SLrfnfC16vvfba1Cm57LLL4l3veldqN4LD7QQCeVi0aFH8/ve/7/B40kHmne98Z+k9Xi7e3+mS7iZdGebII4xI1/vkJz/ZIzrkXHnllfHCF74wqtXEiRM7DHNs2bKlFKjJuqc8UqtXr07dYKAaO3OkbYyQdA164hOfWLHXPvPMM1PDHEl3jhe/+MUVe30AoHcT5gAAAKgSWw40x5cf6fgX7I+bP3RAvHD6mFzGRM9TGDIo+rz6xZk1vcnMwf3jM2fOjTfd/lgcbEsiGx1raS/Gu+9eGF8+e37MHTogtzECAN1DsvgtazfRU089tdPPN2zYsFi1atUhj61Zs+awx0fX7cp7xhlnxG9+85sOa9avX5/rmDh6yc9nVy9WTnuN4cN1FCyXm2++OZqamjo8/sY3vjHe8pa3RDlZ7P7PkgCjEGP5ZS3m/uhHP1r2zlHe3+myrt/CHNB9zJo1Kx5++OEOjy9btiyOPfbYsr9ue3t7rFixInWDgWoL/iZdm5KAS9q1cfDgym2wNHXq1NTjWR3aAACORs1R/WkAAADK5qqHVsa+1vTOALWFiPedNC1qCwUzzxEpNPaN2hPnpz6Smt7m2OED4xOnz466Tvxs7W9tj7fdsSBW7TmQy9gAgO7jlltuiQMH0u8RzjnnnE4/37hx4zo8JszRvZxyyimpx9O6dlCdhg4dGn369EldnFdJO3bsKHWM6MioUaMq+vq9yW233dbhsblz58YVV1xR9te02J1qeH8/5SlPKXuQI+H9nR0WTDqidKSjoG+5VPr5oTeZP39+6vHHHnusIq+bBDmSrh9p9y/VJvlsaGtrS7337sqgdkcdVgAAykGYAwAAoArcu3lX/H7ttsy6l84YG7OG9M9lTNDbnD5mSHz41BnRmajUzubWeMsdj8Wm/R3vzgoA9D7XXntt6vGGhoY488wzO/18kydP7vDY5s2bBQC6kZEjR6YeT9v1n+o1ceLEDo8tWLCgoq+d9fzJjsuUxwMPPNDhsRe/+MWpi66P1OLFi8v+nPCPdu3aFcuXL+9wYl7+8peXfdKam5tj5cqVTkaKQqEQEyZM6PD4woULKzp/lX5+6E2ywhxpXTuORtbzZo2rK6SFTx7vdlhJWc9f6a5IAEDvJswBAADQxVra2+PTD3Tc8vpxY/r1icvndbxQBDh6504cEe86Ib2l+uM27m+Ot96xIHY3t5p6ACD+8Ic/xCOPPJI6E+edd140NjZ2eraOPfbYDo8Vi8W4/fbbzXw3MXDgwNTjffv2vu54PcGsWbM6PLZhw4ZS94xKydrJefbs2RV77d5k69atqcG5pz/96WV/zU2bNsW6devK/rzwj9JCFcnn0hlnnFH2SUvulZJAB+lmzJjRJWGLpONTEhjuTWpq0pdNJffccKSOOeaY6N+/48257rnnnopM7t133516/AlPeEJUm2Tjg67s6pT1/FnjAwA4GsIcAAAAXeyaJRti5Z4DmXXvPmFaNNbV5jIm6M0unD4mLp/b8Q6If2/FngPxnrsXRlNbe8XHBQBUr2RR4qc//enMuosvvviwnvf4448v7c7ckVtvvfWwno+us3PnztTj/fr1y20slE/WrsZ33HFHxab7T3/6U4fHkuvGvHnzKvbavUnaoub6+voYNWpU2V/zvvvuK/tzwuG+v5P3dvIeLzfv785Ju4YvW7Ystm3L7vB8JO69997obfr06ZN6XPiIo5F07zr11FM7PL5mzZqydytqb2+PO++8s8PjgwYNKoVMqk1WZ4xKhqQfD7OlSQvlAAAcrfL3fAUAAKDTNu1vim8tWJtZd874YfGksUPNLOTkNXMnxM7m1rhu2cbM2vu37omP/nVpfOy0mVGTstgSAOi5rrrqqtLCujQnnXRSHHfccYf1vMOHDy/9mQcffPCQx2+77bbYt2+fhSXdQNYu1+PGjcttLJTP6aefnnr8l7/8ZTz3uc8t+5QnXT/SFtwmHUOGDRtW9tftjQ4c6HjzjSFDhqQG7o7UDTfcUPbnhMN9fyf3IJXg/d05abvmt7W1xS233BKXXHJJlNuNN94YvU3WAu2mpqYeuSP/xz/+8dKDyjv77LPjj3/8Y4fHf/WrX8UVV1xRttdL7hG3bNnS4fEzzzwzamtrqzJYlXz2dBRWS/7/5DtVJYK0iQULFqQeHzt2bEVeFwAgoTMHAABAF7rqoZVxMGNH/8bamnj7cVNyGxPwf3eyfcfxU+IZEzq3eOH3a7fFFx5eZeoAoBdKFr19+9vfzry3eO9733tEz3/uued2eGzXrl3xve9974iel3z95S9/ST0+efLk3MZCeTtzpIUm7rrrrti6dWtFrjvJrssdedKTnlT21+yt0joT7N+/v+yvt2rVqlJQD7r6/Z2ERcvt7rvvjsWLF5f9eXuiJMybtkv9TTfdVPbX3L17d6+8/mSFH5P7bTga5513XqlDR1rIrVgslm2Sr7/++tTjz3nOc6JaTZmS/u9gyb11paR1M0n4vgYAVJIwBwAAQBe5Z9PO+MO69NbNj3cIGN2v5+3+BdUu6bLx4VNnxOmjB3eq/kdLNsS1SzZUfFwAQPX43e9+F+9///szF98kO/OfcMIJR/QaL3jBC0q7lHYkCZLs3LnziJ6bfOzduzdzcdCJJ57odHRDNTU18exnP7vD462trfHlL3+57Ittv/Od76TWVKIbSG+Vtsg3WeyenI9y+sY3vpEa1IG83t+bNm0q6+LixNe//vWyPl9Pltz7Pe1pT+vw+P333x9/+9vfyvqaV199dTQ3N0dvk9Udbf369bmNhZ57rT3jjDM6PL5y5cr4wx/+UJbXSjpX3HzzzR0eHzx4cKlTSLU6/vjjU49XIsj2+Pe1tO4pnRkbAMDREOYAAADoAs1t7fHpB1Zk1k0Z2Bgvnal9M3SV+pqa+Pjps+OYYR3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+ "text/plain": [ + "
" ] }, "metadata": { "image/png": { - "height": 391, - "width": 661 + "height": 559, + "width": 1593 } }, "output_type": "display_data" } ], "source": [ - "fig, ax = plt.subplots(figsize=(6.5, 3.8))\n", - "az.plot_posterior(idata_seg, var_names=[\"tau\"], ax=ax)\n", + "az.plot_dist(idata_seg, var_names=[\"tau\"])\n", + "ax = plt.gcf().axes[0]\n", "ax.axvline(\n", " np.sqrt(tau2_DL),\n", " color=\"C1\",\n", @@ -743,7 +727,7 @@ " label=f\"DerSimonian–Laird τ = {np.sqrt(tau2_DL):.2f}\",\n", ")\n", "ax.set_title(r\"Posterior of between-segment SD $\\tau$ (one study, six segments)\")\n", - "ax.legend();" + "ax.legend()" ] }, { @@ -751,7 +735,7 @@ "id": "2e5f189d", "metadata": {}, "source": [ - "Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor throughout was the segment, and that is the only thing about to change. Replace \"segment\" with \"study\" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution.\n", + "Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor was the segment. Replace it with \"study\" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution. The substitution is mechanical; what it opens is not. Once studies replace segments, τ becomes the quantity the replication programme was designed to estimate.\n", "\n", "## The hierarchical re-framing\n", "\n", @@ -761,7 +745,7 @@ "\\theta_k \\sim \\mathcal{N}(\\mu, \\tau), \\qquad \\hat d_k \\mid \\theta_k \\sim \\mathcal{N}(\\theta_k, s_k),\n", "$$\n", "\n", - "where $\\mu$ is the population mean effect across markets and $\\tau$ is the between-market standard deviation. The substantive story lives in both: $\\mu$ tells the team what to expect *on average* from the redesign; $\\tau$ tells them how variable that expectation is when the next market is queried. Neither quantity is recoverable from any single experiment. Both are recoverable from the joint." + "where $\\mu$ is the population mean effect across markets and $\\tau$ is the between-market standard deviation. $\\mu$ tells the team what to expect on average; $\\tau$ tells them how variable that expectation is across markets. Neither quantity is recoverable from any single experiment. Both are recoverable from the joint." ] }, { @@ -770,10 +754,10 @@ "id": "b2a086f6", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:21.212963Z", - "iopub.status.busy": "2026-05-28T17:00:21.212872Z", - "iopub.status.idle": "2026-05-28T17:00:21.220209Z", - "shell.execute_reply": "2026-05-28T17:00:21.219697Z" + "iopub.execute_input": "2026-06-01T17:59:32.156776Z", + "iopub.status.busy": "2026-06-01T17:59:32.156571Z", + "iopub.status.idle": "2026-06-01T17:59:32.165025Z", + "shell.execute_reply": "2026-06-01T17:59:32.164553Z" } }, "outputs": [ @@ -945,7 +929,7 @@ "id": "958dfc38", "metadata": {}, "source": [ - "The per-market `z_score` column is what a frequentist replication exercise would consult: anything above 1.96 in absolute value counts as \"significant\", anything below does not. The columns disagree about how many markets \"worked\"; the underlying true effects disagree less. This is the gap the hierarchical model is built to close.\n", + "The per-market `z_score` column is what a frequentist replication exercise would consult: anything above 1.96 in absolute value counts as \"significant\", anything below does not. The columns disagree about how many markets \"worked\"; the underlying true effects disagree less. This is the gap the hierarchical model closes. The quantity no single experiment can recover is τ.\n", "\n", "## No pooling, complete pooling, partial pooling\n", "\n", @@ -958,10 +942,10 @@ "id": "ea8e78de", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:21.221575Z", - "iopub.status.busy": "2026-05-28T17:00:21.221477Z", - "iopub.status.idle": "2026-05-28T17:00:23.739435Z", - "shell.execute_reply": "2026-05-28T17:00:23.738976Z" + "iopub.execute_input": "2026-06-01T17:59:32.166252Z", + "iopub.status.busy": "2026-06-01T17:59:32.166169Z", + "iopub.status.idle": "2026-06-01T17:59:33.447380Z", + "shell.execute_reply": "2026-06-01T17:59:33.446901Z" } }, "outputs": [ @@ -969,70 +953,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [mu]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 0 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [mu, tau, theta_offset]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 2_000 tune and 2_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + "NUTS[nutpie]: [mu]\n", + "NUTS[nutpie]: [mu, tau, theta_offset]\n" ] } ], @@ -1077,24 +999,24 @@ "id": "35d9a55e", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:23.741306Z", - "iopub.status.busy": "2026-05-28T17:00:23.741162Z", - "iopub.status.idle": "2026-05-28T17:00:23.857692Z", - "shell.execute_reply": "2026-05-28T17:00:23.857186Z" + "iopub.execute_input": "2026-06-01T17:59:33.448789Z", + "iopub.status.busy": "2026-06-01T17:59:33.448713Z", + "iopub.status.idle": "2026-06-01T17:59:33.646494Z", + "shell.execute_reply": "2026-06-01T17:59:33.646063Z" } }, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 538, - "width": 740 + "height": 1066, + "width": 1532 } }, "output_type": "display_data" @@ -1106,8 +1028,8 @@ "complete_pool_mean = idata_complete.posterior[\"mu\"].mean().item()\n", "complete_pool_se = idata_complete.posterior[\"mu\"].std().item()\n", "partial_pool_summary = az.summary(idata_partial, var_names=[\"theta\"], kind=\"stats\")\n", - "partial_pool_mean = partial_pool_summary[\"mean\"].values\n", - "partial_pool_sd = partial_pool_summary[\"sd\"].values\n", + "partial_pool_mean = partial_pool_summary[\"mean\"].values.astype(float)\n", + "partial_pool_sd = partial_pool_summary[\"sd\"].values.astype(float)\n", "\n", "forest = pd.DataFrame(\n", " {\n", @@ -1177,24 +1099,24 @@ "id": "261a8315", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:23.859356Z", - "iopub.status.busy": "2026-05-28T17:00:23.859242Z", - "iopub.status.idle": "2026-05-28T17:00:24.025864Z", - "shell.execute_reply": "2026-05-28T17:00:24.025453Z" + "iopub.execute_input": "2026-06-01T17:59:33.647890Z", + "iopub.status.busy": "2026-06-01T17:59:33.647802Z", + "iopub.status.idle": "2026-06-01T17:59:33.932721Z", + "shell.execute_reply": "2026-06-01T17:59:33.932284Z" } }, "outputs": [ { "data": { - "image/png": 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nvd5pGtD4C3Cys7MJcAAAAAAAiEOEOwAAAFGkAY1W2lht06zwxjrvVoCTk5NjTrm5uXUCHL2MChwAAAAAABIL4Q4AAIDDNKDRuW6857+xTtpizQ1aaeMrvNGf2mINAAAAAAAkB8IdAACAMAOcqqoqvwFOdXW1awGOFdhokGNV4+jPjIwMVx4TAAAAAADEF8IdAACAAAIFOHqdKx/QMjPrVN9Y4Y0u63UAAAAAACC1MToAAABSnlbZ+AtwtL2aG7TKxjPA8TxlZWWl/N8EAAAAAAD4R7gDAABSgs5z4y/AqaiocC3AsSpufAU4aWlprjwuAAAAAABIboQ7AAAgqQKc8vJyvwGOzpPjtPT0dL8Bjs6PQ4ADAAAAAACcRrgDAAASigY0GtSUlpbWC3A02HEjwNGAxl+Ao5cT4AAAAAAAgGgi3AEAAHEb4FiBjWeQo+e1QscNgQIcrdABAAAAAACIB4Q7AAAgZgFOVVWVCWw0vPEMcXS5urralcfVVmn+AhydIwcAAAAAACDeEe4AAABXWQGOZ4hjLbsV4GRlZfkNcDIz+fgDAAAAAAASG6MbAADAkQDHCm28g5zKykpXnmGtssnNzbVDG2tZTwQ4AAAAAAAgmRHuAACAoGiVjXeAY510fhy3AhzP8MYzxNEAJy0tjb8eAAAAAABIOYQ7AADAVlNTEzDA0XlynJaenu4zvNFlnR+HAAcAAAAAAKAuwh0AAFKMBjT+Ahy93I0ARwMaDWu858DRIIcABwAAAAAAIDSEOwAAJCENaLTSxl+AoxU6bgQ4GtR4hzca6uhJK3QAAAAAAAAQOcIdAAASOMCprKz0G+DoHDlu8A5wPE8EOAAAt23cuFHWrFkjRUVFUlJSYg5YaN68ueTn58t+++0n7dq1448Q47/PaaedZp+/7bbb5A9/+AN/E8Td6+/dd9+Ve+65xz7/xhtvSIcOHaKynQDcsX37dlm6dKn8+uuvsnv3bmnVqpV5Xx944IFmPleEPuawfv16Wb16tWzZssU8pzoe0KJFC+nRo4f06tWL5zXGCHcAAIhz/gIcPbkV4GRlZfkNcPhQDACIxYDt9OnT5csvv5S1a9cGvG1BQYEMGTJETjzxRDnooIOito0AACA21q1bJ48//rh88cUX5vuzNw15Tj31VBk9erT5rhtL//73v2XSpEl1Luvfv7888cQTQd3/8MMPD/uxP/vsM8nMDBwH7Nq1S+bNm2eey0WLFpkDafxp0qSJnHzyyXLOOecQjscI4Q4AAHGgqqrKrrgpLS2tE+DodW7QD3X+ApyGPvABABCtI3Cffvppefvtt30O1viiFT1vvvmmOfXr10/Gjh1rfiL1/Pjjj2aAynL22WdLs2bNYrpNAABnvffee3L//feb79H+bNu2TZ599ln59NNPZeLEiTELIvRgFf1cE680zLnmmmuC/syllTyvvvqqzJw5U6677joqdWOAkRsAAKJEq2ys8MYzxNFlnR/HDVplEyjA0XlyAACIR9oGRAcK9GhcT/q/q3fv3rLPPvuYVmx6BO5vv/1mWrB8++235n+rRVuzXHbZZabiB6kZ7uhgnmX48OGEOwCQRLS65O67764zp2znzp1l0KBBpmWrfpbQahX9zm39X7j++uvlmWeeMVUn0dZQCBUq/UwUSmv0hr7/a9WOd7CjbdgOOOAA6dq1q/ncpWMXP//8syxevNh+Xvfs2WPaXOpnsNNPPz3M3wbhINwBAMBB+qHSCmz0p2eQ41aAox/m/AU4OuBFgAMASDSrVq0yFTfFxcX2ZY0bN5Y///nPcsopp5jWa77o/9wFCxbISy+9JN98800UtxhAotG5eJgPCkhcWo2j82pZwY5+773qqqtMlaZn4KGfJW699VYTRiidP0ardzQUinaFkXWwibaJ0+2P1EUXXSSXXnqpOE2fSw3IRo4cKUceeaTk5OTUu40eWPPggw/Kxx9/bF/20EMPmZa4OhcPooNwxwHae1B3EJs3bzYJZ5s2baRTp06mX2Is5iXQkriFCxeaia502/SLT8eOHWXgwIEmbQUARD6poBXeWCcrxNGTXu/Ghyt/AY7u2wlwAADJQv+n/vWvf60T7Oy///7yj3/8Q1q2bBnwvjr4cNRRR5mTtl7R+2hFDwAASC7PP/+8qRixaMhx7rnn1rudVps8/PDD5gCRNWvWmMs++ugjOf/882W//faLyrbq+Oyjjz5qlvW7+9VXXy1/+9vfJN7otg0ePFj+8pe/NBjQ6Gey++67T26//Xb54IMPzGUatOncQRryIDoIdyKgSa8mlJ988onPXoStW7eWs846Sy6//PKohCr6pUXfPLNnz66zc7O0aNFC/vjHP5reiU2bNnV9ewAgkWlAo5U2ngGO58mtAEcHpfSUm5tbJ8DRywhwAACpQL9jrV27tk6wM3ny5JDbp/zud78z973ppptc2EoAABArWjUyY8YM+7weZD9q1Ci/t9fv0zfccIOpClb6fV7DIa3giQYNlzTgUSNGjDBtzuLREUccIUcffXRI99E2d9r6zhqL/vrrr03xA2PP0UG4Eyad0POOO+7wGaJYtm7dKo899pgpT9MvI7qjcYu+iXQn5Xl0m6/JSKdOnWrCKN2ePn36uLY9AJAI9AOdhvP+AhzPvr1O0sBfwxvPEEeX9Wco/XIBAEg2Or+OTspr0f+P+r0r3L74elTpP//5Twe3EAAAxJpW53oeaH/qqaeaOWUD0TZjOm+MdQDJ/PnzTbWwfid3k7Zi0wPxrc8lWhWzc+dOiUcNPYe+aDHBYYcdJnPnzjXnq6qqzNxGAwYMcGEL4Y1wJwzz5s2Tm2++uc6gX7du3cwLOS8vz3wh0Re0NZHn8uXLTfXO9OnTXUktv//+e5M8e07Ipa3hNGnVHo6bNm0y27Njxw5z3S+//GJKFV977TVp166d49sDAPEW4OiHC38BTnV1tWsBjhXYeAY5+jMWLTsBAEgE//73v+tUx5533nnSpUuXiNYZShcF/Y6n39904EePsNVt0XYu+n1PD45z6iAMXa9+j9PvjtpzX+fI6969uxkI0WV/9LPLkiVLzPbp9z8dUNHt6t27tyPbpe1lda4ibTmu3x/1++0+++wjffv2jWoFsR60uGzZMvPc6HZouKcDYtrHv6HWfNFog/7tt9+azhl6AKV+1tNW6HoUdocOHSTWtOXQTz/9ZLZPP3Pq2IC+rvRvGQk9Cltfe3oQq/5NdA4s/b0PPPBA8xiR0rby+jfXv70eRKsToet6Dz744JhMeh6q7777zn4/6+d9HWvR571Zs2Zhr1MHfxctWmSeG+0ooN1hdF/k1Ps9UrqPtN4L+jfT96ZOT6DTAgSi+y99vrTyQgeS9bk65JBDInquAr03tRIhkoOt9bW/cuVKM5amr30NFHRsT9et+0YnXv+e9HX0ww8/2C1F9XnV9wHjd/XDHU/HHXdcUM+v3k4rdqz/eV999ZUcc8wx4hb9X/33v//dPq/dlPS1Hq/hTri832NFRUUx25ZUQ7gTIt25XnvttXUm69Iyf+3b6PlBX1/E2j9RS9GU/iPQHoRO9xzUD/dXXnllnWBHJ9PSbfT8EqP/jLSX46xZs8x5/UCmO5RXXnnF0e0BgFgJFODodW7QLyOerdOs8EaXwzniBQCAVKaDlzrZsEW/X+mRuNGggywvvPCCvPPOO2ZQ0BcdHNft0bYvwQw233XXXfb3Lx2Ue+utt8z3yDfeeMN8D1u/fn29+2iQpN/vvCd5188zzz77rLz++us+u0doMPR///d/ZgCwIdoORsMbdfLJJ5vvqbr+J598Ut59913z3dGbDipffPHF5vd3M+T573//a/4OGrD5a4Grg6l6sKIeXOnP4Ycf7vPy0047ze99Hn/8cTNPrT8abDzzzDMm/PJ3cJD+HS688EL5/e9/79rzpH+je+65xz6vrycNlTQE0Co1DQ29acij81DpGED79u1DerzCwkKZMmWKLFiwwO/vve+++5pxiHAGSOfMmSP/+te/TCDli36mPvTQQ+WKK64wjxON59KXMWPGmL+90gBD55RQus/Q16yv97M+7/oe06P09b0dLB2v0W4r2gXG1/cYDXj0/Ths2LB6r3e93KnJ1Tdu3FjnPaMT1+u+SUMO/f31/eq9ffq615aYN954o9lveNLnT7vb+HqN6t/5jDPOMAdG63epUATz3tS5Q3TcbujQoUG9N3UaBp2T5fPPPzcVCIE6OvTo0cPM83LSSScFdQDA008/bfbnnlUdSoMp3Q8tXbrU5/10/6TvYSaqF/v5slhzjQdDA2nv14+b4Y7uP/Wge6X/t6z3bbLx/mzCeEj0MPIUIv0HpkcDWK666irz4c2b7lj0n8vIkSNl1apV5jL9YH/JJZeYvs9OmTZtmr2TUH/605989pTWowq0d7UeBaNlh0r/8ek/K/3gCQCJQD+s+wtwfM195gT9UuYZ4HieAh1ZCwAAQqMD+noUrUWrNNq2bev606jVAjoQafXC90ev10FcHRDW71ahHj2vg6A6OKptsv3R72s62Kzhi353VFoJoAcOWt8rffn555/Nd1M9OliPUg/nAMZA69fBZp2XQAc6dfJkpz8Dabimz40eQd0QPeJfnw/9rq19/t0eQNLQccKECXWCx0B/Bz2o8sMPPzThXqiD1JFUvOmgsL+Bbb1cO5Dowaf6GtGwJBga+OlrvqG5JnXwW7ub6KD+3XffHdTvrQOBf/3rX+2B7UDvmy+++MLcTt8TGiLFA/3uoS0jNZzyR593DX908Fj/PsFUeOj+SN+PvkJWz+osfZ1pCKDvgWjSEFGDZM9xMU/6WtHQZ8WKFWZMzPqdNdCeNGmS35BE/84vv/yyOTBa96/BVFyG8t7U8FD3MTrpezDvTQ3IAv0NPOm+U1/3GsbpusOpNHvppZdMOBuoq4Q+95dddpn84x//MO3FUpn+T/L8+4QS/O6333713k9ufq559dVXzbIeBKqfNZKVvnc9OV3RBv8Id0KgH6qtN6XS9gC6Y/VH37j6D3f06NH2PzkNh/QIDKc+TOg/S4uW9QWaLFSPILjzzjvlxBNPtP+h6j8Pwh0A8UT3T/4CHP0A7wbdPwYKcKLZhgQAgFSlA6CenDwoLtCRvxoUWC21re9xOvit3/f0M4C2ENLQwfocoq2E9Eh8PQI9lG3UAUsr2LHaEGkbbe3CoIN2npUL+j1PqwO01ZceqW0FLzqA1a9fP9OySrtF6KC31bpHvx/q9z1tBx5sCy4dSLz11lvt9WtVh/7uerCith/S7fIc+NI2ODpA6tliJlIaaGlVhOfj6Ocv/T31iHj9nqtBgD4/nkfmv/nmmyYM1Mojb1YLXP0O7jmYHKg1rq/Pe7p+fX14vjb1c6P+XfRvoW3x9Da67QsXLrTDSR3c1kF3Hcx2ux2vvgZ0jEF/Vw26dNBXnzfdTm3vpKGItV36WtPBRQ0atAIqEO068p///KfeYJ0eea6vWw3kNFzQYMfz9aHPl64/UOim7yVtLa8Dn56s17f+zXX8Rd931utb/45PPfWU+R20ui3W9D1gBTtaoaLvZ/2pr099rerrwXqtaqWLDv43NBak4aA+f55HwOt7Qdet7RH1OdUKIf2barii41M6f0i06O+hrwvdPt0WbTvXs2dPE8Todulr0dp2/buNHz/ejIFp+PLII4/YBx7rPsaqstDnSkNH632qFWIaKDZUfRTKe1P3Y9Y+Ppz3pv4v0Odf94/ajlDfaxr262vfs8pQw2/dB99///0hPa86z5z12tBgSN/D2uJK//bez6u+/nWcUcMy/R1TlTVnjiWUlnX6/02fW+vgULfCHQ0sNXy09gM6NuzGXOyLFy82Y8H6XtLXpb6u9bXRuXNn8zlC29C5OQe8VeXnWXGm+3CnKy3hH+FOCPQft+eR4WeeeWaDRywNHjzYlGbrP2mlR8voTln/IURKP+joB2GLlsc2tHPXf/x6JNdnn31mzms5rP6D1jc9AESLfnjXD9v+ApyGjg4Mh37Yt1qmeZ/0CwkBDgAAseXdlsnteSV0cFQHHz2DHZ23VCsQdPDHkwY69957rxlkU/qdTkOFF198MajvdjpQrWGEfn/Uo/K1NZp3+x4dqNUAyLOVix5hrEfD6qC6bqt32zD9PKWDR++//745ry3ldNBPW1gFQ+dm1c9eul0aImkLJu/PRHo0uv7u1pH6+p1Wq5e8W8eFQz/z6WCoNbimj60VOVqh4f03sAaQdFt0sNbqjqEDodr6ypMOsvpqu6XPcSjz4ujfw3PwWAfJNJTwtQ4N23TgW7tjKN1GbTembbLcpIPU+jxqpZu+Rry3TccM9DWig9rWa0arC6ZOnWo+G/uit/UMdvT1oQPp2inE+/Wh4xK6Pn2PWIGpvnYDBTB6kKlnsKOhiL6fNMTw/s6g26ED39YAqW63DliGWqHmJK0e06BRnz99P59yyin1ggId+NcQQSsMrNBCAx9/FRf6+2kA5Bns6G31efE+Al7fiw8//LB5fevfP1q0ukQHrPX515DXuw2W/q76fFhH8OtzpJVL1vQEun/R14V3ZYu+ZvS5sioxtEONtjkLVAET7HtTX6/63Op+wGqTGcx7U8PPY489Vo488kgTZvqj+yT9/awxNn3vaOVeKK23tCpSnXXWWeYAcu/fW9/DGsLrIL51XkP8QAebh+P000+v0xnIaVp159T+0HpfhVMloq8J3efo387Xupyir2Prc422Ujz//PNdeRyrXaQnDd81GNTOTbo/1naE+j/e1/9VJ2ibQc8xHG1zR1u26HFmNsgUoR9qPWkFTDBOOOEEe1k/SFkfNJ3eHs/HCcR7uwOVEQNAuPSfu+7z9OgRbS2iX9q1PF8/hOuXG/0QrxNF6lE3OkmoDkbo7SMJdvSDmoY1erSqHr1jTTiq/ef1y6J+6dVBEg26tc2LBuL6pYxgBwCSh1YbhHsKVCEayXo9wwNfX8DDXa/nvJvedPAvlHXFA++5brzna3CjlZU194zSllI6CO5r8EMntNajsT3nedGBk2DnMLXmpdABRh3g9DUvg843cfzxx9vn9ShYnWNHj4DV9li+5oPRzzHa2spzHhUr6AmG9ZrXAWRfA/fWoKm2AfIcvNaqJScqqvWIdc+2XDowrC2f/A1A6cDto48+agaXPaucArUyCpcOAM+YMcM+f84555iWdP7CId1m/ft6/g11MNztSbP176Cfd7Uywte26Vwvut2e87JoRY++tnzRQMWqsrBo+zEd+PX1+tD3hAYMniGnvresgVNv+r7xDI60Ck0rfbyDHaXvk7PPPtsMbHvSwXQ3DgYLlh70q+8HDRg0qPVVAaJHrWtY48kKF/yN7+j8Rp7zgujv6WvQWgf/NVzRUNOtzgb+9mNaGaPvQV/zm+g+W39nz/2b7lP1f5UGF/re9hXY6Hc0DWYs+p1Qg2en3pu6/9YwzHPMrKH3pr4HdI6xQMGO0sfU/w0aAlk0eAn19aSVSrr/8/X86HtYAyDPisxAr6VI/r66L3XrFGjeokjndwn1AHrP2+u2ebaEdYLuY59//vk6c7XHqqW7/n7ajlDnnPI131WktPLO8/Wov6fOTYjooXInBNbRQUp38MFWu3h+8FQ6qOnEBFp61IdFP0xo+XIwtHTWe3us1nEAEAr9UqVfKKwqHP3grsvWTyc/wHnyV4GjlwcziSUAIHnpZMzh0rk0PQdmPd1www1hD9LqoLkOjPqiVQsbNmwIa736ncLf/BM66Og5UNgQnesg1rxDpnDmLQhlME0raTwfSyt2ArXp0aNQdZBZBymtYE0nYNdBjGCOUNUjZ4cMGRLwNjpQrIMwFv0spVU4gapNtAJZB3mtCbo1sNJKoYYGJS26TQ19P9XvkFpR89prr5nzevCODkYHe8Cjv8+RemSzZ9cL7Y7REH2udZBYj+zXdWiIoAGR5+CqE7RCxKJtzoJtBXbdddeZ9mT6GtEByNmzZ/t9/ztBBw5vueUWyc3NDfic6W30+bUGMfX1r4Pi3oGNViB4BjPaxl1fu4Ho86OD0zrobw0m6mtl3Lhx9W6rl3t+R9DXt7a9CkRf3zrYr8+rFRDpQbNHHXWUxIru1xua90QPMOvTp4+9L9aKH390X+L9N21o3hkNA/R58A7G3aLbpfvAQNul7cv097aqTPRvrQfVeYY3vui+RAMV62AIfa78VQdG8t7Uyhqn35v6/VOrDa0DuXXb9W8SbNs0DQJ9zeXtSUPQ4cOHm1AqnP18svE+uCWYOZoC3V7X56+SMVT6f0nDOGtfq69j73FhJ2irNf3/rQd+6PtOQ0B9Ler/Z93naBWp/p+2Dn7QCiWtkNMDIpxq06avQf0c6xm262ciukNFF+FOkPSocs8vc/oPOljefZgDTVQZLP0H6dkXUo9CD/bLj7aJ00FQ65+mE9sDIHnpP2o9isdXeKOXuXGkpPWBy1+A43bvcgAAEH3eR+IGGqyOlA6+aRstix7RrUd3N0SPotfBbm01ZA1s6JGweuR5QzQcCea7ow7OWIPf+rlHB/Qa4j1/yurVq4Me9AsmUFEaalnhjtL5gyIJd7Qtl+d32mC3Q+lAlg7qWu2f9EBMJ8MdbTHmWVGkA/nBtpjRo+t10N8KInTb3Ax3dCDde4JwX3SAXVvl6ID2/2vvPsDlqsrF/68jRFQEEpASkovxhg4iIEivCVKk65UaQHpHkN57QEpooYiUEEBQepdmIjV0CEiognQE4V68Xgnq+T/f/f/tcc3OlD1zZp85c8738zznIXOY2WfPntkza7/vet8V/t/6KcQCWDMllrZvS5EAyoP398UXX1w6j9lOpeROvH2q0vK299tmm21KxzTdTjuTO3lfU4K6aXKHY851Vfa9xDGjq0H8GOI29XD8+OzKro1UFD7n8uwX1T1pcge8xvWqFrjO45xOKwv4DOukc5N9j/E88rYOrFbNmcX7Ik3ugM/PViZ3brrpptApshVrjVbFZJM7razcoaosff/znquX2GwGiXTWrqpUTck4hR8SP3x+kygmpg0SPyRj+KzuKY4Z205bcqYVh/USlWo9kzs5pWvmpBrp08uHbbxYV7UvqUYwuy/+8Glkfzj5aVeUDqQZYLBv7SoRlNQ3pAmcOImT/ruoBA6fO9USOPZolSRpYMm2VanVdq6npk6dWnabtXbyYh2GNLmTbqtecoeJKfUWsAfjIAK26Ux8ZnTzu3qyrZvyVplxzPPOKGYmLi1v0+vInrZ3idcJ4Bo1T4Isuz9pcof1TVopXssjTaA0um/V1pJqtUYSHLQeTJM76WuYTe7E5wYJTwL1efA+pfoqbftOdQ1rgzCbPEUQMK4KolVc3hn3vAZsK113OHsO9yZiPPWqjVJxy0QmzdEyM1vRQfInrmaK2z/Ww317M7mTR/bzKG+XGR6Xfq6k6++0+9wk8cY6YySC+LzhPcxrWC8Z8OGHH+bep2x3nTzvJfSVlqrtkP3ciNdHbyY51KqqHd4ftC1N7bPPPrkruBqR9zOCCSO0zWSto/Sc4rOT9QN7sm4Z8SHaucafw0wgYE084zi9z+ROTmmWM0VyJC8GqrzJGdxU2lZv7w/Yn3RQTkCXD6BGtyGp8/AlnCZwsj+NDojyIpjBzNs0aZP+mx+/+CVJUtx2JkYArSj0w4+RRMkrWyXB+oF5nlueJA0YK6XJnbyLRGernPImxpht3khLW45Teh1Jixdeo2bb58UJGYLejbYuj4PhrQ5yZpNFY8aMaXrfim6ZlU3O1JJ9n2cnnvI6pHGLSvfPc27Ea/pybsTJnZ6cd+n206oNtsX+tmPtzEbWA8uemyQLssHebGvObBVILY3ct7eed/Y5N/M5lq3k7O1zkzgZa+fQ7rLavtTSyGdSs8e1yAkQfV32WDS69lT2/q2qFD799NNLkytI2uWpvC0anZ5olXb++eeXfnfPPff0KLlz6qmnJknPeIxDW8W857pay+ROLy3WFQ94+ZIgy9+TzHD2Qqcn+1Npe3m4ALl6Q/w+8z2XT7ogYKU2arUGPT05vgQFKlXgMEgigeNrJ0nqLRdddFHTj+V7q9p3FmvYNLuAN+P+attlofJm16hj5mq17bJGENcdefWF7+p4sWgwAa2o/YorWxjHzDnnnLn/FtUMces0tlXpsfHvGBfl3X4zj6v396v9nufSyDHmOMV47l//+tdr/l1uV/obtIeJ9aRSnBnJeY5BtX3p7X3LG1RjBnh2Vn92W428htnWg9n3LnGB+Lk2+v6ot/1sRRkVMM1un/0kTtPs+68n9631XVHvb1T6Xfa4kPzJu/3s52be93ge2e3kfd7Z+zT7Odauzw2+O4855piyRGWjmDiZ932XN57XyOd8f5c9ZnwWNHIs4hgvk1HzTr6ohVaRrA2WdihhHb9a+9Ts91MzNtpoo3DBBReUxrGs4d7s3zr33HOT1nPxazFu3LhcLRtVDJM7TSZ3Gk3MZO/PoKknyZ1W708zMxGygwipaEWUs3Yqgglp8obzl/+m/65WIp4mYJrFlz8Der68+W/871rBJUmSelNRY1S3Wzx6tbMAcFxVUNRxjye8MD6Kqwvy4DHpNRTbqrSfcdsYxmF5n0tcScM28jwu28KIMVq1x8XbZ7ZtI8c4e18m8WR/l3dfWjnrnHFopb+RDQDmfb6tXH+BYFr2b+YNSDMpM/vY7HOiK0fe1zCbCCGIHT82XZc3xf9r5P1Raf2PWo8nWdOT9x8B1Gbff+n/y/v+iDsOVHrfV5Pnb2TXE+XzqNnXlM+mVn1uNnIss/dr5rzL85lZ9LkJ1iOJEzvs1wYbbJC0QFxooYWS6gSeY3biRlzVyf+r9pyz1+R5X69mX4/+iHXXYlRh5T0WvO5UnqboYtSK40jSI7XrrrvWbWOYfT0b+VxpFNulJWFaPcnz5/ul0eU5SBBdeeWVZefG+PHje1QFpJ4zuZNT9guk3Yt19bX9kYoy/Yvu8M47/wqff94dZpmlKwwb9qXw5UEDI4nAoCNN4GSTOPy+2dnDtTA4TStuskmcWrOOJUmSeoqFrmPPPfdcYQc17mSQjqvyjnPSMVqlbXWaRhMs2fs32kGiWhscEhTMeu4r4n0jyMxi93nXhultjUzUzL5+2fdu9najk0Cz96+3/Ubff/W236lYZ6vZzirV1qbpr4o+N0nWxxXAtEy7/PLL67Y/LLKNaG+hNWa2RWAr7bnnnmHvvfduybay7Qgb2e+PPvqorCV9qypO0vXAcOGFFyY/jXjssceSNXJSyy+/fJgwYUJoFZLpcWtMquAaaTF51VVXJa3X4qQ0FTsmdtrP5E6TlS7tXqyrL+xPthxWapVPP+0Ot9/RHX43uTu8+hrv73//P/KYC44MYa01usIPNmCGXmcnGwgOcD5WWweniAROes5XaqPG7yv1XU/3R5IkqSgsUM5YJJ14RtuQl156KQn8t1o8c5qKaKqEsi3HqqFdXNxKj21VujaKr7m4f97rp3jbbCPP47LrOxAEr/a4ePus5drIdd17771XdpuxavbxefclDsyz+Dj70qpFreO/nd23PM83DiBzvFjwnnULWmXKlCm575vd3+xzIliXd/3c7Jo3HO94+7yeBOzSyiJe70beH2+99VbZba4r4sdnK1S4f7PvP7ZFHKTZ91/6/7KPrXbfuM0l/86733n+RjY58dprr4VFF1001/anTZtWdptrtlbFaho5ltn7ZbeT53F5PjMbPTdJ2qcdQKjuqHd9/fjjj5e1ydtjjz2SirR6+x+vVVXvdcheV+d9vZp9PfLifOpJm7t6SOa2an85Z6haS5ObL7zwQu5tk0SJDRs2rCX7Fb+3mj2O8ePyfv83mwhu5LPizjvvDCeccELZeXXkkUcmk3I6MTY8uJ9VvJncySk7I6nRSpfs/Xs6w6TV+9PMjKuigs4auKZP7w4TJnaHX11DxU7l+5DoeXEaP93hl5d2h622DGGH7brCoD5czcO5wkCpUvKGc7GoARQDnkoJHH5qLZzruS1JktqBbgTrrbdeuPnmm0uBO/69yy67FJJIipFEWnHFFZsKphJYrDR+in/Hv/OOsZp5XPY+tR4X//7VV19NxqK1xoaxV155pfRvZvxyXVvpb+fZF9rqpOsT8P+ffvrpsMIKK+Taj7waOS7ZfYs99dRTM7xn2iW7/ywwzwzvPLKL0Y8YMWKG7Q0fPjy8+eabpfs3cm3AeVTr3KAtUPb+zW4/fT2aff/15L7Nns/VHrvwwguX3SZQTRuwPP7whz/U3X5vnz+teFytz41mz808+/HGG2+U3eZ7Ic++N/I61HvPtvq45lV0HKDV+0vbs4ceeqhUNUOymM+verJVwUsvvXSfjIG08ngxnnr//ffLWsCRHMuzfapqSezE92Vdx/XXX79PHreByOROTtnkR6Mll/H9OYl6OiMpmxzqyf5U2p7U2959rzscdkR3eO31/I8hATRhYggPPtQdxp4UwvxD25vgYQZX2jIt+1NUAocgSLUETnZmnCRJUl+39dZbJwv1pgED2oAQQMgTsKmG2a/ZmfFLLbXUDMGLvMmdSZMmzbBWUKdi5jdJle9+97t178vMdCqcUksssUSP/jYzfi+99NLS7d/+9rctT+7E66Rkq5ZqySZL2LdNN9009EUPPvhg2GabbXLd94EHHii7Xek15NxIkztUqZFoyPNac83z6KOPlm5zzmbXsqItELPk0xZKVDBVOj8rof1W3Paok8+7LJITHKv0+ZH03H///Wd4/1Zy9913h4Gk6HMzW93AekF5xGv0dKqbbropdJLVVlutlNzB/fffH7bbbru6j0snFYDYbKu+d+I1A/N49913w+abb166vcwyyyRr2hSBhFZckZZNKFdDBTUVOnE8i/Z6P/zhDwvZTzUn3/QczdAKgJLxvLgwie/firYCPdkfZDO2DLKkdiZ29tqnscROjMfxeLbTGwkcBnxc6HCBy2zH559/PvnS44eLH8rouWDhPiRSe5rYSWdVUA7ORRL9fpdccsnkgpgLcS626DnLhRLnMslaEzuSJKkTMdOfZE4cMD7uuOMaXvsj7q1PICKL8VPcho0AIWO3eliEOA7gUL3S0yRHu/3617/Odb9rr7227Paaa67Zo79LEiFuJ8ZrwDi6yEmacXCrFvYrTh6QAHvkkUdCX/TMM8+UVVRVQ+u7ODFJFU123Yo0YBr71a9+lWs/qLKLJ5GuscYaFe8Xb582U7fddluu7V999dUtff/1JbQ4WnfddUu3//KXv4TrrrsuV3sp3psDSdHnZvYzgwB8PcQEsolTFY/Pknj9cSaGxO0TKyFmkyavsdJKK5W1+uuvLrvssrLbeRJaxLYOPvjgsnaJP/nJT3Il0NS7TO506GJdBHHjPtGN7A/Jpji5wyyR+ANR6u1WbFTs/Pmjnm2Hx7OdL77oeYKHZAwBBC7wGcxxkckXGyXfDAZI5nABRXKH85tkT71BRD0kY0jKpLPZ+MwhUEDyhh+SOSR1SO6Q5CHZk2cmlyRJUqeh3UfcZmfq1Klh3333TQKejaAah0AEY7csrn8222yz0m2C0qeeemrNSTmM904++eSyRBOzbjt9TDZ58uRkxnO9BMINN9xQ1q9+7bXX7tHf5bjtsMMOpdsc+0MOOSRJoDWCfcuuKZsaOnRozdZJtey8885lt0kyxpVLeRD0jatNisD1/dixY2uuj8mx5f0d34f3P0mFrFVXXTXMP//8pdskM+u9Pzguv/jFL8qubX70ox9VvC+/j9sAsuh4di2grLvuuit5n8aJKYKy/QnHJY7LMIM/uzZIjAD1scceGwaiIs/NbLwubRNaDQnKY445ptC1alQZsZNNNtmkdJv4zBVXXFH1cNES/4wzzijd5vOPMUItxIOo6k1/2l3BybpRJOobcfHFF5et80a1Ujz+qeT1119Pqgfj8c4WW2wRdttttyb2WkUzuZPTPPPME2abbbbSbRZtyys7gGxFcofBEP1xUwyG8rZmI1Adr7nTiv2RmsUaO81W7GSxncuvyJfcoSUDLdQY0LEwJ4NBzmsSOCyiSNkqCRzOLS4wmeVX7aKxkfOWmUDMEuWCiXNv8cUXD8suu2xShcMMpIUWWii5WGEWKJ85DPArXXRJkiT1V4yXTjrppNIi2CBB8+Mf/zgJ3NQKlnOdQ5sqFsFmxmmtRAEt4OLKEZJBhx9+eMUkEpN+SDzEM8SZdEOwo5Ol7bAIEtOSp1L/fFrYkHCLg5d77713rlZa9Wy00Ubhe9/7Xllwbvvtt0+C+bWCpQRUb7311iTQtPvuu1ddg5b1OZgUlbr88svDww8/XDMREs9sZv9SLBq90047hd/85jc117zlGuOee+5JAmPbbrttw8mqRvE6EHPg73Fdk8V+876O2xeRPK3WVodrlp/+9KdlvyN4ff3111d8f5CA2GeffcriEZxb2cRafN5wLsevJe+nJ598suI1G5VlJ554Ytnvea797RqJ47LjjjuWbvMe43mefvrpyTpfvGeZNMyaMJdcckmSGOWzqj+1p8uryHOTisJ4sXUq16iiqvTeJ37Adw0xtoFQ/dEX8X0RV1uRyKDKL9uCk3EDr3ucBBw9enRYZJFFQiehaxOJ4FNOOSWZ2FBrzRu+T4844ojk8yLGe58Yd62E1n777Zd8Nqc437LfC+o7OnuKUS9j9nxaxszgnqBvnoXbCBbH8i52mGd/0sU8GfgSjM4zeyVbttuq/ZEa9emn3eFX17T2uLG9//phdxg8uCv5omNwF699w4CO3/FTxOJvXGRUWwOHC6/+dhEiSZJUBCa8EKQ54IADkgAFqJY+//zzk5n+iy66aBK4JwjHZBiCnMxmJdiRDdxXG39RNc2Mb6qC0oAg1QGsG0LwkBZx6Qx5Zr3GQUOCSccff/wMLXw6zVprrZUEcqiOIlhE8oxkC5ORCOwQcM/OiKfd1oYbbtiSv0+FB4F7Wucxkx68liSbzjrrrGQNApIEBE6ZQUyAjglYBLjzzJSnOoh9veaa//+ig2Au76l09nJcQTJu3LhkYe0YCUK6XjD5C+wDM795D3JfJmXxPuI9R4CZIC8/ceeOopFYOfPMM5PrfJImXN9zbnBsiVnwfo7PCZ730UcfXXMd4NVXXz3ZVtqyj+dz2mmnJe8Pzg06CTD5jRjESy+9NMMi5/Vmd/N689h0Iizn7l577ZUEWgmuM8mNmAv7nm1BT2By5ZVXDv0R7Y54/6StH3mPk1io1qKN9WD4HIpn4Q+U9tyNnJvEAHifvvzyy3XPTa7Zqebg8wAkCUiwcS5wbvGdw2cjnT3iidwkDqjsVO9iUuwJJ5wQDjrooOS1IsZzzjnnhBtvvDGZQMs5whiCSR/xdzifkYceemhHvlxMOmYyBj9MgmH9HGLTTGTg/Oe7iFgxP9kk16hRo2aofMu64447Zkh88jt+GkGymqSrimdypwGcBHGPWmYT7brrrnUfR+/gFAOoVVZZpWX7wwKj8f7kSe5wv+x2pHa4/c4Qprf4uoftXX7Fn8Naa7yXfHnnXTS1EQQIOJf54UIzTuDwOxM4kiRJPUewghmnF110UVkvfcZ3BNXytNgiUUFVQDUEos8777wkMERABIwhqeKphsQHQcTFFlssdDoCQVRJMSOXNiwkemotqk3LrmwVRU8RfCORR2uxeHF4Xo944etazyFO0mSRaGCG/bPPPlv2++wM/0rJIhKHBHl5j7DmUDo5jCqVuBKmGq4Lim7bR3yBYN/48eOTwDWVSfxUwrULSTxaPtdDEoz7k9BJnzeJFs7Feu+Pes+ZADrHlIoiEjgpAvDZZFF8LHfZZZey6pb+hvcyCWfWWCYhWSuBSctukglDhgwp+z0JjYGgyHOTikzeh3Ewm0RppfaBbIfqwY033tjkTpvwGXjUUUeVtZ586623kp9KSIbwOdgfzhXatJHgTJOctT5bSFryUy9eVWkSdDNtB4uIxakykzsNoKcwsyLSTD8ln5wYtdaroWw/nunELKdWze5KZ1SlbQNuv/328LOf/SwZHFfDrLN4oMeaHsxokNph0uTWV85gyuNfCSt+7/96vB0uOrigiZM4/Jv/1rqAlCRJUmswK5UZ2rR5YuY011fVAjYpqgpYbJ2KDSp86qGtEdueMGFC0uorbkUSY8Y2ATzawPSHoFCK9iyXXnppkhwgmFmp3Tezo5mBy/oGRUxkYqzNtTbVIhMnTkwC/rXaK3ENToKCBbVZiL7W68G2qfhi3Rgma1L5w7qZBAHzBJ8IAJP84rmT6CDxV6slOkE0KlBIdKy33npl69cUZZtttkkClryGaXeP7D4RAOV5NLI/tJwihsF6OgQPqwX4+Nu0CWtkHSbiIgTnqVLh3EsrtyrtO9VCJOk6rYVSM3i+VGPx+XXbbbcl5wKVTSTwOA+pKOR9xevC9Wq8njLiNoT9XZHnJtVttFBnIXqqyLKIB1BZSLKRrjpqr/XXXz95vfisJ7lXaU1kxga8V/is6tR1x0n8brXVVkmHKD4z6yVdiA+vs846yXdrWo2s/qeru4i+RP0YA864WoYSfsqHK2EwygKb6SCFQTCLUPKBUwmlgnEVDYuq11u4kJlsP//5z0u36ZtbrRSUgStfPHGfaAZ/9JlsRtELQ6p/++KL7vD9DbpDER0LZp75X+Gk454MM81U/+ONAXGasIkTOfx3oJS0S5IkdRKqS6gyobKDaxIuaWnjxMQ3AnfxOjqNSquCaPvFtrmGI6nDeqdcx/WHCT4sCJ0GhDfYYIMkiJki4UF7L/4/SS6Sa7SvoVVWb1anE8im7dE777yTzEzmNskAXgsqutgnxu/tQDCNBAqz+Nk32kFx/cCxYuIk62oWmfwj4B9XTxFjiIPUTC4lgUVbHV4zkncEn7MVHo2iDRttD9ku7410LVGSowQce4r3HO0BmbxKGy2Ckuw7AfT+lExtNRIaJMBTVMENxHV4qp2bvE9p7Uhgm+RYo+8lkgSs+UZcj9agJM9IEjBRmu2p7+G1p1KTpCjvAT6niK/yPdafYjx8L/J5z5iICQs8V8YwjIf4PqK6j7GLnWVm1NPvw77Gyp0GMVuEMvV0NsC5556bfFkweyse6DMgYQGqePYJA+dqiZ2ezNBhZlO6cCKLHHIS0+8zXuCSLyHKFOPEDoOkZhM7Uk/xli2qFfU//vGl8Je/zBLmnvvvpRk9ceu0NHnDv4tukyBJkqTWIpBdVDUE13RUhORpW9UfMT7O0+q7aFzLcr3KT19DcJDALj99EYkvflqNgCGVUkUhKduTxOxAFS8dwHuT9coGqkrnZpqgBxMCGp3fTryANXyy63Gp7yImyrph/R3fk0xoGQgVjarNqGaDmJHCYoWUJ6eLddGrkX6oK664YvKlQeszegPHixaSMaXqp4jBN9U3JHmY3QJK6pnNQ5nuXHPNlcyAoQIobi/ADAMWqJTa5fPpxW5/3vkWCIstOig5Rzq15FaSJEmSpEqYTHzPPfeUbi+//PJtq2iTJLWHyZ0m0L+ZxR6PPfbYUkKFsn1+KmGhTRZ5K6r3KbMSqCA68MADS4uAUn7ImkCVDB8+PLm/s2LUTrP8u7CsEPPNNyTMNlvvtY6QJEmSJKknJk+eHFZeeeW6ExRpx3TIIYeU1oQGywJIkgaWzm8Y3MY+xfS2pa1ZtS9dqmNYj4fFOUmoFInyaBb/ZL9oOVWtNHHMmDHh5ptvbnl7OKlRQ4eyEGoxx+3Lg0KYf2gx25YkSZIkqQjjxo1L4jp0jGFh+A8++CBJ4NA5hrVEHn/88XDaaacli8KzJlWKTjJFts2TJPVNVu70AAsl0hKNhTafeuqppP0Za/GwuBoLKS677LINLdZFAuill15qen9YbPDUU09NFsR84oknknV4+PJPFw9bbrnlytbhkdpp0KCusODI7vDitNZve+SC9Ma1akeSJEmS1Fk+/vjjZJIwP/HaMdXWi2GNJdZYduF0SRp4TO60wJAhQ8KoUaNCXzHrrLMm6+1Ifd2aa3SFF6d1t367q5vYkSRJkiR1lmpr5lRK7DCZeJ111gkHHXRQEgeSJA08Jncktc0P1g/hkktDmP7vNsEtacnGdiVJkiRJ6iQTJ05MWq89+eSTYdq0aeHdd99N1laePn16mGWWWcLss8+edIpZZpllkmUCFlhggXbvsiSpjbq6q9V1SnXQjk7qqYsv+VeYMLF1x3H7MSHsspPLiUmSJEmSNJDRqm7w4MHJv0mSGQKVNGTIkH51EIyASmqr7cd0hZH/2ZptsZ0dtrMlmyRJkiRJkqT+zeSOpLb68pe7wtiTusLc3+jZduaZO4RTTuoKgwaZ3JEkSZIkSZLUv5nckdR28w/tCuPPbb6Ch8eNP6crDB1qYkeSJEmSJElS/2dyR1KfSfD88qKuZM2cLw/K9xjut8N2IXmciR1JkiRJkiRJA0VXt6uJqUmffPKJx06F+PTT7nD7nSFM+n13eO21EKZPL0/ojFwwhDVX7wo/WD+EwYOt1pEkSZIkSeW6urrC4MGD/1+c4dNgCFTSkCFD+tVBMLmjppncUW/45z9D+OtfZw9//7w7TJ/+WRg6X3eYeWYTOpIkSZIkqTqTO5L6e3Jn5nbvgCTVQiJnxIiZkn9/+mlX6O72eEmSJEmSJEka2FxzR5IkSZIkSZIkqYOY3JEkSZIkSZIkSeogJnckSZIkSZIkSZI6iMkdSZIkSZIkSZKkDmJyR5IkSZIkSZIkqYOY3JEkSZIkSZIkSeogJnckSZIkSZIkSZI6iMkdSZIkSZIkSZKkDmJyR5IkSZIkSZIkqYOY3JEkSZIkSZIkSeogJnckSZIkSZIkSZI6iMkdSZIkSZIkSZKkDmJyR5IkSZIkSZIkqYOY3JEkSZIkSZIkSeogJnckSZIkSZIkSZI6iMkdSZIkSZIkSZKkDmJyR5IkSZIkSZIkqYOY3JEkSZIkSZIkSeogJnckSZIkSZIkSZI6iMkdSZIkSZIkSZKkDmJyR5IkSZIkSZIkqYOY3JEkSZIkSZIkSeogJnckSZIkSZIkSZI6iMkdSZIkSZIkSZKkDmJyR5IkSZIkSZIkqYOY3JEkSZIkSZIkSeogXd3d3d3t3glJkiRJkiRJkiTlY+WOJEmSJEmSJElSBzG5I0mSJEmSJEmS1EFM7kiSJEmSJEmSJHUQkzuSJEmSJEmSJEkdxOSOJEmSJEmSJElSBzG5I0mSJEmSJEmS1EFM7kiSJEmSJEmSJHUQkzuSJEmSJEmSJEkdxOSOJEmSJEmSJElSBzG5I0lNWmSRRUo/5557btX7TZkypey+3JYkSZIkqV0OPfTQ0jXq2muv3RHX1mq9MWPGlI49/67F10nqe2Zu9w5IfdHbb78dRo0aNcPvL7nkkrDqqqvm3s4NN9wQDjvssNLt8ePHh9GjR7dsPyVJkiRJGuiqXcPHurq6wte//vUw++yzh4UWWigsvfTSYaONNgrDhw/vtf2UJKmVTO5IDTjrrLMaSu5IkiRJkqT26+7uDp999lny884774RJkyaFc845J2y44YbhyCOPDHPMMUfoNEwo5blg2LBhYfPNN2/3LkmSepFt2aQGTJ06Ndx7770eM0mSJEmS+rCZZpqp7OdLX5oxBPavf/0r3HLLLUlS5MMPPwyd5sYbbwznnXde8sO/JUkDi5U7UoPOPvvspB9tpYGhVMkKK6wQXnrpJQ+OJEmSJPWC733ve2HixIkz/P6vf/1rePHFF5OKF5IhVPOkbd323nvvcO211ybt2waCU045JfmR8jKuIfU9RqelHGae+d950JdffjncfvvtHjdJkiRJkjoIa+4sv/zyYezYseHcc88tm7T57LPPhjvvvLOt+ydJUiNM7kg5rLfeeuFrX/ta6TaDwH/84x8eO0mSJEmSOtA666wTxowZU/a7W2+9tW37I0lSo0zuSDnMOeecYbvttivdfvPNN+1nK0mSJElSB9tyyy3Lbj/++ONt2xdJkhrlmjtSTjvttFO4+uqrw//8z/8kt8ePHx822WST8OUvf7mwY8jijlOnTg2vv/56+Mtf/pL0A55rrrnCyJEjw5JLLtnWdX/eeeedZN/ef//98M9//jPMO++84bvf/W4YOnRoj7b7f//3f+HJJ58M7733Xvjkk0/CLLPMkjznb3/72+Gb3/xmj/eb48j2WSzzs88+C7PNNluy78suu2ySxOvrpk2blrQG/OCDD8KgQYPCPPPMk6zpwzHqyTGfMmVK8pr+7//+b5h77rnDAgsskByTgdJvWpIkSdLA85//+Z9Jl46//e1vyW2uEbkmmnXWWSve/6233gqvvfZacu3E+j0zzTRTmGOOOcJ//Md/hO985zvhq1/9asv2jW4hTz/9dPK3uH6lXTzXfksssURoJ67/X3311SROwX5x7HjeHIeFF144LLrooslx6QS8hiT0iD/w72984xthscUWS376ciyn6LhJ0YgjPfPMM8l/eZ+z75w/nI89OeZPPfVU+OMf/5gc88GDByfxKdbf+spXvtLS/Zf6EpM7Uk6zzz572HHHHcNZZ52V3OZL9Jprrimr6GkVEkgXXXRRuO6668Knn35a8T5DhgwJP/7xj8Ouu+6a9A1uNYL98XO74oorkoEkyYVTTz01PPLII6XFJ1MkAvjiPOKII8IiiyzS0N/jC/jss88O999/f/j8888r3mfEiBFh5513Dj/84Q8bHgw9/PDDSTs9BsfZ/QbbI5mx3377Jc+hN45lJYceemipKmzYsGHJ8cB9990XzjnnnOT4Z3HcaSlw8MEHJxcVjbzPeD+zmCiDwyz+/k9+8pNSq4K11147ubDAZptt5uKbkiRJkjoe19NpcgcE+dPkzhdffJFcS951113hoYceSibZVUPyZfTo0WGPPfZIEhx5VLrG4nqYa9frr78+CVLHuK5kUfvDDjtshm099thjVa/D42vLetee1a4d77333nD33XcnyRCOUTVMoPyv//qvZIIsyZJ2i4/J3nvvHfbZZ58kIfLzn/883HHHHeHvf/97xdjDgQcemFxn96VYTtFxk2aOZSXEGOL3KPGM4cOHJ0lB3uOcSyRjskhc8r5sJCZDsvGqq65KjvtHH300w//nGHNuHXDAAUkit5H3vdQJbMsmNYCBVFzdwZdHpaB4T5B8+P73vx9++ctfVh0MgMEIf5/1gJ5//vnQG26//fawxRZbJIPbSgkSfkcig0HETTfdlHu7VERtuOGGyeKV1QYoeOONN8KRRx4Ztt566xkGudUwC4RBBUkKZnFU2m8wsHjiiSeSRMYxxxyTDBD6ipNPPjnsueeeFRM74DkxyOa1YaCfd1C40UYbJYOgau9hLjJOPPHEZMDGRY0kSZIk9TfZREUccJ88eXIShCdYXSuxk157kgT60Y9+FH71q181tS9cg3E9ffHFF+e+5u0NJJu4rv7d735XM7GTVj9deumlSacTqkv6Gq6ZN9988+Q1rZTYSWMPJC+IDVSLIfR2LKfIuElvuO2225Jz44EHHqiY2MELL7yQxG7yrn1FUpb7n3TSSRUTO+D9OnHixCThyCRtqb+xckdqALN3dttttzB27NjkNl8efEkw2GsFBj7MbomD7ZTXrrLKKuFb3/pWUqFBWe+DDz4Ypk+fnvz/P//5z0lCYsKECWGppZYq7PV88cUXwxlnnJH8XWZ/LLfcckmpMuXXlKb//ve/TwZxIBHAwI/jVW+my+WXX146nvFMltVWWy3MP//8yZc1A5644oZ/M1D59a9/nVRUVcP9qcRhhlF2JsuKK66YJOoY7Dz66KPJAChFRRYzbsaNGxfa7bzzzkte2/S4rLzyysnsEt4LzHphYJS+Fz7++ONkNgqzUGq1C+Q9Q6KSEvoUZfPpa0rJ8rvvvptsm4EniaMzzzyzF56tJEmSJPUeWqzFVTskdqq1ZOPanIocWkdxLUkVAMkBrp2YSJgGjrkePvbYY5NWUwT78yJgz8S6V155JblNey0qGKh+IUBNUoJrcX7StmfZSYnV2qG1sk0a7a6o4OC6mlZsHBf2j2vquKqHeAnxE65PG+kwUST2jWPMa4YFF1wwOcY8D66nqShJK6nS2ADXx5UqpXozllNk3KQ30PmFc4IEKMeFTia0qCOexGtBEpXYA7gPySna13HsqiFBtNdeeyUTjGO0Blx++eWT1zSO9xA/2X///ZMKIqk/MbkjNWirrbZKZqGks3aYlcHvKD3u6SDjoIMOKhsMjBo1KpxwwgkzrKfCIIDWZ3wBgi9ySoYZNFUbiPZUmthZaKGFwumnnz5DmTl9iSmvZeCQftEeddRRyTo81dayoQct20ox4KGEnZ9scoL78hzTJAyVJ8yiqZWAof1ZnNjhNTr++OPDBhtsMMN9mRly9NFHlwb2lGczyOO1bReSLyR3GLyTpKItYPa4kFjjeKUXAAxYbrnllmRGTDXHHXdcWWJn8cUXD6eddloysI3xerO21IUXXhguu+yypM2AJEmSJPUX2QobgsIxAtFUoPDDZDhuV0JAnaoWAthprIDrYYLvedfh+e1vf5skawjEEwegsiOLazSuCTfddNPkNskB2rGB61cmnxaBBBOtvriW5vqx2tqs7B/dIYgfkORiAijXn8RN+gKqX9hHYgNUe6y77rozvI7ch0RK2r2CxMpaa62VTBBtRyyn6LhJb+A9QNLmBz/4QTj88MNnaNdHPImEDnEYkDS94IILktZ51fBep6tMnHQkJsVrValqiPORhBfVQVJ/Yls2qUEM5miRlfrv//7vJNnTU2wjniFC711Kn7ODAbDgPUH3VVddtfS7N998Mxl0FIUBEBUjzCqp1D+YgQiDF0pdU8y8OP/886tuk7V74nZfDIhIYlSqOmFhQJI18803X+l3fPHTSq3aACtdHwmDBg0Kv/jFLyomdkCLMgYP8YwmBqQMMtqFY8PgkoHl7rvvXvG4MAOK90L8/26++eaq22TRwnvuuad0m9lgJG6yiR2wTWa2MBuG/bA1myRJkqT+gusiEhExgs8xkjMEmKnAqJbYSQPuXMOzvTRIT2uuWtdmWSR2uG7l+qxSYge1OjQUiQocrtdZE6VaYifdP9pkMXkwRbUKFVJ9AXENJi0ygTGb2AHPbZtttklao8dIBLUrllNk3KS3sP+0G6QjSKV1mDhneJ5UxcXJzmpt8/g9E2FTnDeXXHJJxcQOaGdHbIqJs2nllNRfmNyRmsCXUlxWTMKjJ71M+XK59tpry0rBSZTUKp3my4sBBqXg8ayjIgPwzLCoNECJUa7MgCXF2juV1nRh/RhKtlNLL710UplSy7zzzpvM5ogxcKmExSfj8voddtghLLvssjW3z0wcypZTzDJKF9prFwaG6aysakjQxIMYZutUWzOIsvIYs4aY4VILycx4kCVJkiRJnYhJgFyHct1Ke6547Q9aVGeTO40iThBfv02aNKmhx3NNTDuqTrf++usnXT/ARMG0UqUvIHlDFVYtG2+8cdk19ssvv1xx/aCiYzlFx016CzEiOqXUQrJq2223LUvgsDxAJSSsaKWf2n777eueNyuttFKy1pLU35jckZrAlzGL66Wo7mDBw2Y9++yzZYu/UUVSaTZDFrMx4koUSnyfe+65UASqdigtrocZFyS/4gQJ/VWzsuvg8GVcawZQijV8SGakGCRWGgTdd999pX8zsKJcPQ8GSszmqLSddogHN7VQgp8imZb2EM5ibaQUvWbXWGONuttmZtMWW2yRaz8kSZIkqd1oVUb7sPiH5A1tw7nGuuGGG0prk6QB8XRmf0+x5kd8rd+ILbfcMvQXPTkORcobG2Cd2ljcAaO3YjlFx016CzEi1i6qJ9sWsVrFV5ws5HjkbadPYk/qb0zuSE1iJkfcyory67S3bqPo+xnLk0RJZRdozG6rVdZcc81cg4hK+19pIEd7sDj5Uq18tpLRo0fXnM1B1QrVK3FpMoP1PFiMkFLzeN/j2Vy9ieNSr9ooTr7FaBeYxfo8LBKZorVAXo3cV5IkSZLajevC+KfSdR3XuLTnomMD14K10D6Ldl60reYxdH7gWjObRGJ9kRQdPvK2gaLqp94+9AVPPfVU0kKL9mt0mmCiIVUT2eOQrp+CZmMlrUY1UdyFpRZe37i6plJco+hYTpFxk96UTdrkjWvE1TmxOBFGEouJq3nwvqy2JrTUqVwdW2oSM3r23Xff5Aeff/55smYLCyg2ikXusl84eWXv+/rrr4ciVFpnp9YMHY5POniuNNsifs4jRozIvcgk4uRL+pyXWmqpsoFj3JKNGVqN4JimySGqstje0KFDQ29jIc28xyV7v/j5p95+++2qM6nqoS0bFWuuuyNJkiSpE5HIIVg/xxxzhJEjRyYtrliLg+vRWt57772kjValyo08CFDnqebo662wSeoQ73jppZcafmylyYftsMgii+S+LzEN7p8mXerFNYqI5RQZN+lNeSfbxsm0anGNf/zjH2WdShp5TdME35QpUxp6jNSXmdyReoCZFnxhvvDCC8nt6667Luy00065Z4JUmo3AAKKRmQQMEuNESrWZDT1Vb62dGOW29JpN96XSQC7ezzwD3Vj2/tnnnL3d6PbjNYPS/W9Hcic7sKklW1UVtxhIZV8HLmryYpYQr+knn3yS+zGSJEmS1A5Uk0ycOLHH2yEgTnsu2mY1i4mgecw222yhr2Jx+wMOOCAJrBd5DIrWaGwgjoPQcp7r7Pjau+hYTpFxk96UNymVJ66RfR6NxDVQb81hqdOY3JF6gC+e/fbbL+y6667Jbaoaxo8fH0455ZSGtkN1SJwYydv+LN0HHpPOaIi31a5EQ/rlnX7pVpptEe9no9vO3j/7nFkks9b9e7r9TpVtB0AlTiNY4FCSJEmSBgJauf30pz8tS+zQNm3TTTdN1u5hUifBf66T4msl1vM57LDDagaoK2n0+qy3vPPOO+Hggw8uS+x85zvfSdaMoS0dx4SuE8QlmBSYOvTQQ5N2d31JI5Uv2fuThGF92zheUHQsp8i4SafKdhMxrqGBzuSO1EMsSM+6KJQo45Zbbgm77LJLUuad16yzzlrWCzU7G6QW7stjKm2rlSolaGph0FNrEMJ+1kr+NLIv2edMhUmt+/d0+50qOxOs0cFdNmkmSZIkSf0V1SpxCzLW2Tn99NPrTnpr9Pqzr7v44ovLYg4kbVhvp56+eBziOEWj96fKJpscKjqWU2TcpFNl4z3GNTTQfandOyD1B/vvv3/Z7J5zzjmnocczyyWeDcKii3l99NFHZQtDxttqpUb2iZLrOBFQqUw23k+eQyOy988+5+ztRrefLblvtMy3rxoyZMgMvaPzoqVbf5npI0mSJEn1/O53vysLjJ988sm5uhk0ev3ZSceBdnd5Ejt99Th8/PHHTd+fyZLZxE3RsZwi4yadinNxlllmKd2O19/JW4km9Scmd6QWYICzyiqrlM3wefHFF3M//lvf+lbZ7T/84Q+5H5u9b1GLMDbyfJjdFA9SKlUxxc/5jTfeaGgGTbrGUbXnzGJ9cbVQI/uePaYMHPIu/tfXsdBgXCb/3HPP5X5sI/eVJEmSpE4XL2ZPG7ZsxUA1zz//fOgvqCx5//33S7dXX331XI9j0muj1+G9Ydq0abnvS0wjrtyqF9coIpZTZNykky222GJlxzHvWlBMWGUdLak/MbkjtQi9eOPy2rPOOiv3Y2nrFrvvvvtyP/aee+4pu73MMsuEIkyaNCl3r+D777+/7Db9eLPi/WTgF88Gqic+PvSojb/YQQJjqaWWKt2eOnVq+PDDD3Ntm2qWeBDEvlN+3R9QQr7ooouWbj/88MPhk08+yfXY22+/vcA9kyRJkqS+5bPPPmu46oHKjSlTpoTeMvPMM5ddV7caHRya6WoxefLkPtmW7dVXXw1vvfVWrvvyOsbPoVJco+hYTpFxk04WH3e6xvz+97/P9TgmYudNBEmdon9ELKU+gGTCqFGjypIhzzzzTO7HfuMb3yjdvvXWW3OV3H7wwQdlQfd55pmnLKnRSpSuZpM21WZCXH/99WWlyyuttNIM94uPFSZMmJArecQAhRkrqTXXXLPiAnrx9hkEXXHFFSGPyy+/vKzqaPTo0aE/YfHPeCHC888/P9cA+Lbbbit4zyRJkiSp74jXKMnbyumSSy4J06dPD+3Yx3RtlqK2n/c4cD190UUXhb6ImMOVV16Z677EKGLrrLNOr8dyio6bdKpNNtmk7PZ5551XFseptnzABRdcUPCeSb3P5I7UQvvtt19ZlUec5KiFvr1bbrll2cyDo48+uubMG2YbHHnkkWUzSbbeeutCv7DpMVyvh+ypp55aViVDMiG76CCoIKGdXYpE2KWXXlpz22z3hBNOKPvddtttV/G+m2++edlAlKTNU089VXP7jz32WNlAj8TUZpttFvoTns/gwYNLt3m+N910U9X7MzDde++9k0SQJEmSJA0UCy64YOnfzz77bFmLrkoeeOCBcNlll4XeNGzYsNK/CebH1UatQCu6oUOHlm7fcsstSau2Ws4+++zcE13b4aqrrgpPP/10zfuQeImrZBZeeOGkNV9vx3KKjpt0Ko5LPImY7ivHHnts1QQPx/yggw4Kf/rTn3pxL6XeYXJHavGaJhtssEHpdiPlnixKGA/MmGmx7777VlzwLw24x6Wn3/zmNwv9wmbQ8vbbb4ftt9++4qCWgckxxxwTrr322tLvSCLsueeeVbd5yCGHlA1gTjvttHDuuedWTCTwZc3fpm1aimNdaYCVDkJJtqXY5m677RbuvPPOqoO33Xffvew1+9nPfla2dk9/QMLqiCOOKN1m8MPrcOCBB4YnnngiqbziGFCqTkJsww03THpNL7DAAmGuueZq675LkiRJUm+Juzhw3cQ1+MsvvzzD/QjkM2mOa1/+3ZvXkEsvvXTZNe/hhx8eXnvttbpVDM0eBxav32effcKnn346w/1ILBETuPDCC5PbffFamrhGGhvItkUDVTHXXHNNco0c47i2K5ZTZNykkx133HFJu7kUsSgSbbyutJ/nHOA1INbD5F9asvH6L7HEEm3db6nV/t2cU1JLMNC56667Gu7jSTKCL+kdd9yxNBPm3nvvDQ8++GBYZZVVSovfsfgbv6OkNMWg6fTTT5+hZLqVDjjggHDmmWcmg1mqcZZbbrnkS3GWWWZJkj4MTuIycCqYTjzxxDDnnHNW3eaSSy6ZJBXGjh1bGkhRTnv11VcnCzUyQ4gFA1kzh6qbuPyYhQX5Mq+FARLVOBxHsH+sjcRMohVXXDEMGTIk+dJ/9NFHyxbLTAdAW221VeiPNt5442Rxy3jGD+Xj/FTCAGjcuHHJADVe10iSJEmS+iuSGlzzpmuyMuufdlArr7xysn4J10S01+L6/M9//nNyH1p0jRkzJrl+6g1rr712mHvuuUt//+67705+2Deu1VPzzz9/0+uo7rzzzuGGG25IJgKCa3/+7hprrJEkJkgyvPnmm+Ghhx4qVaNQbTLffPMllT59CRUytJvntSTJstBCCyX7ylpCJAJ4LbOt53bYYYeKreZ7K5ZTdNykU/He49jtv//+pUQXFXa8rrUSZc8//3zpnDauof7A5I7UYiNGjEiSH9ddd13Dj2U2BdUSe+yxR2mhewYHzPyotjAfg0dmxnz7298ORVp88cWTwQQzVhiMkDThp9qijieddFLFnrRZDJRIHnD/NCFG67darcKYnUSv1HqLWnZ1dSWJHEqeb7zxxtLvSeRkkzmxLbbYIplx1J8xqOEigONTq6yeiwDuw4CSAWM8gJUkSZKk/ooJi+ecc07YdtttS5UQVAMQoOcni+uriy++OJlI11tI4JxxxhlJQDuebEkFUdz2K76WaxRJGpJVTGRNExMkeu64446K919mmWWS5EOajOhLuI5l33bdddfw/vvvh1deeSX5qRUbOPTQQ9seyykybtLJiDnxHI866qiyaqUsKnyI8VDBE3d4Ma6h/sC2bFIB9tprr+SLtxkMhCgX3WmnncrWRsmi6oQBCfctOrGTokUXJcrMbCFxksXv+H+sNUSCq5HZM7fddltYb731ah43EmdUAzFDpVZFUDbRdMoppyRVKhzbSvudDtwZkLFA4fHHHz8gZnAws4gBOe9XZqTxfuP4U1LObDSONa8LCztycRD3b6a9myRJkiT1Z8OHD0+qVri+5dqyEoLndH24+eabk4qe3rbCCisk13VUMNChgiRT3K6qFajSIRaw/PLL1zxWtDa/4oorkkqYvtxOn8mfdLSIq5uysQdanxEbqBZD6O1YTpFxk0622mqrJVVprHXE+3+eeeZJ2thxHhDLIJnDsSaxgzT5BpM76g+6uuN6PUl9CrOCnnvuuaR8lxJhBhV8OY8cOTIZBJCQKMqUKVPK+r4yQGPQmKIVG/vGYn0E/vkCJTlCpUdPMKOItV/o5UsfXwZbrPVC5QglxT3FcWT79LplZhMDcb70aTM3EAY+zZo2bVrSgiDF7DCSfZIkSZI0EFApkV6rTp8+PblO5fqX6+BmJ3d2Ilqa0f6LVnDEJLieJqFAIL2vJnNSVDhRgZQiJvD4448nVTxUIxETIEHX03VZio7lFB036a8IgZMASteMojKLBJ7UyWzLJvVhfOFTShsvkthXMCuHn1b76le/msy8KAoDnnXXXbew7fdX9G2O9Va1mCRJkiT1BQTnv//974eBboEFFkh++gMme44aNarjYjlFx036KyYxp4kdGNdQf2BbNklSTSwSetVVV5VuMxOIxQslSZIkSZL6OjrOsIZWilb8JsjUH5jckaQBaPLkyUnZeZ7EDotCxrNb6CctSZIkSZLULk8//XR477336t6PNopHHHFEePLJJ0u/W2uttcJ8881X8B5KxbMtmyQNQL/85S/DgQcemLQVYHFM+gqzbhK9oj/77LPw8ssvh0mTJiWLZnI77ldsckeSJEmSJLXTgw8+GC666KKkAmf06NFJm7Vhw4aFWWedNZnMylrRjzzySLjyyivDW2+9VXrc1772tXDQQQe1dd+lVjG5I0kDFItHXnfddclPHvPOO28YN27cgFosVJIkSZIk9U1ffPFFuP/++5OfPAYNGhTGjh0bRowYUfi+Sb3BtmySNAB95Stfaej+a665ZvjNb34TRo4cWdg+SZIkSZIkFRHXWHjhhcOECRPCeuut5wFWv2HljiQNQBdeeGF44oknkhLlqVOnhj/96U/h448/Dn//+9+Typw55pgjDB8+PCy33HJh3XXXDYsvvni7d1mSJEmSJCmxyy67JG3mH3jggfDMM8+EP/7xj+HDDz8Mf/vb35L/T1yD9vPLLLNMWH311ZP7dnV1efTUr3R1d3d3t3snJEmSJEmSJEmSlI9t2SRJkiRJkiRJkjqIyR1JkiRJkiRJkqQOYnJHkiRJkiRJkiSpg5jckSRJkiRJkiRJ6iAmdyRJkiRJkiRJkjqIyR1JkiRJkiRJkqQOYnJHkiRJkiRJkiSpg5jckSRJkiRJkiRJ6iAmdyRJkiRJkiRJkjqIyR1JkiRJkiRJkqQOYnJHkiRJkiRJkiSpg5jckSRJkiRJkiRJ6iAmdyRJkiRJkiRJkjqIyR1JkiRJkiRJkqQOYnJHkiRJkiRJkiSpg5jckSRJkiRJkiRJ6iAmdyRJkiRJkiRJkjqIyR1JkiRJkiRJkqQOYnJHkiRJkiRJkiQpdI7/D1N+4YS/qAnCAAAAAElFTkSuQmCC", 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+hp4G07Le/Na3vlX+HGeeeWbRtJzNJTarjXFAkU5SeX7jSv5LcfGcR49aCAFWJsUMsraaRHI4K5+npnBLknSbTmgbJPInzrFtyb1ZqySePO/ZPsbTNyWvdRLqxtml+fOf//wy47rTECbzn3EWJU+SfBI1x1m0P8WhkrTa5PwqMWgpoPPBD36w6IO2770kHiX7/ePe2/zKV75Svg7TorOJdRtssEGlr09niFNPPbXos2TJZhKQBeskZEKW171Nk6FscKmKsWLpYphA30lV6MlmXZI9uygT6rynqpg/f365UbnbbrsV0+RHP/pROd6MY8GRA5C5tGuua9xMFcM2dZNZtGhRuQmRyUkb2upmkpggvlS4oZ2SAJmgjnEfRuTwOgej40rKzqFkFrapXtgGmVcmOL4tz2dmoOVBBx3U9NPorCz+sqE3bjkInVmZN4vabDZMoptcAnKWldg3rb/PVGZqk2ycpRrkNHYzSzWxqolL66+/frnZc8973nPor00waDbjv/nNbxZtMKg897Wvfa3pp9Jr0z5/+M1vfjN1FcGYDHOe6dLGOU9TgVE5RGzLGUDG+gQq/eEPf6jl++WwNwdibTys67Np2XOhX/q0dsr+efZATjjhhGKSQWvTuOafZvm95Vy0qS51y5Nz2sxtqiRrdi2xLtW/R+kqlkJlo1YL33nnnYs11lijmBbjPJ+tImNx9jAmKQlJX//61yfyWCnW+qIXvWjiPyPNSOeLScUqRM7hx7G3Oe1r6GkwLevNxJEkJqxN+xkzkyjaJF0jcz8ZdwfHdCnMOUXVIg6wsrlYklpo7h6S89FR13Xjkk7jSeo///zzizZIfHtiMCeVbN3GePqmJOZ6EnEUmbfPnOOki1yKkU+im1z2ONI4ZBxJmim6meJQbZhfZf/h0EMPLV7/+te3upPbtO+9pBt1zoon1YThwx/+8NTsgXQ2sW6rrbaqnBmbyUIGlEwc+igD16QPBZKkNYng5Ln4/ve/37pFcdtk0rbXXntNPOjwpz/9aesOrOZyI8oisIpUQcz3ecADHlBMo/xO664CmI2k97znPcUkZcK3//77l5tLTctkOMH4bfy8pMJNJsm0y6WXXloGc0yqs+EgYSTVpuuUzsPZRG5bVf7cL/P6Jkm8DfJ7ziZMXxaqdfvd735XrhUmJQmQg/dOKlNN8rAwi/y+dNJKktaxxx5btMW73/3ucsN4GpPqvvzlL1f6Hptssklx+OGHF3e6052G/tocMmYd07bDgYzHqbSmY3ozpn3+kMOXBKa57zMsc57p1LY5TxOB58997nNbszab3TH6z3/+c+XvlTnFKIHmjM+07LnQL31bO6U43bg71S0rcTHrkKuuumqij0u1QPJTTjmllS9hAr3ymR01oP5e97pXsdpqq430tRknRgkQqjK+ZJ3729/+dqLJfFWSD/t0Plt1rTLpsTiF7SYdF5LCqPvuu29rgoQpxpYgNOkkgRQhqLugZh/W0F03LevNzMcTR9JGmeck5uuwww4r2iBzqNxHxtnZZqbcr9Jx1bqFcUpCySjze6rJ/T33+RRqa6Ozzz67LPiUe13Tkvg96WYLbYqnb3LNOqn1Wu73icfKnDp7Duk0Nsn8kXTkq3t+laK7iatpmxS2SVGQvmjT3ksK+7/3ve+d+H7+2972tokl8o1TZxPrdthhh8rfIwNjBpSHP/zhZVWXbOIlUaJNWaPjdOSRRzZWNbnpDcRswLzpTW9q9Dl0QdrCNrVJlba7Xfks5oZY9Ua03nrrlWPQLrvsUkyztPmtq/vOhRdeWC5omnifJHDpne98Z9G0/PzpRtXm3/ekuqKyctl0zYJwUhvuA1mM5nETRFKHBDXss88+5aFkG2WOk/bt464cN9cK3E0/jy5LlaJJVvNJFdtsmGWu3ETCdCpAdWXuVVXWAak+3rSTTjqp+MIXvlBMmwMOOKDcEKxiyy23LL74xS8W22+//Uj3u3R8bGuAcT5nKXKTJC8mpw/zh3QY0QmDUZjzTK+2zHkm7dxzzy0Dzutag9YtAUoJ2K8SsHjiiSeWc0naY1r2XOiXvq2dUjE3we9NSBJU1XUykwkAyZjalk49y5NgwyQZ5Kx7WOuuu26x0047jfS4udcMW3Ay852qxVtHSZKrkli36667FtOozvPZqkGz2YeepLwHE2jY1OdVEdLp1lTMVYol1tUNoA9r6K6blvXmN77xjTIOrO3S2eOYY45p9DkkwD9JdZPuOpP4wFe/+tUTfUz6JbHaeY9NKmGU/41vzPox9/s2y/lmknubPseddAGQNsXTNymNNSZdtChx2h/60IeK3//+9xN97CQd1TkfSsz6V7/61aKt0oXwox/9aNEXbdl7aWosO/300yfW8XOcOptYt9122xW3uMUtahus0iUgi6NHP/rRZWJLApoS2JoM9D4v0MdhkNDYpFTBGeXAgckeNE560jSKz372s2WnkSo23njjspLZXe5yl2LaZeOnrg5zOQRpcoO36YV+3ntdmIik8kQbuvvxv50omqr+dMEFFxRveMMbaumk+vKXv7zcyG+zM844o0wsadIPfvADgZYdPYTNmqQJWdyna3Af5CC2DZs3+V1PWzJjujxWTRa89a1vXSbVbbPNNiN9febmbeu2sKw5cTqKXnLJJU0/lV7oy/yhapdImCRznn7NeSa9V5MxPwejbZZiUfvvv//IlRuN+e0zDXsu9E+f1k65P6QAVc4nm9LkYzM36Z7yy1/+shMvVwLrU2F9lH2lKh3Zhk1Yq5LgNur3yNp/1ITh9ddfv9hxxx2LaVTn+WwV6QQ06WDZJGk32enZ+M+4JBi4qr6sobtuGtab6YqYDhZdkPlVfuYmOzulc3dTxbKSuNuWrn1MpxQ9yDkwk/HWt761M51fs45rKlamaW2Ip++bdMj71Kc+1chjn3DCCbV8Lr/3ve8Vn/zkJ4su7Lf1peh0W/Zemo5p77rVi45affXVi/vf//5jSSpI0FU6AM3sArTJJpsUd7zjHcvrTne6U3HnO9+52GCDDWp/7L44+uijy42P1VZbrZHH/9e//tXI4zJ8oNWDHvSgVm9WVu1atvnmm5c3k9vc5jbFuK266qrl42T8uu1tb1v+70033bTYcMMNy455a6yxRnllAzUHTzlkyOFcqiGlOkOq2GfjrI6NmHRDvP3tb1/pe+Tqqywmc/hUx730oQ99aHHf+963TOxMkmeS1jNGZoP65z//eVmNq0rV4muvvbYMfkgFsnnz5lV+znTX97///bIL1yMe8YiRv0c2seuoxpdkjd12260sppD/nTndKqusUgbpZHzKHPDb3/522UFsVOnW+IAHPKB41KMeVTTBXKe7lbFzNSXdjvK+Hafce253u9uV66rMR5JElbVW5iOpmp25SO4XuX/kSvembLDnSgXW3JtS6beqVG1K96o8blOm7XP69re/vfKGb+aHn/nMZ8o5yahJxXVsOq+55prl+J37RNb/eZ+ss846ZVGH3IcyL06VpxQDGVUKvSQIrqkN2z7pw/whlSb/+Mc/jvy4WQ9mjrbzzjsX2267bblOXWuttcrPQg5zEvSW939+1ryWqRyZ4hnp5JD1IgzLnKdfc55JSveLOrrMZH762Mc+trjnPe9Zzl0z5uf3ld9JumykIEWCnKoUfEqhv8wDUuBvGBmTZ55bDGvBggXlXlDuZ/nZMuZnnpMxP5/N7AvmfCQ/a/YBM99JEY68rhn/+xrI2Ic9F/qlb2unnOXo7syKZP718Y9/vFMvUuYD6YT4zGc+c+jEugSQjSLJuM94xjMmmlj3hz/8oZz/zJ8/f07/fRIPRu3ukrEsZ6l1m6bz2a7th+aMM/NumEYpEp+1W8aRUfVhDU3z683sL//nf/5nuf/VFbkXJ6bxiCOOKM8A+iaddB7+8IeXe0ZMl8w5U0jiDne4Q3lGPzin32ijjYq11167/Pec5eecK5/Z3GcyJ02iadYCSYxIMbeqMp4/61nPEkM2gblw7r9dkoJyD3zgA8urb5qOp++bKuf5dcXFV+kSmz2C//qv/6r8PDLPud/97lfGJ9z97ncv9yoSt5W5UIpvZF/nuOOOq7SuTtGC/KzZI66ydlkZey/tcNppp5WJo1kXdlVnE+ti9913n1i3ngwSsxM5Mrm8xz3uUdz73vcuN6GnJUAhSRUPe9jDil133bW45S1vWVZnu+iii8oKMhnQ6+g8lGCsTLizQU03bbHFFmWHxyy2ckPNAWpu2Dl8SjBgHcE/ORTKJCYBH22Tg9iqVTISjJnvk9dyXPL5TbBMEpEzVs2l02d+l7kypt3qVrcqx4InP/nJ5b+ddNJJxUc+8pHKhxBZCKXj3KiTrTpbIidg9MEPfnCZXJYxLz93NnUz7qU6aYIMmuyMt6yfPxVlRj0YHHjMYx5TJrxtueWWN/u3vAa5dtppp+L5z39+udjOY476OuSgLhWHnvvc51Z6znRfkpGzGMsYM8o9IdVWqsiGYKqTJih+WQfkGfNyZdzcd999y3H+2GOPHfnx8vXZ8Mk4w/TYeuuti8c//vHFDjvsUGy22WZlYOn5559/Y0JF1fF5RbKBkfnXve51r/IwI5vb6a6dikaZf1XtxpRuwdkkH+UzuiKZK+bALRsyee5zmdvlc5Mr96O85pHAndwHjz/++OKQQw6plESSQJUkfb/gBS8Y+Xvwfw466KCyA3MVSfJPRa1RN9Py3k1yX9WNw2c/+9nFi1/84mWu73O4lOuud71r8aIXvag8VH3Xu9418oFwPrs5lM76m/Hoy/yhyvrsec97XvGKV7xiuWP/IMAva8n8nHe7291u8u+pnJvHz1oxQTIS7aaHOc/wzHmalb2HqvORjL05vM4+WMa+2bKHlyt7SBnzP/ShD1Xq1ps5bdYW2Y8aplJ95rKj7gW96U1vWu58K4f2+bmTFJP9ogR/z5QE0F//+tflflldVVVpx54L/dK3tVPGrlGTiJYl+zGPfOQjyzEy9/6MmTlDTqG67KtkTjzqOE0zsrd34IEH1pY8nnOV7IPlTG4QpJr3Yc6ckryQgKQEpNbRxSpzkewV5ixwrhIoNUjiGtawXS7r6IqZvdbMf+YaU1BlfZzz0LpM4/ls16RoRJ3nyUkUSpJBEocy/mdOneTv7M+feOKJZZJTk53xaJd8lrO/lzEg50i5F+T9kiSzBLgnabiO8TFxDE94whNG+vq+rKFpfr2ZOJHEG9Yh98isCR7ykIeUe5eZa2Vtkvl49qVzfpjzgHzeqsrnNDGSo37Guixxejmn+PCHP9z0U6EGiU3M5yb3pJzvLGu8ni1Jdrly/0phiNhzzz3LNUySfz72sY9V6uqYuOGs8ZsqUt2Xz3H2YeqQcTbryKxzEyufArmZGyfxMuNv9ouzzs2fo3RVny3jT9ZQc3mvTop4+makKMzjHve4sihs5n8pujPzfl/H+215MsfIY2dvM/P5SGx88keSIFY1LiyfmSqJdZmbVS1ckzl6ipktKwEqn798zrfffvuyoFOKgVYpZpw9sXSuq6MT8kz2XoaT5PmMr5lLZ52W93kS5lP0Ne/r7D0treFzlfe3xLqGJDBzu+22qyXRaxSpbpjr61//ehlclcre2UzLAXUXk+zyoUlgaQ7CZld9S+BwJthJsEil4wROVq0mkwN4iXXdk5vRG9/4xvKgZnZlniTZ5ZDg5S9/eXnzTqXEKvIey2FJElfbJBuMVZPqMulIN47cnMYhhy052M7nts6FRj6zCYj/6Ec/Wk52RpVA0wTyjFL5MQcT2eitKo+did9+++1XbggsSybIOVzMwXs2BsaZLDFX2WxPhcoqP/fb3va2Gw/j5iKbGalatPfee488Qc5G9x577DHWyhOMJgku6Q6agJDMqzIupYpgFjUzF6R1JEwn+SiBNNl0G/YANHOPqgkbOXjOgedcZFH+wQ9+sNxozOd/FHkNP/3pTxcvfelLR/p62iUHhKn486QnPelmc6AEU+UzlOSEzJdzmF63pz71qeXjzz60yvwrCeJ77bVXGaxWZW00mHvVEUSS+00W409/+tPLuVxd1Z7z2mfzP4fSuZ9lTBlVNgYk1lWXef9hhx1W6XtkfpmxdnlzsrnIeFulK1nuhxn351oBL+vnJHsmYO6FL3zhyAUI3vve95aPuaLOvinGsbx5aO41VYoXpNBC1nCjmut9tQl9mj+MmtjwlKc8pdwwryKb6jlMG1QsTpBE5o7DdibJvTQFPVYUgJFrVCv63tyUOU9/5zw5aF9Rlc4qgQ7Zfxm2m8rssWZlMuZX6f6cInoZu9NBZK5FL7JHmj2+N7/5zSMdNuX1TrBv5lNzNep8P4ekmXdUqbSe5JEE0uV6zWteU+4ZJAgmBUaqFL2YZl3Yc6F/pnnttCw5p6qjQ1LG/ezLpOjr7LE0Z6iRPff8fLknda0qfBVdn8snMDTVlKvKGJ9zr8QKLK+YYd4r2Vs844wzygIuVbsEJZDx0EMPLV73utfN+Wuyt5iA2gTLDCt7nrl/JelrZRLQnq63dUiF9LnGFFTpklfHefQ0n892TQK5EhxWVeJmXvayl5X7b7mnLUvOPzO3TwJC9vH60uU5+2ArGqMz101HtFHltayy99hU8lXOcl71qlctM6E2QYzp0JPXJveBqsV9E3M1atJPX9bQ06wL680UF8i9q6rct/LZSWzPsgrS5Wwp78nEkWa/ImNP5p9Vx+N8n8SAtiW5I7/j7MNnL3KrrbYqx8gkGSSxJfvyufel2GAd3QHTpTBz1sFah27JWJD3bu5Js4slVpG5UO47eR+mE+XMBiWj7FnPZb/ZGeloEpOaAkBVZWxNIZoUl1heUaXs92QNlCIgudcnnr6KfP1XvvKVVuxxiqdvRs6DMldNHNRsGdMSz5u1f/YZ6y5ukjnHPvvsU+ZQzF7/Zf8r499znvOc8swt999RDbrUz2V/ZVlFjHLPryJxCC95yUvmfF6UdUzmgukePeoeWj7Xmc9lXKnK3svwsl+Z/aBl5Stkzyn72kmQ3n///cu5f9V1apdjZTvdsS4f6mxIZIBsWhZjGaxzvfvd7y4H9QTW1jk5HacsMjOhSob1ymTSncovSbCostGSD2FbX4vcCBIgkESSHHSkAkd+xzl8yw0tC9LcoFJ9LNXZ+yKbXJ/97GfLyhMr22DOZlheuwQhVpH3SZsS6w4//PDK1VzvdKc7lW3Nh6kgOdcxMRtn6TYwzs2NPE4qiuUAbdTArmwSJzlslDGyasLmYBMhG3j5nK9MNsny8+YQLON6Nv+akg3mdHKpIpUfhkmqG8imeJLjEnybzblRDngzQW7DPZv/k89AgjQH1VVmSkB4rgTfpUrK+973vnIDumplitxHEnQ6TBBMNm6rbL6kilYSZEcZd7NBf+6555Ybe6PeN5Jo1abuq1n8p6JUxrUUhsh9PffsjHeD6slJYE4lvlTZruPwuetyAJr3fyoirWyulMPzpz3tabVWR8ridmWByNlw+fjHP1489rGPrdS5LnOvKol16XKRiqG5dw46zY1DxpADDjigXND/7Gc/G+l75EAo85k2VjfNc8pnNFdex6xHMoblHpx7apJV8jlNZaisS+o4JBtFAvAzrle9FyUgaHZxl2EP1KtU1837Ngekcw0MnSlr6AR4ZvN0lM997m+5z+WAa3lW9Fmq8rpFNs5S5W0a9Wn+MOrm5jg6SudemCv3gVNOOaX42te+ViZcrEx+1hW9F+eSVLMi0/o+r5s5T7/nPKlOOC75DI/zc5i1Sw5qRpU1UfbqRjnEzF55DkEzFx81mD9FytIBaS5GTYbJYW+VpLplyeuVAh+5EjifAoTf/OY3a32MLuvKngv9Mu1rp9lyrlfHeULGu3RXWdbnebasH3JOnHPGFLEYZ/Xstuj6XL7qvsbgOeY9MtfCuznDy/la1o55X1eR75M56Vw6og3k3HWUxLpILMRc5kx1dSuIuXZpy75Z1qGjyOd7rskhfT2fHbeMn5kbZQ6VBJHsh2b8SLGpxGjkPpAYjbx3M/df2Xlt9lbqeE65b67sXGAwFiaZI0kdOQvtQ/fSzDlXNEZXKRQbGWuSONKl1yPFB+ZSxCz/TdYDOXevep4zij6toadVV9ab2SfImVbVc+10O5xrgcAk3iUmJkkgmWtViWtMwHzecymM0KSsw7Kvk/f+7AKs+f8pbp17Z5KUEtOYn7/KZ3wg5yNVEqSZvMR7JBkpiQ/jKvQ/iK1NTMQTn/jEkTtSpot35ksrS1x1Rjq8vK5VitsMZBzN/X6u3UpTCORb3/pWGVv529/+ttJjJ6Ey8TEZ/5oinr4ZOadKfOnKzrPy/kzSZ+71dSbVJdY9CewrkpyCQQxtlflV5vKjzKdHLe47kLyP7C2MsgZIcacUHRulO3DW9slRyRx2FPZexhtzGIkh/epXv1om0VdJHM16M7/vrp5Tdb4UVQ5vEjzaJnlDZBM8AbXJxq9abW7cBgkmc0mqm7nhnkl4FXW1eq9LJuq5aSSAPDfIJJ7c8Y53LG/WCVbMZmiqvWTwyGZBNh8SyJokl5XdTKdBXodMlleWVDdTbsDLqljR1fdJFh2ZkFWR1yOT/7qT6iKb9Zk4TapiUB5vrounZRllIyeHRVlcV5FJ1nve8545JdXNrjqRyWGTN/yMT1U6EaVqRpUK8XlvjTKxHkgQw/XXXz/y11OvBGtnM3QuASHZgE5loSRQVA3ES0BoKqVNKrghGy2pXlhl3M2ibtQAkBwWZPOoLdL1IlVpc6icQKYk2GUzMp0HMhcaVE5OkHB+5+k+kArbqSxeNXmjq7J5kkODuRyeR+bUdXZlzppirmN3DplzKNnk3CsHxwkgG2eA+ewNgCpGDSIal8y1M+akwl/mnakalM/koNJoPqtbbrlleSCatUs6DWROlcIPqQY3Sfk9Z+OrilRSzVq06vhy5JFHVqrumwPJZVU8m6sEzGTjdFRVAltZvj7NH0atxjfKZv2wRWXyOpxwwgmt7m7I/zLnGY45T7tUnZNkLlVlTExg0+1ud7uRvjYBXsMEOow65o87IDUBtdkrzJifPfW+69KeC/3St7VT1vwXXHBBUUXm9Fl7z+XzPFOCTBLITLvlPZIiBFXkvZH151yT6mauO5NsmuDDKpKQkaD1YVQpaDrXjnBzTYabiyTnzCVJ6Xe/+93IyUxVXpM+nM+OU/Y70/Ek88jEX6T7QPY5E0iZM4sEtOZzlmJwCRTPXmJ+hqzJlheHkKSCqsG8eY3ze53rucBAEquzb1p3UQvaLb/vrCvnmvgTWUOmG24VSVAbZY3YpzX0NOrKejOB5nUkOeesbJjP1szYmKpniHHYYYcVTUpiYYpTzvX+n89mYtKqrLsGEvs6SuA8zUkySjqijjOpbiDztGE6Zy9rHTNqUQxWLEU/q3YrzRw4c+Fh1x2Jr07yfNUCOZnj/OAHPyiaIp6+GXn/JGFtroWxk1BeZ1xSkvTmmgeQOOLE3006Nitxw6MW4Ryc3aeZ1ajyu0kC/6hSsGDUvWl7L6NJEahh4sU33XTTMra+iuzNVe1e2qTOJ9bFW97ylta2ns6mcSqXpHtXlUpD45S2paNsFqey+ex2p8NIp7e2dHtL0liC1nLIlc3bYYKO0kUimxDTLoPlsN08sjGTKhRVpBtHG2RzbtRs+ZmHxklOHOY9Noxhkh7rkJ9j2OS0mU4//fShv+b444+vVFEqciCz2267jfS1OeCs+p6uokpyTjY1UiG3qiTXpOLVKNJmPp0+aV6SpkZZKOXrXvnKV1Z+/CRqzVWq3qeSxaiSKF91npiD2wQOjaoNFfszBmTTPcUDht1YyOuXuWw69/RR7huZ7w2jyv1xpgTkZHNg2Odbxdlnn92p+Uiqbt7hDneY6HxkXBJ0kUOqJEcOU30tG4xZc1ZJfh9lbZDAwipS5ShJq3UULagyR0r165e85CWVn0MKz4y6Pk6AWO531Kdv84cUeBpFuupNwiCBn3Yz5xmOOU97pFNF9ouq7NeN0nlppszdqhShy1wmXZXGOeaP2uluWIPg5z7r0p4L/dO3tVOKS1WVc8PsPYwiHT1T1Ir2mkt37ZVJMOmw56cz75sJFq+6NzJst/QEUo16VvmrX/2q1v9uLq699to5Bd3ONelvWZK0Nao+nM+OS/ZeMlbn3jDMvkE+MznrXV5AXx3jfxJXRu3sl+c2TIdVui973ZnPj/J1wyZmz05cGjaepm9r6GnTpfXmb37zm8r7z3mv7bHHHpWKl1Ythpqg9z//+c9FU9761rcOXew/sXqJpU1H3SpSNPu73/1upe/BZE16Xpo5dJXHbNO8dJpUXedmDMk6ddQ1Y2IKM3ZVNew6t07i6ZuRfbR0gxvGsLFcy5OiD1kDtj02K/PgKl3yUnCharGFrHdvfetbj/S1CxcuLI455piRvtbey2j36Yxnw/qP//iPyoXdq8YeNmkqEutyE0+yyqgVbcYtA1kqQmRT5O9//3vRJul2MOwNYSCV26t+eC666KKiaRnk00WpaqWEaZbs/nvc4x4jD85V2jK34T2S98cBBxxQ6Xuk20gqKlSpINhG22+//chfe+aZZw79NalWWEUqcqXqYRUvetGLJlLdZ7ZFixYVP/7xj0f++my2JYm4jntulc3yKhv11CMHNFkojSrzhmEXsrPlvZzF0lx873vfq/RY6R5chyqHoDn0T8fNpmTsS+XC3IsYTgK9RgkaS3BKHfJ5y+9vGPl8jtohqS1zr2ENWzW46nxkHHKfTvW2cRVgqFMqR2f9XXVsPuiggyqtE2Ym7ldJoMqhbB2ve7rADNMFfrZ0CKU+fZs/jNr1MWtUCHOebpiGOc84/PCHPxw52azOMT+V0EdNIr744ovLDivjHPOzJkxAFOPVtT0X+qWPa6eqRd6S8JHOc1VUqeDPeCUgP/OIKtIt65GPfGSl75HEzWc961mVvsepp55afsbnKvsxy+v0tTLnnHPOSvcPk7Qx1/nn5ptvXluiXpUueVU61vXhfHYcci9IJ6NxdLivep6cc+C999670vdIAsuoc3e6JfOjUROWMh5XTfoZ9kynb2voadK19WbVuJDsV9ZRODqdIasGkDd1fnOf+9ynLKw56viS5LoU56ziuOOOq/T1TLc0pKgSb9uWeek0ufLKKyt3D08ydrpxVZH1ZtX4pMzpEys5aeLpm5GEzFGaXdQVm5Ui1hnTht1HqDLHGCU2q0oMRD7Xd7nLXYqq8joln6APscNd3nvJXDBdbKsUhq+ii7GHU5VYN9jcSkepqr/McUqloHQfGKWF57hkQ7DKhl6VgSOqth2uKtXMPvShD815076vRslaHkiF4kw4R5XWr2kN2pQkxaZTXZVM/xy8fvCDH5zKCv1VEszOP//8of77/A6qLv6yaBu1cujM9/QTn/jEYtJOOumkSp1PR6mSt6LOfaP60Y9+VNvzYPTF4C1ucYtKC6RhO2jNlnF9rhVrq7xn0sVqu+22K+pwq1vdauT5QsavKomxdVTW7lpgQFvkwGLYDn9xm9vcppZF7ij3m2zcVOku2PTca9LzkUl17ljZ+PLe9763E3PFdJhLUHbVIgWpJl31IHPmfaLKXL0tc6Qm7xPTqG/zhxQ+GrVqZdb7TVbcpR3Mebqh63OeNo756b503/vet5bnkT32KgEH4x7zs6e255579jL4cJK6tudCv/Rt7fSPf/yjcoeMZz7zmUMH1YwraIX6nXbaaZXPqZ/xjGfU8lye/vSnTzyRqEqHtpXdZ379618XS5YsmdP8dq4V3ld2Ppj7X7pajiJ7uVXPDqf5fHZcQZMHH3xwse6669b+vdPhsOqcN/vywxa8my3FToftMEQ3PeUpT6nUPWHSMVd9W0NPk66tN6smOedsu47z1hTLGLUD6cDPfvazYtLy+6qaWLjZZpsVz33ucyt9j8yvrr766krfg+nW9XnptPnFL35RubhaW9a5iZFM99NJ63s8fVMe/ehHjzTPqWOukMLloySCpsBGlb2ESy65ZKj//i9/+Uulxk5t2dvN/k6VGOhJ6vI97qEPfWil8Wj7imPZsO/vNpmaxLrBBtynP/3pYv/9929tV6jLL7+8vPk22bVkZnWXtOWsokrC1KBiXZP22WefchHNiqthVx0kqyYuNnUj/drXvlZW8Kly2JzFxjvf+c5aunG0UZJTq2y6XXbZZXP+7zMxrLphk0l4HR772McWk/bb3/525K9N4Pqo1UeXV0F1VKkgVyWgL1X0UtFhlCuVL/suFclSXbqqbGQnULyKX/7yl3MaJ/74xz+O/BhVqzzONkqC1UBTwYspOlFHYMbyZEM/c4VJXHVUJRzWqAffOVysco+Mu9/97iMHzVb9fHZlE2OgymvddJWc3KPf8Y53VA7UmIQPfOADZVe9KtI5eL/99qvtOVWdI2WTtOpap645UgL6upbU2lZ9nD9U2ZvJ/Dqd9bLGSaf2b33rW+VheRKt6Q9znm7o8pxnnKrMBe5xj3vUumdXZcyf689RZcxPkHsK/+VA7U1velO595m/6/IBV5t0bc+F/unb2ikdoKvI/SHjZR2qdjRjPKp0cIzs5TzoQQ+q7TORIi+T/HmqFGL7n//5n0r/PpBzq8zH5iIBlStK1svPn4SqUXSxKN0kz2fHIVXTx9GpLlLkuuoeW13jtvG/H6rGDUw65qpva+hp0bX1Zt6XCf5uQ2xPVOmqMliDVE1UGdbOO+9cyxosyb9VCl2mw6XiPYxrXtqG+OlpM2qxkYGtttqqtuJAOW9N3EyTP08T8fRV46Sbjqfv2jllksiresADHjByYa0q86pJzuOrJsPVubebucWpp55adEGX916qrlO32GKLSl/f5cIMqxdTJgPcC17wgnKTKoF/xx57bHHDDTcUbZJAhnQO+dznPldblf5RB8q0iq9i1GDfgSba9c5MxNxrr70ae/yuqDpZHBy6dvF98rGPfazS1/+///f/yuDhNsnE5Oyzzy6TjJKolsz4Cy+8sAyaTDvwf/3rX+V/k02h/DmJZOO5jkNnnHFGpcdKF5i6DsfSwSKLyaoVbyd1yJvORamSUZeqn+ksfKseEjCa+9///rUlb2Su9clPfnLkrz/99NPntEldZSyqq+V7He/9SW/4DLziFa9o5HGn6TMziszxM09OMvGoqlT9rFKxchJzr3z/dEdKkEPmIxdccEE5H8lc5KqrrirnI4O5yLgPqpJEmKCbpgqz5PdcZ/J7W+fG+Uy88Y1vLKv9163K+Nqm+0Q21jLfrVKll/7OH3baaaeiqozJuWbKvSwBLtkcv/Wtb13+ue2225broS502WTuzHnGw5xn/DLfzlxyGsb8HCTmHGNlB7h1jPnp4vTVr361vAbWXnvtcqwfjPup9Jr9pNvd7nZlAB/Tt+dC//Rt7ZQzkCoSSFb1DHXgwQ9+cPGud72rlu9FexLrEiRY5z0ygVxZz07q50mgdoIcR6mGv7Lg6rkGXydBI8XFkqSxsg532cPL67O8z36VgO9JJNZ1+Xy2bplj1lkpv+7xPx0Hdtxxx9rmhwnOnXQyBpOTtVPVNdokY676uIaeFl1bbw5+P1U88IEPrPT1s7/XgQceOPLX5xwx55p13R8mFas3CIrO/KnKejC/88xVmQ4JdB+cB2WPMPeFJLhlTpor/565y+Aap6aLPUyjqkWG6hx7s85LLMRRRx3V2Lq9iXj6rsZJNykdAkdN+qr6ejcZmzVsQZgqn4fMI3O+36bY4ToT/Qbsvfzf77vq3G2DHo9lU5dYNzMT+P3vf3/Zkeywww4rvv3tbxcLFy4s2iIVXL7zne800vVooI6AvRy4V9HkJuLjHve41nY2bJO+v0+qJDK8+MUvLtoglaC+973vleNOKhe06aY1TPXILOirSCBQ1e5BM6Xb5SQT66pU9MrXpttTW1StTkY7NkEyAa+y6T6XZNmq75V0Raq7M9Ko/va3v5X3sxyiTko+93VVk+qjbIBUqSa17rrrVkqsq7KpUTWoZxxzrxyiff/73y9OOumk8n9PIkBkrppMrEvHkGmXTfODDjqo2H333Wv/3nmvVpkj/vSnP23VHOmss86SWFeDPs4fBtWiVxaIOKwE+OWavVGf55NAuOwX3O1udysDSqtWOqY55jz1MueZ/L2zive+973l1QYJFk+AdQo5rUjmLgm8rLuzaIJmEqw+O6A/QYpJthuM+TnsrPMAdpp0bc+Ffunj2umvf/1r5f3/OoPus1fTpnNqijK5qYq6PxNVv9+wP0+KIO26667Fd7/73aEfK4HlmYssKxkk7/O5Vh5PsakUhUyQ+ly+Jp3wlvfZn2uXvNky1xlHMNc0nc92bT80CYxtGf8z9mcuXfU50V51JJpNMpamj2voadG19WbVeVaSnOsIlB/I+ySftSqdM7JXP8nEujoT2ZKYWSWxzh5DtyXJ9de//nXxwx/+sDynT6JkW5qUNDknnVYZq9oyF25inVuVOOlmpMjfqAVd83WJ9akyniSmuInYrGFjpqrEQGTuPMl5zCRjh+29LHvsrVokee0e5nxMfWLdQA55DzjggLJD3H//938XxxxzTHHyySe3IpDz4IMPLh7zmMc01rWujolQ1aDdpUuXFk3ZY489GnvsLqnjhlq1clKT75Nh5fP8+te/vnj2s5/d6PNIRYOjjz66+MIXvtDqCsXDTGqrtoCv+5AzY+jxxx9fTEImGul2Oi2yyU0z6vwcpKJtFamEm7bmK+qmOE3vlQS6ZxybZLdGc51qkjBQRdVErSqPX3WBXNfcK5/xr3/968WXvvSliW96DqOpoJYEHz3oQQ8qplmKGqTgzcMf/vCxfP9UUaw7kahJ03Tfa1If5w8JMkhA5Iknnjix9cmgomnG+UiXo3TiePSjH13c8Y53nMjzoB7mPNWZ8zRnmsb8mEtQYBKpM7c64ogjJvKcEmiTgIxc2WuMFCBJgFfONpLc3dT5Rtt0bc+Ffunj2qnqfnqdScQZJzPnqtKNjPqdd955rTpzqjr2J6EtHR+Gqa6fTm2jJNZl7zDBuQ95yENu9m+/+93v5hSHkX2xQfBaEuzmmli39957L3ON+pvf/KYY9Ry8areoaT+frVPGwxQ/Hqc2jf+R97nEuulVRyzNJGOu+riGnhZdW29Wfa9VfY7Luv9kPM48ZVST/Pzkta3zvV319WzzGS/Ll7VBzui/+tWvtjberE2FJ6ZB1iKXXHJJq8bfqt9v0nOXOuLp+xQn3aZzylHXuCm2kzPupmKzhjFNc/mqe3L2XqZrndo2q/ap+vKTnvSk4jOf+UxZFewjH/lImfiy0047lYfRTcjCI1Uem7LttttW/h51dn+apExg6q6wMI1ykFC1vXFMsjtPkzKWvP3tb288qS4JxAmwSYJfmw9tYpjD/KqLv80226zS14/7+61INjmmKfAhgRw0M0bVeSC43nrrFZtsskml77GyhNlpe69M+ue5+93vPtHHmzZVO+9U3UDpcuef3LOyUZ/gmne84x2tP3BpqlJOqtlO8zw5G5iHHnro2JLq4sILLyymybTd95oyba/jXH+eptehSbjIntsTn/jEMrn/qKOOak3VU1bMnGd05jzN6+uY/6xnPavRZLY8z6985Svl83jkIx9ZfPGLXywPMvusi3su9Esf105VAwfTZa5OdX8/qs/jqp451R3IX8f3G3bsT4GWUf3qV7+q1Dlu5513vnE+k0T9uUgy37LWmUlaTfX1UVR5DfpyPlt3J4I6Yg+6NP6nYx3Tq2sxV31dQ3ddF9ebVdcf40iYrPo9J7mmqtI5Zxzfz/5CtyRZ7ZBDDikLIabpR1uT6rrezaaN8lmtmsjQtnVu1nlXXXVV0aW53TTHf0zjOeXGG29cuWj6JGQvZJrux1XmVfZeVs5YVk1vEutmV/Z42MMeVrzhDW8ojjzyyHKD97DDDiv222+/smPARhttNLHnctxxxxVNWXfddXs7EWgyobJL6niPdDkBc1hPe9rTyiDCpqQq1Cte8YpyLOvKJuIwC7qq1ROrtqcd9/dbkX/961/FNJm2n6crMr+ZP39+qzZBVraJl3Ftmkzy58m9VxGBZsf5KnPNbPx0df6UoKTnPve5xVvf+tbiiiuuKLqgqUo5SaybZi972cuK+93vfmN9jGmbU0zbz9OUvs4fsp91r3vdq2iDP/7xj8WrX/3qYvfdd69UBZjJMOcZjTlPO/R1zE+l+oyxbZDE6re97W1lgt2Pf/zjoq+6uOdCv0zbWmMuP0/Vn7nu/X8dJNsl3d2qFgKp+z1Sx37g1VdfPXTw2qj3m+Ul0M01sS5d6gbm2gE3AZVnnnnmzf7+5JNPLkaVrn1VTfv5bNf2Q43/TLrAe5dirvq6hu66Lq43h52TTGLuXHXuNmoS/yi23HLLVn2/dD6TANUN2ad78pOfXHzoQx8q1zz0S9Wxdxzjbx3r5kmOv3XESnc1zqdJVd93VWKzJhn/W/VzME1NOUZZt9t7mew6dY0JdmNsm14m1i1rcEw1she96EXFxz72seLnP/95cfzxxxfvec97ysnmOKsI/uxnPyuaUsdEqKvJaQk+YHKT5bTM7YO0T//e977XyGNfeeWVxZ577lkce+yxxbSqmlhX9+JvkgfhVX/2tpm2n6crurgBPW2bfZP8edKqvu5Dlr5Jh+Om5j9VH7sp559/fvHUpz61UjBLn0z7miRd4n/zm9+M9TGmbU4xbT9PU/o8fzjooIPKzvNtkYDHZz7zmcWXv/zlpp8KK2DOMzxznvbo85j/ute9rlXdL84777zihS98YfGBD3yg6KMu7rnQL9O21pjLz5Mq/VXUHVjT1b2eaVXHZ6KNY/8oP9eoiWXpyDY7cDMdbP/whz/M6etndqnLOnaunVSWlbg312S+ZQU8zrVbXp/PZ7u2H2r8Z5K6FnPV5zV0l3Vxvdm2otl1fM9Jrqnq/p1XLeCQhPxpW1NOo8zPc06/rEIU9EPVeXCS/euOMapjPJ/k/b5rc7tpsWDBgkpf34fYrGm7Dw/789h7mfxYtmpPcj6Wpb8/+RwqtD3ucY8rDjzwwDJRJol2r3nNa2qvopUqLk216KzjpjCX6nFt1KZAszara+LQ1ffJsFKh55WvfOXEqzPncZMYfNppp030cbum7pv9JBdCOYycJtM22e+KcSwGq26CrGxjZ9re+1U3soZhrlNd1c2brj72qFId6HnPe15x7rnnNv1UOmPaP6c5WH3BC15QnHLKKWN7jGm7T5gj1aPP84fsZaVgVB1VyOpcr77lLW8pvv71rzf9VFgOc57hmPO0S5/nAuutt17xiU98othss82KNsl96MMf/nDRN13cc6lT1a5Pk36+fdTH8bLqz1z3HKmLez3TbPHixa2s3LzmmmtW+vpR3vejJtZlrffb3/72Jn+XPaC5fD5zj9tpp52W28FuRWYn0eUeNGphp7ve9a6VPpvOZ9u5H1p1/K/6OZzN+D/d6ggWn2TAYh/nhNOgi+vNqnOtcRSPrfo9J/n5aePvfFKftzr2F/q4x5CuyTmnv+KKK5p+KjSo6tjbxjVuF8ffvsRJ18k55cr1eR5v72V4xrJqJNYNEZy01157lYFAX/nKV0beZF6Wv/zlL0UT6tig6WpWapuCzNqsrt9vnyoxZJHy0pe+tOx8OSlp4T7ubiBtUHWjq+4qzaO0JB7VtLXWTUUrJq+LG9DT9t6vayO4bV01p1WT89wuzrHf9KY3FX/729+afhqd0oc1SZIPnv/85xdnnHHGWL7/tN0nzJHq0ff5w93udreym/pcq/1Pylvf+tbiz3/+c9NPg2Uw5xmOOU+79H0ucJvb3KY44ogjKndaqdshhxxSnHjiiUWfdHHPpW37HVk7MD59HC9Tab1NgaN9C+zsw2diHMHFVSvxj3Lv2HXXXUcO/Jud5DbXznFZt84+O55rYt2vfvWrm3XmGPW8Lj97FX05n+3afmjV8b/u8dr4P93qCJyeZPB1H+eE06CL682q77VxdCeq+j0n+fkZx++8anJLHYUpJhlPMcl4rjaMhfvtt19x+eWXN/1UaFjVcaqNa9xxjYnL0+d4+iY5p1y5Ps/j7b0085larUc5H7MZxUeQzd7Pfe5zxate9araqkYwWYLNGadsIu2zzz5zPkCq4u9//3vx2c9+tuiDqhs9dQdnXH311cWk1F0dkX5q4ybIyhZ+KnmOzlyHScqc57vf/a4XfUh9+ZymOmKqJI6joIw5Esti/lAU2267bfHNb36z2H///VvTHTPr5IMOOqjppwGVmPO0jzG/KG55y1sWhx9+eHHggQeW/7sth6IHHHDARIvLNK2Ley51qiPATmLdePVx7VT1Z667UN8kzxOYzGdiHL/Tqt9zlJ9ro402Grkwy69//eub/P+5nosuK4lurol1l112WXHWWWcN/ZjLUqWQcp/OZ7u2H2r8h+Wzhu6mLq43q77X2jjPmuTnp4u/87rUlcDXpz2Go4466madpOmnqvPg7OXWndhcx3hu/gL93NsNey80oVq5pp5L5f1//OMfZQe7Kq666qranhNz0+dsWiYjC40XvvCF5aHKXe5yl7E9zqc//elaNhbWXnvt4v73v39xn/vcpwzC3GqrrcqWsPn7FVVJS7Dm6173umIScrhXRd0t7y+55JJiUvoSeM94tXEDemULP+/95qqywjA+9rGP1fKCbbDBBsWDHvSgYpdddilue9vbFltssUU5H8lm6YrmIwcffHDxkY98pOiaPq1JEvT0nOc8p/jiF79YbLPNNrV9X/cJvC9WfND9ghe8oHjmM59ZHHnkkcU3vvGN4rTTTmv0TZPO7nkOd7jDHRp9HjAqc572MRf4v+qTT37yk4vHP/7xxTHHHFOO+Qkyb7J6/1//+tfi+OOPLx7+8IcXfdDFPZe6E+irBrlceOGFtT0fbq6P42XONqp0Kaj7cy2xrl0yZmX/9Prrr29NF4wEUFd5PlW6gSXB7E9/+tPQX/eHP/yhvAdk/blkyZI5B/Quq9vuxhtvXNz61rcu/va3v630608++eRiu+22u/F/jyJ7jne+852LUfXpfLZr+6F57do0XtedqA1V9HFOOA26uN6sOhaPIymq6vecRNfVNv/OJ9UxamXdEOfqn//8Z9EXde1Zb7bZZsWDH/zgsuDFbW5zm7KAV9ZNK9vvee1rX1ueQTEd9/msc+tMZKtjPDd/gf/dw8h6OnsvfWLvhSaIuK3oZS97WXHEEUdUqr46jkojQD2JXJdeemmljYkk4KbD5TgCBzN2fOc736n0PTLhynNMwOUkN4JGUbXq9p///OeiTmeeeWYxyZ+9j5Nj6tXGDeiVbYBk8w5ot/PPP7848cQTKx/AvfzlLy+TPyZ1MMPk58YXX3xx8dznPrf4whe+UGy55Za1/Ao233zzWr4P08X84eYb7Rlfc51zzjnFD3/4wzLB7Te/+U3tgaBzcfTRR0uso5PMedrJmH9TCWrffffdyysBRD/60Y+Kn/3sZ2U3mRQ6aGLM70tiXRf3XOouMrfeeutV+h5//OMfa3s+3Fwf105J0qmSsHneeefV+nzq/n5UT0rPuUuV30uqZt/tbner7Vcxl4SycX3Wk1h32GGHDf11ixYtKpPrkiiXcXwuAdvZ+1teQtu97nWvOb0Ov/rVr4pnPOMZZRGB2V3z5irPed68eSN9bd/OZ7s4/rdpvD733HNr/X5QhTV0N3VxvZnCmU3Pi5Y1d+vK56fu33nVAg6Zt+ScYRLq6JaVOeIZZ5xR9EHmxSluVcX6669fvOpVryr22GOPXhWFnUabbrpp5djCjJX5PnWpOp6vu+66lZO1YVr2sfLZvOCCC4q+sPdCUyTW1RBcmISZU045pdKgB7TPhz70oWL//fevdPiajpR77bVXcfjhhxfbb7997QvkKlWFsphKd5dUnBnVJAMxU6WxilGqba7IJDdiUjm1yuT4MY95TPG+972v9udFt6TLYjZh6wysSnB2FSvbkKmaeHHccceV1bSA8TnhhBMqdcLIQUwCd6pUiG4iMaSPPvWpTxV77rlnpdc7SQmDznVViyYMAsaqHBCkw/QrX/nKys+DdjF/WL6tt966THDNlQJRZ599dhn8mLVSDrdyYJaAr3FWU899o4tV9cGcZzrH/FS7vO9971tMo8y1nva0p5VXZIzPmJ9CUQm4yXo+1zgC9AZSgCPztD4E5XRxz2WmFXXEmVQ1/b4EvTWlj2unjIOnnnpqawr1nXXWWbV+P+r5XFRJoKm7+GLVM6wkZ42aoJWuFKN28EuX3CSp5cxyLu5yl7uUxQCWJd8nBY1XZtClLr+DK664ohg1mXBUfTuf7Zq2FWqt+/tBFdbQ3dTF9WbVwh51x/ZkHfSXv/yl0veoq2DkJJIA6/5+iY2dVGxrHfsLOePoy1zppz/9aeV50+c///myc/So+vJad0HV2MLB+Jv1YV2qjudVE7VhmmQuMurnO4WhvvKVrxRdYu+Fpkisq0Fu4FUS61bWIh1oRjZ7PvOZz5SV/S+//PKRv08OdZ73vOeVyXW3ve1ta3t+cz2kWp48pyqHNjHJate3v/3tKz/XbJZtu+22lZ9LJqkJQp2kvHdGnRzXvfFId+W9cPe7372W73XRRReNfGg91wPOqmNmfl6JdeP1zne+s7zor6rzkVTAq5JUF0103+ijnXbaqfjYxz5W7L333pW6rv/jH/8ok+vSua5qBekcENzqVrcaudqdOdJ0Mn+YmxyEb7fdduW1rGCNHD4Pku0SrJguBOk8WVXWUXUHgsAkmPNM75g/rYl1s22zzTbltdtuu93k77PnmTE/QXUZ9/Oa5Kyjjk4dSdTOXlzdxcbaqmt7LjNVTX7MmqzKnuvpp5/eq2q7Tejj2iljXlsSIfL+FmzYPhm3qszxMnbVqer3qzIvyvrsTne6U/Hb3/526K8dvIZJsJuLJM8tTzrWzUXWplmrVvn9VU2s69P5bNdUCQyve/xPsmrVLjJQJ2vo7uraerNqTE72KlIAva4ucdmTTqfdKiYZc5CiHCmMV1cyW9V727BJ61l/jirvzRR0rVIA6Ac/+EHRF1XnpQcccEDluZN5abtk/K2yx5bCbNOyzoVpk8/DqON+5pJV76+TZu+FpnS2VVoC+b797W+XC4mmVWmfG4KJoN0LjnTnqNpWOoGJ6QqQQOK6VEnsyiQpHUeqmuRhQDaqFixYUOl7HHvssbU8l+985zuVuvOMokrSQX5PCxcurPX50E11HvZXrcib5OWVfabTFXjevHmt2fQB6p2PZB30xCc+sfLLKjhhchL8lK7OVQ7FBr+zBBFVKV5RxxzJfWI6mT9Ul6TXu971rsXuu+9evPzlLy8OOeSQ4mc/+1lx5JFHFs94xjMqjQFZR1WtiAxNMOdppwT2VAmyOu2004q+22CDDcpA+kc/+tHFS17yknKu98Mf/rDcQ0uHqnSYblOV9Tbr2p7LTMvrHDRXVZPijj766Epfz9z0be1UtVDfr3/961q6JdTRRYDxqFroKQE+VYPSZ8r9t8mfZ9dddx3p637zm98UixcvLj8zc7Gi7ge5f821G0y61g061w1r/fXXL3bYYYdiVH07n+3b+J/5a137Fr/85S+L6667rpbvBXWwhu6urq03U6ixyhl/HH/88UVdqiZapSvwJJM7kgRYZ6Hvqr/zYX/2KnsMicFN8mcVfdpjqPI+SULdAx7wgEqPn/OePu39dUHVdeGPf/zjyrHwM8eynC1WkY7nQPXPd/Y4uzZe23uhKZ1NrMsG1P7771885jGPKY455phGE+yqbqxVbYEOjNcd73jH4tBDDy3mz59f6fv885//LLtznH/++bU8ryrfJ9Vph60qNFvG3ZNOOqmYlFRO3nnnnSt9jyRkV10AprrgN77xjWLS0pJ5VDnUrHPjsUmp4plDsVGuZz/72UXfVT2cr/N7zeXgOuNulQPu4447buSvBcY/H0kAb9X5VQKYuhjg12UPfOADi3e84x2Vq1mlKtZee+1VXHXVVZW+T5J/qszPEwDWZVW7i2SeOG3MH8YnARFvfvOby+6VVarl5rM3rKrVeQWyUZU5T7XP4TjvN1XmAgkUqNKJd5olaOqVr3xl8aUvfalSct0oY35XdW3PZaaqReVSTX9U6eL1rW99q9LjMzd9Wzul4EbVALCf/OQntTyX//7v/y76rK1z+SqficH85vvf/34tzyV7W6N2lKzr5xk1sS4dahO8PJckwxRpWdnzXFHi3UzpkDdq9fL8rFX2tfp2Pts1mQdVHXfqOlsy/lf7PUzjvmUbWEN3U9fWm1X36OO73/1uUZfElVZN7Jh0h5c6u651bY+hSoe9rFv7UkgrxdWrFDBdUSfpuTrjjDOKSy+9tGiKM9L614VpHFHXWiNFhqoWLJJYB/XEDncxhtLeC03pbGLdwF/+8pdiv/32Kx75yEcWX/jCF8rN20nKgWECAqvYYostans+wHjssssuxfvf//7Ki7Lzzjuv7FxXtcJOVFl8bLTRRpUf/+c//3mt1Tjn4j73uU+lr8/B5BFHHFHpe3zxi19spBLkve51r0od+wSoMKiOWUd3oBzcVg0amGtwSxI4qnzmf/e734389UD75yMJTmhDF/O+edzjHle88Y1vrCVw7PnPf37x73//e+TvUbWaYtfnSFW7i1x55ZXFNDJ/GK/73e9+xaMe9aiRv36Uvbuq7/WqSbxgzlPtczjO+02VMT9zkDoDlabRjjvuWKlQ0aTPa5rUxT2XutZmVQJu0iXxsssuq/T4zE3f1k7bbbdd2Ym5iq997Wu1FGftewJMW+fyeY9stdVWlb7H5z//+Vqq+ef7VJGEtarnZ3e/+92LNddcc6Sv/cQnPjHn+9PKEvbnGuCbe+WoQbz/8R//UXR5bdDE+WyXrLPOOmXR3CqOPPLIyp/tjF19T6xr6/jfd9bQ3dTF9WaV91r89re/Lf7whz8Udbx2VTu2VV1LjeLYY4+tLa61SjGcLu0x5P19wAEHFH1RNWGpjnlpXe/TUTkjXXZ8a5XYwvjc5z5X1KHqOneDDTao3IEPpknVfayjjjqq7DTaFfZeaErnE+sG0qYyk+NB9fyqyW5znZC//e1vr/Q9snm89dZb1/acgPF56EMfWn7mq1YiyniV5LqqAQNVqrRVXWBHuvhN2sMe9rDKr3+CNS644IKRvjYJdQcffHDRhCx873//+4/89Wmvnk3DpqRj3qc//enGHp//67iY5NCqUsXk4osvrvQ95np4/YhHPKLS47zvfe8rmpLX6MADD+xVhX76p8n5SMa0uQbuUL9nPvOZxUtf+tLK3+f3v/998cIXvrCsrDiKbB5WCZb55je/WZx99tlFExKgk8ev0g25SgeZqpU/28z8od1V8TJ+D8t7naaZ81T7HI7zfvOQhzykmDdv3shf/+EPf7ixrpbp1JXHP+WUU4o2m/SY31Vd3HMZ2GyzzSrP6VOlfFi/+MUvyq6ITEbf1k45R6iaPJPklVRYr+K9731vr8bCrs3lc+ZUReIRvvrVr1b6HpkHVE1cTTJagg6rBqUmuW4Uc+22N5ekuRSZHHfyftWxoY/ns11z73vfu9LX535XtVBrfk/TWtBqUh2LJhHz1UfW0N3UxfVm1XlWAr8Tn1UlADxxnYkhrSNWbNJOP/304uSTT678fQ4//PDKcUrD7gtV3WNI4P+111479NcdcsghZUHPvqja2bXqvDTznDrGpWldazYlxVJSHLOKn/zkJ5X3QlJgouoY9uAHP7hyAwyYNg9/+MMrrXOzv9qUjAkf+MAH5vzf23uhKVOTWDdz0pas+cc+9rHFk5/85OKwww4bOYFiRRYtWlS89rWvLQ9VqkhW/aqrTt2vAabWHnvsUX726+i2meS6KhUFR60cOUjuGzV4OXLI96tf/aqYtFvd6laV29Gnktfznve8oRMbL7zwwmKvvfYqg56asvvuu1f6+re85S2Vfu/DyiZnEuqe+MQnFi95yUumclOiiz71qU9VSvRKwGHVZLUcpt3lLneZ0397u9vdbugqaLMXZl//+teLScrr+653vas8MMhmddVNTWizKvORUQIvZ/r4xz9enHvuuZW+B9Xsu+++xXOe85zKL2Pmlfvss8/IQe1V5kgZo9/85jfXUmF+mMfMfPrRj3508brXva44//zzKx1qVqk8mMORUQ4o265v84cECKSa+iTnHFWCg0c57Nxwww2LLldOpfvMeap9DpN0k32VcVh33XWLBz3oQSN/fQLBJx2cnP3APGYCGj/60Y8OHRh+9NFHT3R/Z9Jjfpd1bc9l4Da3uU3lPcD//M//HKoTdcaFrAEmOQ+nf2unRz7ykZWfQ4J5R02MyN5437sVtX0uX/XMJRIUNOoeV95beW9XrRj+uMc9rqhD1YSzlbnnPe+50v9mm222KTbddNOxPYfNN9+8fIwq+ng+2zV1jP8pgDHq3nM6LFVNZJgGVRN+kwBE/fq4hp4WXVtv3v72ty923HHHSo/3u9/9rvydj+qDH/xg5bPIzF+22GKLoglJCszef5W4tKpduBOfNWxXsKp7DHmfv+lNbxrqa5Lg1VSR9KbMnz+/0tdX/WzkPGuYfaBxcEa6bI9//OMrv7bZ/xl1Pz9z6BQgr6qudS5Mk6qf7/e85z3FJZdcUkxSOtEmVvvZz3528Zvf/GbOX2fvhaZMdUZXNqwOOuigsovdk570pLJLUbr1JCluVFmwHHPMMWVyTSpkNF0tC5i8JMTl0L+qM888s9h7771HXmhWOYzMODjqZnQW10nQasrTnva0yt8jneeS7PWjH/1ozge3T3jCEyoFPdchm9y3vvWtK/3cL3rRi8YePJ3KRl/+8pfLg6sk1J166qljfTyGk0PbV7/61SMnL7zzne+snMiSLjLDdBTIuFtFxqy5ft6rOO2004rXvOY15cHOZz7zmYkGOkJTqsxHUgBl1Epl6cT6kY98ZOTHpj4JAKsjEC1Fa9IBb5TEoMzT1ltvvZEfO+/DjN9VDijnWuDhk5/8ZHmfyONlblaHW97yliN/bYL4cjjSVKeecerT/CHv3RSASeXcvMcuvfTSYtyG2fiuI7CqapXbdDeZxO+T6WXOU+1zmMSs//qv/xpbsEXVMT8BetnHGLdUBD3ggAPK/Z0Ed42aqJHuRznzSCD/eeedV0zbmN9lXdxzGQS9Ven8GGeddVbx/Oc/f6WFLpOUlflKDtL7GhDbpL6tne5///sX66+/fqXnkuDxUfbUf/3rX5cJp7R7Lr/DDjvMuUPa8lx11VXl+JfA5WGkiOMLX/jCygUJN9poo7LgcB123XXXYlxSaHjnnXee039btcDmuJMH+3o+2yVJ5Nh+++0rfY8UaM05/rCFWnPP+n//7/8puFjD+J8zgEms0/qob2voadHF9WYdhRmTLJUGC6MkIqZAZ9OflyrSfS2xrqPIejtnBlULo6TIybCq3oMjMblJrlvZOizjQn7Ot73tbUXfZG1fZS/nt7/9bfGPf/xjpK9NwuYwHeXHyRnpsju9bb311pVe18RHJhHmoosuGjoxNnPoYb9uWWv1ca5Poavy2aiSczJqQ5JhZb767W9/u9yL3nPPPUdqYGXvhaasXvTEKaecUl5p+5xKGqlenkEmk/mtttqqrE628cYblxVUk+m6yiqrlIvSJAdkYZkNsBwgp81tnRm7dVTLAibv5S9/eS1tzZNwlAO3T3/602V1p2EkwSoZ/aNK8M197nOfoSo/5jA4SYVNJovstttu5ebROeecU3kRmAPxu93tbuUG4C677FJssskm5eZDDkKz2Mvr+93vfrc1iWG5N73gBS8oXv/614/8PZJgns2/bJxWSdKbLcEUeX+kZXQOBQXGtFveB/vvv38ZjDdMhbEksVQd90apLPSoRz2qrA466sZekjSSrPGqV72qDNyqs1tw5oV5z6erzemnn17b94WuyL2kykFcKr9/6UtfGmoe9P3vf78cw8YdyMfc5yf5PSYg7Ac/+EGll+3HP/5x8cpXvrKcp66++ty3K/L+eeYzn1mu90eVjb0E++fwrc7K6LkHZaMwc6S8PuPoKJZA6AR7Vq12nnn2TjvtVFZfTZXHlf0Obnvb25a//7bq4/wh1SMzv8t6KQEnOfR+wAMeUKmr4fLmkum+MapRKtZWrXKbpJ6s/3LYkNck799syA/2AJdnnXXWqXQwy/Qw5/nfz2ECG0d14oknlvsvud/c+c53Lvfksxe/sgCQW93qVitdtyZIOwHYo3bvSJeYt771reU4mgJBw1biXpHsMWX+mmCT7JvUWbH/Yx/7WPGJT3yiHNse85jHlAko6T5QpwT7f+UrXxn566uO313UtT2XyPO8053uVCmJchCUleSOFKbM2Vc6AuU9maT/JNylW3L2Wkedn1Fd39ZOeW+nwF7OX6rIZyOBICnmete73nWF/20CV1MwIwHAVQq9TpO2z+Vz5jJq4aeBiy++uHyPZB6RAKWVzW+ynspnqErXmZkB63XNXe54xzuW43bmL3VLTMZc5ynpDJNCx+NQR3BmX89nuyaFWqsG2We/LQXFktiT8WdlMufP+es4PkNdlPd4xuIqBVaSTHrkkUeWxaQS45Vk4uxzrWifLveHLbfccuTH7IM+rqGnRdfWm9mfzh591QLW6dz2i1/8oky0WtnnOwWI8v7M+rOqzDuToNKk7P2kWEjmmHOVZLTEtKUhRRUZbx/+8IcP/XXZ98t8uOqa7qtf/Wq5Rnzyk59c3odzdpX3fRJ2co/+3ve+V159TbpdbbXVys/DqOeDWbvmPpsE1GHOZDOW1NGNrC7OSG8u86S99tqrclGOJLhnzznnpNlbWdH8K3EbuXe/+93vrmUunHU6sGwp5DJKotrAn/70p+JZz3pW2Xk0ZwJ1Sox1Yk+yf5wzrCrsvdCU3iTWzc6GTUXwXE3KRkUOFoFueuMb31gu0L/zne9UDjjIoWAqsA7TwjabEdlIGFUO7JJg9f73v79MNF7ZxksOg7Np0/RhcDYH9t1337IaVx3y+ufqihzOpgJclWS//LzZ9MzmWw6Wklw+ahWL//mf/ylOOOGEMthhEp0xqM9///d/l1V0s+mVBNOVbUBno7pKEOXMA/RhD6+zkZeOSFW6hWbjNsEvCeDKmJvN14wnw8qGUALgc3CeAIh8niT30GeZj1QZG1JpOVXL3ve+9630MCzzro9+9KPF4Ycf7nPXMhmnExSUghFVg9FyCJaOBO95z3uGSmTKBnuCLFbWIWNFUgE/B72pGJ8516hVsDLPzuuQg9skCybpcNxVuKtW70+CbNYjw8ih7Pz584u26vP8Ic97cKicw+/MvRKEmsr822677chJgvm+SQbMwdiolW4z1icIalgJHsi6pcpnPHLQMMxhQxI0M76BOc//VsKsI7E4c7lhJKg6Y9fKpAjRk570pJHHpwQGZt8rY2fuHQkcGvU+l85dGfN/+MMfln+OI7F+IPeTzMdzJWgpgeiDMT+/s2ECY2a/HkkCTzDyqMWTss95+9vfvuijLu25DOR9UzWxLjL3/fznP19etFPf1k45A8m9p2qX7nymn/70p5fj6yBJPAkDSVbM/S2vZ4qzSh7t3lw+nQ3vd7/7leccVeQcLftb6Y6SAOyMx0nsS/fWJJUk+S6xCbm/1pVgnPVVnV1UslZMB78qhVSWJ3OUcfy3TSTW9fV8tmsS/Ju95Kpnl/l9JXAxc7qMK4nxyfifRNGck+bfMzZl/E+AIjeV9/ioyVsDv//978trru5yl7sURxxxhF/FSvR1DT0NurTeTBJUEjL222+/yo+f9ULma0kMTWGhdGNK4ewUWsh8PIW5E7OS+JWqXdoG0gmtzoJ7o0rSdj4bSZJZWTfQ/PxveMMbKhVjHMhab9gC8ZEzibw3q54XRubNmTPlYtn3vCq/64wNKTCZc7CsW1Ykc54k9R599NGt+lU4I122pzzlKWVx46rz08RnZExJQc+HPexh5RicsTfr/Pxb1rnZT8y9Kf+7rvf1KN0yoS+yN5nPY4pRjCpzyac+9all3HCKsM3lDG5Zst+VYhgprJm9pMw962Lvhab0MrGuLWTWQ7dlgyaZ+5kgZBOnimwopJrXoYceOufKUve9733L55BNyyqTpGyYpspbqoanGmWCHBN0k82nv//97+XPliCmqm266/T4xz++3BCvuhHfRdm4e/Ob31we4qdS66hyADeobJ4N0Cx+8/sfVJK+xS1uUT5WDu3SvTW//2yUpIPrmWeeWSZCZJO7yvuP5uV3mEVSNptSSXy77bYrDwQzBmTTIxuVWfhkjKprAzpJF6N0t8kGea6q3ZBy+PbiF7+4/DnTySXv+/z8Gfvy3k/HhATa5L2fQ9FUW0zQRzac8t5PUqtqo/B/EnhUpdJ9JMEkm6O5EniUjlmDzfuMRamGls9+NmSrVjVifHJonrnsnnvuWbmITQpXZE6cg5y53jMyfmdjP3PqKjLGJ6nvgx/8YDnfzuFf7hXplpMKy5kjDeZSWQdkfpR7RebVuU8keSpz6EkaZ8BZ15k/FGU1+wQ+D5IvcxB+hzvcoUx0yNw/AQgbb7xxeeXf8tnLXDDzoSRS5P2dg9msvbJBX3VdmPvGqBK8lgp30ARznvbfbzKHzAFg1WSezD1T/X299dYr14yp2JlxM2vIzAMyVmZ9nPE184aMk6m8nvX1YM3YVOGhQbenQdJBEtuy3s2Yn+rNmc8k8CFjfta/GfOTjJcxPz9P5jUJREswRObfVec0ec+0OQl/3Lq05xLZG04XA6Zf39ZO+dxlL/2www6rJZk5gSK5mK65/H/913+V3TazJ1xVgguTvJpr3FJMpu57bQK0xpFYl/fAXOWemb3B7M/XKYFidXQk7/P5bJdkLpwE8Owv9rFQa1tU6YrGeFlDd1uX1ptJSk5sTzrOVZWfJT9THQlbK5Pzyrl0K52U7O8nsTCxPXluW221VbnHk72g/M4Ty5ACDllr1SHvpXS8GlXmN5P4PfVd9qyPOuqoSt8jSc1Jjk6X3hQdyT5ikqby3sq8NLFh+W9yTt/G7slt37NuSoqEpmFEOpzXUegz40wS9XKNU8ae7FmNur8JfZF9rJwDJaa3yrwqXUhzZV83hZayN5G9k0HscM6PsleW8T/jQPZ2s1+R+cYgfriuueZs9l5oisS6Bid1D3zgA5t6eKDGCX2qcqTTStVN4VSCednLXlYcfPDB5aRkZVKJKBOaVCaqIovhY489try6JBWzU22wjQv3cUtFhle+8pVlp4iqsoAettIr0yeBLLnGLZtwj3nMY0b++re//e3l80zQYlU5jE5V2SqVZYGi3GBJ0FzVStu5n6cLUi66K8GTqcyeoPYEpVfxzW9+swwOSwXOuUowUh47m39VZY48Mxmp7QEq2dyU+L1s5g83lQ32SQUfLEvWcKNKwECbg3GZbuY8RZmMmwO1BAG3Vaqgpzpm1ST/QVD8t771rfLqqhx4NhkAnMrmdGfPJd0HkkRax+eH9uvb2ikV+FPApanEZ9o/l7/tbW9bdu5J4GFXpLJ4KpTXLYl14zBMYl2CKPPfV6m+Ps6fre/ns12Se93Xvva14s9//nPTT6XX438KvdJO1tDd15X1ZgqWJ2nnsssuK7ogifg5V2ibFN5OXFkdHQjnMtdM8t6oklCZeCYdd8dfXDJFwKokVkTOF1OsrGrBsiY4I12+rFnSeblLc7GXv/zlZUwksGJbbLFFWUQmn5lpLSRj74WmNN+vuodSCfatb31r008DqLHiXRYhqQZVVQ6fUxV7rpn8SehrusJJUxLM1dQhZw71nvGMZxRNyu8+m1HQJW9605sqjRupUvvhD3+4rKwNtEPuic973vN6Ox/h5jbccMPis5/9bLmZV9WXv/zl4h3veMdQX/Pa1752qGCtadlj2G233Zp+Gq1l/tAeu+yyS6UDsXSOShIpNMGc5389/vGPb/09MWvGdGSjWbe+9a3HEuzP+PZcosm1XYp0MFl9WjulwnI+IzSnC3P5pzzlKZ1JCk8F8VQnH9fZWzre1Cmdc4edn41jfKozabDP57NdK5B74IEHln/SjLvc5S7l2oB2soZmUuvNJKq9733vm1OB8TZ8Lt7//veXZ119PlOpGqif16/JfcS+7DEkbibdK/s8L3VGumJp8HDve9+76Mq+wQte8IKmnwZ0RroWp6PwNLP3QhMk1jXggAMOKDelgek6mP30pz9dy8bwcccdVx5qz6UV9wMe8IBi1113LZqw3nrrlZVNmpRuB89+9rMn/rj77rtvOTltWio8ZWEJc9H0wWE+q3Ucht/pTncqDj300DKpGWiHJz/5yWVl7yakWuLTn/70Rh6bFVeP+sxnPlPLwePnPve54gMf+MBQBygf//jHe1fNLkHQTR9etZn5Q/MSNFE18HPBggVltXloijlPUQZqpApym2V+mPlDgoBoTsb8LgTM1WVa9lwe/ehH11I8blgpyjFMp2rq0be1U/bz03WhCeuvv37x2Mc+tuizrszlc4afrg9tlhiDT37yk2PdH6/73PGe97znRL5mRVZdddWyY0Nd+n4+2yV3vetdy4Dipu61Td172mTagz27zhq6/aZlvZnEjsS35J7c5tc651FNFyBp+neeZhGZb1S1zz77lOuASbvf/e5XPOtZzyr6IvPCpvZBs5/Qhtg5Z6TLl7Pjj3zkI2WxgzbLuPvBD36wLHAIDNeBeprjpey90IT2rlam1Etf+tKyvTkwfTbaaKMygDjVlqo6+uijy6pPS5cundNBXxL7mthQ2nrrrYumvf71ry8e85jHTOzxnvvc55aJdW2QAKVUYk/QC6zM3e52t8a6HO6www7Fq1/96tq+Xw6sP/GJT5RBKUDzEiBw0EEHTTxwNlX4PvrRjxoLWirVyFN4oo6qkOkOfcghh8z5v89j5rHTHatPr3cXghSbZP7QrNe85jXlnLCqVKvcfPPNa3lOMCxznv8N4m0qKHUYt7vd7YrDDjuslg66jBbMkgPPPpmWPZcEOL75zW+eaMGGBH996lOfqr07EnPTt7VTzhJ23nnniZ+lfOhDHyq22Wabou+6MJfP7yvBfI94xCOKNsqY/9nPfnbsHVTq7OwWowSnJ9G7zrPPnXbaqfauiX0/n+1awPluu+028cfNeySJfX2XbqB3uMMdmn4arIA1dLtNy3oz8nO85z3vKffY2mb+/Pllp7qHPvShTT+V4hnPeEZ55tNUUa265sKZ+086tiqJXomhajo5cZISM9NEsaJNNtmkTNhqw+fZGemKpVBeirPUXbykzvVnCj8prg6jyX7+XnvtNbUvn70XJk1i3aRe6FVXLV73ute1JhkDGI8tt9yyPNSqI9nja1/7WnHggQeu9L/L4cl73/veiW0M5HHyePe5z32KNki1kmy+TaK9/Yte9KJyLI+5dBRckboCVLJJkQ2+/fbbr9XVvWjHZ+Wd73znxCsRJTAqCRF1b6gl4CfjZA57gHYc7L3hDW+YaFJdNoDrSNJgfBK0lHtADiSrSiBiiljMVYKl8t/3KdnsP//zP4s73vGOTT+NVjN/aEbWUXV1Gs8BYNajbTispZ/MeYpizz33LB70oAcVbXf729+++MY3vtF4lfG+SVHBOoPuumKa9lwyzr3iFa8oJiEF6r74xS+W3ZdoTp/WTlmbHnroocX2228/kcdLAaLs3TfV1aptujKXz/PLHsQLX/jCVlXLf+ADH1h86UtfqqW456QT60YJ4Mx5193vfvfW/kzR9/PZLslnOV2SJplIng7OCm7/35n4+973vlq6HzE+1tDtNU3rzUjR7EkUChjGxhtvXHz+859vTXGFrNGSZDLpIsNZt6QYSZ323nvviRVfSnfivLdyhtw36Ro3yY7G+cykqNkk1iZz5Yx0xTIPy97PHnvsUbTJk570pDLmo45CvdDnuWIK3L7jHe9o/Z7bKOy9MGmdjcBPhnpaWLZpobU8mURm4p4uR8D0SyBAJv05JKzqC1/4QrnRP5cDtSSXjbtTzIIFC8pKiE1U9VvZAdtb3/rWMqB/HBPEbLzk507y2sC//vWvyq9l3cGqX/nKV1qdZCTxrx3BI6lCPakKmYOq4+OqRJzFUwIlU+24zVXHvPfpiyS5JwF93EFHOUjKXEuQdDckeCoBaXWM0+9617vKwNu5ymOmC3S6nLa5Kn5d94nsk+SzMekD9q4xf5icrE9TJW/mOqoOGf9TCbWPB+S0Q9/nPLlvZY+kC8l1OTtIYFQOFeveh6nTJLuDjUs+D/vss095eNvXNfA07bkkGKuupPgVdSL68pe/LKmuJfq0dkow2eGHHz72z2rWZ23ufNaUrszlc1975StfWY7rTX8mMofJ2VcC2+s4d5yL/My3vvWtaytIOmoX4TrnweNIrIu+n892Sc6Ps756yEMeMva5ffZCnvOc54z1cbomnWQypm600UZNPxVWwBq6vaZpvTm4x3/7298e+5g8Fw972MPK59K2DqPpeJ3EpUmNm3e6052Kj370o7XHW2VenTlMiviMUxI2857tc3JOEssmMf/I/D77rW0rkuSMdOXy+U6idgoeTDpxd7Y8fsaGt7/97WNfS0GfOoUfddRRxc4771xM296uvRcmadUu3+jTxviEE04oJ8ZPecpTyhbDbVvYPv/5zy+++93vqkYIPZP28nVtOmSDLK3qV+ZRj3pUmcQ7rrEwAahJ3Hr4wx9etFUCPr7+9a/XetiWQLGjjz66fH1nuvLKKyt933EEdCWI+pvf/GYZ4NeWe2I2+p785CeXQRm5b9O8W9ziFuW4ct/73nfsG2qpoJsKh+Oeb+2///5lgt2DH/zgog0G1XRf+9rXFj/4wQ9GDhyALkoxkSRRjevgIt24cq9rW4A5K59PJci6jgSEAw44oJzvDSPVML/zne8UL3nJS8r7YBvk3pC5awI6X/ziF9ca/JDiHKms7yBi+cwfxi+b9pmfPeMZzxjL98/nOveDVKGFJvR9zpM9jUMOOaRc87Q9MD6BrXvttVcZJJWOEW1JYktn35e//OXl2UFbf89zlc5PmX/k5+lrUt007rmkSv1LX/rSsSQR5zzvq1/9avlz0C59WTslgCtBqqmMPq6zlLzHH/rQh47l+3ddl+by6QyWz0TW2JNO0s/4mySqnE/lMzDp7nl1dVqsMs+p6z2S/ZFxBpY5n+3WftDBBx9cFisdx7w1+3LpBDKuvZCuS9LGt771rTKJhfayhm6vaVpvDjpeZW8p4/Jtb3vbYtLymHnsFH1oa1OHHXbYoVwHZX0xTve+973L9dG49jlTHCKxveNIpMy9PUVikiiU/9136Zj7xje+cWzng/e73/3KtVzbkuoGnJHOPRE1e9IpIDjps+QUdnrqU59aPv7sOEygnvlNClUniXbc84dh5rAZdxL/nqJRo7L3wqS0t7XHEDfbTNpyLV26tDj11FPLZLtf/OIXxe9+97viuuuum/hzyuIvB4OZfLSp5TEwWal+mOoaL3vZy4olS5ZU+l5J0kt1lZW1bk9HkBzypZtHNqZvuOGGoqpsPiQAqYmDw1Fkgy8TxCSzZPPnpJNOGvp75DAlh7up5rO8KpZXXXVV5Spf45BkzgT45R6UoPNcp59+ejEpOdzNJko233IwkkPTvgdVtVE2RVOZM4uWj3/847WMFbMrhSSBYpKb0NlYPvTQQ4vTTjutDOg77rjjimuuuWaiC8FUWktyX977mQ9CX6Uaew7J3/a2txU/+tGPavuM7bvvvsWznvWsVneoZPke97jHlYUJDjzwwEovU9b9ORTKnCffc5h7X+blmSelM0YSfv7+979PNCAh89Qceuc+kUIc45LXJpX1E8STqpE5nLjwwgvH9nhdNi3zh4yLWf/96le/Kq9f//rXxcUXX1z7851r4OPee+9dzgcnUe08gQXZA8wa8Cc/+Ukj+4D0V9/nPFnrP+95zyv22GOP8jOYoO+//e1vRVvd6la3KvfLkpSRsSP7Z1dcccXEHj97arn/Z3xM0aqtttpq5O+V/a7/+Z//Ka+M+eedd17RhDvc4Q7leyAHmm1JWGyDadlzyR5fxqPs7aXrSh2f79vd7nblXH6XXXap5TkyHn1ZO+W8JZXRB5+pOsbS3LtzD09Sap87JUzbXD6/y6yxBwme+UxccsklY3u8nMk98pGPLO+x6e7ZlJyNpdhmVTm3rFJsIkUcqq7Ts+7OZ36cnM92R+4z++23X3mfyVruT3/6Uy3zpsc//vHFq171KmdDK7HpppuWSSx/+MMfyr24448/vrj66qsr/w6o3zStoafJtKw3Z8rvNwlXKYiU9UdiPccpxapzdvLYxz62E3sZib3J/PPVr351bfuPM/f2spf/ile8Yuz7kHnvJu4tSZuJo/vXv/5VS1HPFAXKeMX/yZo05zQpfJ59wzqksHuKXWe+M+mCH8NyRjo3OY9861vfWo4Bn/vc58q9/To+lysaA3Kun3VuW5J9YFplnM65XeY6mcOn+NdvfvObiT6H3Juzr5SiY/mzro649l6YhHafzo8wICSYIdc+++xTLFq0qNyQ+f3vf1/+mUCpHIwkEK/ux02mb5IIstjLh7ftgQ/AZGRykODhVIWpOvakws4gaWpl1Vaz2ZVkuATaJJB2lEOnJAanYmsqhSwvSTgbatl4GtU4D5czHufKoX82mH75y1+WhyPnn3/+zX4XqcBy61vfuqywncnc/e9//5UmRp955pmVnt+4N39zSJgNk1x//OMfy4OJn/3sZ8Upp5xS6wZrDnlz3013rlw5oGy6ZTxz3yjNJmnGqXT/qWOTOhtq+Z7jqvY818C+jIFveMMbyvf9T3/60zJA49JLL629Wl/e76l0m/d+xg9JpHDTivKpNvTb3/62TOz5/ve/XyxevHjolyj358xFnvjEJxbrrbfeMv+bzTbbrNJ8pK5NHFYuQWhJrksV0Coyl0mHnPzuEmw2jHXXXbcsGJErCUg//OEPyzlS1bndsipw5n2Ze0TuFfnf+btJymcjh60J6jnjjDPKnzfz4QRFX3TRRcVll11WXHvtteVns+59kq6ZhvlDAi5z5XMW+T1nDM57O7/3XONItstaKj9TCl6lm0ITh9hZw+V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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 511, - "width": 827 + "height": 1023, + "width": 1771 } }, "output_type": "display_data" @@ -1238,36 +1160,39 @@ "id": "74a6c277", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:24.027416Z", - "iopub.status.busy": "2026-05-28T17:00:24.027318Z", - "iopub.status.idle": "2026-05-28T17:00:24.150868Z", - "shell.execute_reply": "2026-05-28T17:00:24.150385Z" + "iopub.execute_input": "2026-06-01T17:59:33.934141Z", + "iopub.status.busy": "2026-06-01T17:59:33.934048Z", + "iopub.status.idle": "2026-06-01T17:59:34.138886Z", + "shell.execute_reply": "2026-06-01T17:59:34.138478Z" } }, "outputs": [ { "data": { - "image/png": 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S4hB/RR66WENfC+3cWZ4umJr70AIL3+ITfR3197XnGStCu5roMdXRNvbFdC+88II1FiUZ/i6Vl75u+jdoyZIlxdumTZtm5Sc1J5loNPdlL4bSv7Oa64pkHkjPCXtH1UiimANANFHMAQBh8l7ktNNiCW3R6UuDMP3waQ/67r33XqvC2/cDuAb22pJRL1hqZbk9WLzvvvusC57eC7qh8H6Y1+f6v//7P8eFWt0P/TCsF1b1orQGp/bVEA899JCceOKJVpV3ItMP0ieddJJ10TgYvZCtXzpG4vnnn3d0qtDAVi84ey822w0dOrT4v7WVnr2YY8iQIdbc02iL5zmm1exaOHD//ffLueeeW+r7WuCkAaQWDGjnEG+Qr4kELUTQES+xsnTpUiuRoStXRowY4beVpr7uuuJHE0VeWsygQbwmarQAQs8H39VpmgAYMGCAVXyhx12LLbzee++9MhOgWlClq2Psj6dtH/0dU2+wrl9aZKJJDe97V2fvaqGOJlTi9Z5I5HPG/rqVpxjDtxgkEq1kAQAAgFjwXvS0F1TrRc7zzz+/zPtGuzg9kosofAs3tFBDC/8D0YvduijCH71vsGIO344emmNxQ4F/PBYTJEqBfyItCon3Yg0dIeSN1/V5dbSI/r93NJN2tZg0aVKF8oo6Olr3zV4Udeyxx1o5Cf3/ZPm7VBGa37EXc3jHuGhhnr63dKFXoC6obqPHRY+/nb43Ivme1240OsY3GP27b8+B6d8mHblcFn0PAkC0UMwBAGEoKCiwCiN8K7aHDRvm9+KxBkZ22kHB9yK7r3bt2lnB1AUXXGA9nzexosGBBoLh0A+SemHf3xxFL70wrXMjtcWl9/fSYFVngeq8zkR21llnhfXzeqH8pptusir/tUWm99hrQFfW6xYPbjjHtDjE30V53+fT/Xr88ccdCYpYFnN4C198Czl8aYCmbSm9K1y0UMY7R1lXfgRrM6xJC30OTdLY20LqihR/xV5Kn0dbotppC1BtVVoWTRzqfbXowfs66muozx9ovIcb3hNuPGc04WZf1aRFJf66LYVTDEIxBwAAANxKY3/9XK8XIPXCsV7k3b59e/H3NYegF7xC+Uwc7eL0SC6i0OfVi8veBRF6YVs7mur4zFAKMo4++mjZvHlz8fe0A2qoHT2CFXPEssA/ls+VaAX+ibQoJN6LNbRThh4DzYFoIYevOnXqhFQMFsjkyZOtgpEjR44Ub9NOHLr/+tjJ9HepIrQwRfNAvuOK9PzTLz1/9ffq0qWL9aWLEfVccKO2bduW2ub9exspeu5o/jsYe3GWd6FPWfcBgGgLv4cWAFRSejHzqquukjlz5pT6IKgfnn1p5badVmVrQBCKTp06lbpQ+umnnzqCmLJocKwBZbBCDi/9YK8rDOw0SI7XTNJ4803I+L7mbhHvc6xVq1ZWV5dQaNLAvpJB24/aV1fEIij09z71pQmMwYMHl9qu9/UXWPrS1R2a4PPShJPv3Gk7TfLY25JqQiCUQg4vDcZ1dZKXJmC1raZb3xNuPWd0Lm5FunLoqh+KOQAAAOAmc+fOtWIYf1/6eVdjF73YqiMd7IUcTZo0sRZ8hDKGM1BxelnF2/bidPtnfr0oqSuno0Uv2tovsmrOw7fzaqCCDL0oP2rUKMdIjED7qnGgXki1X0ju2bNn3I+hW16vcAr87bTAP9ZCXRSio1e8yrMoxM67KKQsulijrEIOf4s17Oexd7FGeWiO1F8hRyRyXbqf9vyUvgZaVBFKIYdbzvNY0PNHi3MCLSDS/dYCKv0dtMuILkbUc0b/WwuQ7N1v4s3+HvKKZd4w0AhdFuoAcAOKOQDgfxdA9aKq75cGEE8//bRVgX3yySeXWpWhgdCDDz7ot2WdBl922gUh0Gp5f3R1vb0QQ4ORxYsXh3x//XAeTrW1rniw07aXq1evlspIA237qgTfloVuEe9zTBN/oRQLKV391KJFC8c2+yzMaAunil5XKlTk/looYxeoLa8aO3as47a9q0eovK097Ulbt74n3HrO+I5ICbeYQ9uvahLMS//2Vq9ePWL7BwAAAMTiouAtt9xiXTDX1vyhSJTidDvt+BCsg4adPQeknTXs3TX0YrsWdPijcbV2ZbD/nt7xE/E8hm54vdxa4J9Ii0LcsFijbt26cvPNN0dwr4qKqx5++GFr9LN9cdno0aOtorPMzMyQHscN53ksaccSXZAXbLSLnXbx0G4iugBQc8fa7UbfW24s5rD/HY2Vio7gBYBoYMwKAIhY7fC03V449KL53//+d78jVvSCo7dtp9fw4cPDeny9GKgfGO0XGbW9n++M10DsLf9CDah15qRelLS3lAwl8EwkWpWur8+WLVus4E4DA9+xOcqeMNB5iRpIavGOW7jhHNOEQ7jPt2rVquLb9rEW0aYzeEPVqFGjUkUF+v4o7/0D/Z579uxxFExpYiLcY6p85zSHU5AT6/eEW8+ZihZz+K7cINgHAABAotFYQBezfPXVV9aqePvFzGgXp3tHOnqL03V0RLT4jjsJVMyhYyTsHUv0fl27drUW83gvMGqxh7/42fcxg8XYsTyGbni9ylPgb48JNXatV6+euHFRiG+Hi3AXhdhHSuiiEC0qiAbvYg3vgoTyLNbQcSShFleEQoujdKzKpEmTirdpvkELDsId8eqG8zzWtHuE/t7abUM7mNjHvpR13D///HP57rvvrLHi8Rwn4m+hpBY2xRqdOQC4EcUcAFAOekFSP+Rq20d/fCvoNQAtz0xCDebsFxnDqcwvz8VEvY+9mGPlypWSDLTo4csvv7Qqz/V4FhQUhD1CQVsPBpqjGw9uOMe0zWw4fFci2TsZRJt9lUu4+6nFGaEmm/wFoPYVIXZaLKXnlpd2cvj4448lXL7jkHxnpbrpPeHWc6aixRgE+wAAAHAbXaV9+eWXB72IpwXmGgPqynzvSAO9CHjHHXdYnSAfeeQRR1G3G4vTy3NcND70XjjfsGGDNVZXx8sEKsjQeFA7euix0N/Ru0pff+a2224r9Ry+XV0DFXPE8hi65fVya4F/oiwKcctiDfu4ooratWuXXHfddVaOxCs7O1v+85//hL1QzS3neTzoOCftuqxfWqCjf6e0CGXZsmWSm5sb9L76918LZ7SAzXe8Uaz4y52F0304UueivYhPz59gY5IAIFYo5gCAYH8k09KsanVt9aYf3rQqXjtxtG7dOuhx27dvX4UuYHr5JhN8HzdSF68D3ce380Mi+vTTT+Vf//pXhX8XDSrcVMzhhnMs3DESqampQYsQoimcffVNYNjHi0Ty9/RtD6u3w+0Q5E9Zr2E83xNuPGc2btzoOGb6O/m+L8Jtw8lMVQAAAMRbgwYNQhoRobRQ+5VXXrG+vJ+5tUOHXhzUsQduK06vKC3M0NXo9uIL3xXp9oIM/Xxfp06d4sIMbzGHFsZrLGGPi7QAXS+e2gvUA3VYiOUxdMvr5dYC/0RZFOKWxRpt2rSRSNAOJNdcc41VUOWlnVdefPFFvyNwy+KW8zze9G+O9++OdrfQTkPLly+3uvHOnj3b0QXG7plnnrEKWQYNGhTjPS76d8iXFvXEku9CHT3PAxU0AkAs8ZcIAP63MuCdd96J2LHwraAPNBs13Auf4VxoD/cCdEWfz42ef/55a65mJMSy8CBRzrFwEhPxVpF9jdbvGa33l3dFnRvfE248Z3xHrJSnEIPOHAAAAEhkGhdqh4n69evLQw89VLz9s88+kxEjRsjAgQNdU5weCToyxV7MMXPmTEcxh3Yy0BXt/jpr2Me0aEykF0b1GHnp/ewX0zXfFOhiYCyPoVter2gV+GvxUagFEJo/OeOMMxJyUYhbFmtEarGTFpzZzyFdSPfyyy+HvcDCbee5m2hRnuY59Ev/zmmxy4IFC+SFF16QGTNmOH5Wv6fnk/7Nj3X+xt8x1n+TYsl3oQ4jdAG4BcUcABADkfoAHO0P0vbq9UQ3b968UhettT3f8OHDrXaQbdu2lYYNG1rBubbN0+DGbsiQIbJp0yZJFIlyjiF+sz8r23uivMUc4Qbr2r529+7dxbfr1q1rHUcAAAAg0VxyySXyySefOMZvarcOf8Uc8ShOjxTfsSdakKH5EG88rKv77R0g+vbt61iprRcYvSv1ddSKvZjDPp7F33PF6xi65fWKVs7hySefDDle1e4goRRzuHFRiFsWa5R3QVFZ5+WoUaPKXcjh7/ES6e9SrOi52bNnT3nttdfk3XfflQcffNDxfe3iofmj8oynqQh/o77L03W6IlioA8CtKOYAgCjQsSyRaAPp22LO93GD0ef0tgEN5z520R4rEs1uFxrI2+mFaq06D7WlZzgtLSvrOYaK8X1/aZLvzTffjNphTfb3RKSKOXxn45aFYB8AAADJdJHvpJNOchRzzJ8/31r1X6tWrbgWp0eSjnHQooxVq1ZZt/fs2WOtyPYWdtsLMrQAvkePHqXGtHz99delxrH4u23v5OErlscwkV8vJO9iDS2U0s44Xv/+97+tv0NXXXVVuR6P8zz8Ar7Vq1fLhx9+WKrALdbFHEuWLCm1LdYjbH07c4SbHwKAaKGYAwCiwDfJUd4AyT4zMtziCp1/GG4xh+/MRN/fI1gLSW1DWtFRIZGya9cuaw6kvV3ls88+G/JFa22J6m9Wo5u44RxDxfi+Pzdu3Bi1Q1oZ3hPxGrNin4etaMMJAACARNa6dWvHbe1YoRe4fDtMxLo4PdJ0f73FHEovKHs/y9sLMrp37y5ZWVmlCjS8xRy//fabFY9rbLV9+3ZrVbuXdvDwPZ7xOoaJ/nohORdrvPjii3LrrbfK5MmTi7fpmI/Dhw/LLbfcEvbjcZ6Hb/To0aWKOfTvWqzNmTPHcVu7nh5zzDExe35dJGf/vTVvpkV/AOAGzitxAICI8P2wp6tYynOhdvny5aWCs/KuFi/PfYI9n29LRQ20wm19GK3gURMy9q4fXbp0CSsA0ONQnuKUynaOoWJ8iwa0sEZHdkRDZXhPlIcmXe2zhXX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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 368, - "width": 1081 + "height": 501, + "width": 2367 } }, "output_type": "display_data" } ], "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(11, 3.8))\n", - "az.plot_posterior(idata_partial, var_names=[\"mu\"], ref_val=TRUE_MU, ax=axes[0])\n", + "az.plot_dist(idata_partial, var_names=[\"mu\", \"tau\"])\n", + "axes = plt.gcf().axes\n", + "axes[0].axvline(TRUE_MU, color=\"C2\", linestyle=\"--\", label=f\"true = {TRUE_MU:.2f}\")\n", + "axes[0].legend()\n", "axes[0].set_title(r\"Population mean $\\mu$\")\n", - "az.plot_posterior(idata_partial, var_names=[\"tau\"], ref_val=TRUE_TAU, ax=axes[1])\n", + "axes[1].axvline(TRUE_TAU, color=\"C2\", linestyle=\"--\", label=f\"true = {TRUE_TAU:.2f}\")\n", + "axes[1].legend()\n", "axes[1].set_title(r\"Between-market SD $\\tau$\")\n", - "plt.tight_layout();" + "plt.tight_layout()" ] }, { @@ -1288,10 +1213,10 @@ "id": "a8ee0c5e", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:24.152347Z", - "iopub.status.busy": "2026-05-28T17:00:24.152239Z", - "iopub.status.idle": "2026-05-28T17:00:24.155238Z", - "shell.execute_reply": "2026-05-28T17:00:24.154796Z" + "iopub.execute_input": "2026-06-01T17:59:34.140359Z", + "iopub.status.busy": "2026-06-01T17:59:34.140263Z", + "iopub.status.idle": "2026-06-01T17:59:34.143762Z", + "shell.execute_reply": "2026-06-01T17:59:34.143250Z" } }, "outputs": [], @@ -1328,10 +1253,10 @@ "id": "e4d6eb38", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:24.156412Z", - "iopub.status.busy": "2026-05-28T17:00:24.156332Z", - "iopub.status.idle": "2026-05-28T17:00:29.024766Z", - "shell.execute_reply": "2026-05-28T17:00:29.024321Z" + "iopub.execute_input": "2026-06-01T17:59:34.145276Z", + "iopub.status.busy": "2026-06-01T17:59:34.145189Z", + "iopub.status.idle": "2026-06-01T17:59:37.211234Z", + "shell.execute_reply": "2026-06-01T17:59:37.210473Z" } }, "outputs": [ @@ -1339,35 +1264,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [mu, tau, theta_offset, baseline_logit]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 2_000 tune and 2_000 draw iterations (4_000 + 4_000 draws total) took 4 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + "NUTS[nutpie]: [mu, tau, theta_offset, baseline_logit]\n" ] } ], @@ -1400,36 +1297,39 @@ "id": "a5796f5c", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:29.026195Z", - "iopub.status.busy": "2026-05-28T17:00:29.026087Z", - "iopub.status.idle": "2026-05-28T17:00:29.151208Z", - "shell.execute_reply": "2026-05-28T17:00:29.150714Z" + "iopub.execute_input": "2026-06-01T17:59:37.212716Z", + "iopub.status.busy": "2026-06-01T17:59:37.212623Z", + "iopub.status.idle": "2026-06-01T17:59:37.407819Z", + "shell.execute_reply": "2026-06-01T17:59:37.407400Z" } }, "outputs": [ { "data": { - "image/png": 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N7TYYAKI3L0888UQ55JBDEl4g6XHo7Em9eRmkxz5ixIiUHRMql94UOfTQQ00KM/9CVbPR6A0VTZXuW7NmjWl/ehPbpyUstERLKukFuh6PBnn4tMTQFVdcEZOyTy+ub7rpJnn//fdjSiS9+OKLKT0e2MmmdusPtuqNxmOPPVaOO+44E80fTy/BtJzRzTffbDqAPj3H3n///Sk7HtjLpnZbljfffNMcW6JrVAJAosfGtqvZ8/Q6NRiAN3jwYHOO1aDlkoLvdbaY1om+8cYbU35MAKqHbeco+jPRYVPbo08SLTa1vbLQr0gvNrY9+gXRYVP70zE+7Y9qGZDgRAQtS6OP8bRsyJVXXhkzeeHAAw80mUAQzgAQnfCngT4aiOFno6mqABD6G0ik6CyYhvQ/2UMPPVS4rnW0/vOf/yQsCaCZFXTfYPkM/QKoyvgYTRnkB3/ol4LeMEL02Nhu9Sa6BqVoZKy2y5KiYzWKVQfZzz333JjtiUoawV4PP/xw4YWzuuSSS0xwWvDCWTVp0sTUMwymp9PgC72ATSVte8HgD22DejEfX69R26VeVGsZDZ9GU3/00UcpPR7YyaZ2qz9Dyw/oDAMtW5Qo+EPpuVs7qpo2sE6dOoXbNSgk2GFE+rKp3ZZGB0vuvvvuwnb75z//uUp+LuxlY9vVa+Jg8MdRRx1lriFKCv7wS8ZpyUKCP4D0Yts5iv5MdNjU9uiTRItNba809CvSj41tj35BdNjU/nQcLziW17BhQ3nqqacSBn8oza7+zDPPmPspPg0oKE/pGFQfvWenWUY144tO3NdM/t9995257q+OrM70NxC5ABBNmbR69eqYUhQl3YTxZy/qQGEw84HevKkKepx//etfCwfWb7vtNqlZs2aV/GzYxbZ2261bN5NBYffdd0/6PZdeemlMHT2thean2IbdtCzFK6+8Urjevn37YgE98Rc7mnXDp8FHevGdKlruSC/QfXpR7M9AT0Qv8MeOHRtzoR8MqEJ6sq3daiYm7XDGBymVRKPDdXZCkAaBIL3Z1m5Lc+edd5r6yeqkk06S/v37V8nPhZ1sbLt6rfnkk0/GnFd1ECYYJA0gGmw7R9GfiQ7b2h59kuiwre2Vhn5FerGx7dEviA7b2t+kSZNi1jUbiGZPLU3Lli3lhBNOiDmmb775JmXHhMqjWfC1/elk6FGjRpl7aPGBR1WF/gYiGQAybty4wuc1atQwA9ZlOfXUU0v8jMp0xx13mNTD/peDZlxANNnWbncmECkzM1MOPvjgmG3MZg8HjTTWiwafno/037M0mnGjc+fOMbUX/TRnFaUXvf5NR3XEEUeYCOrSdOjQQfbaa6/CdU27pinekb5sa7c7c97U1JNBnDPTn23ttiRffPGFvPPOO+a5Dl5cddVVlfrzYD8b265mUgoe0w033EAwPRBRtp2j6M9Eh21tjz5JdNjW9kpCvyL92Nj26BdEh23tLz5zx4ABA5J632677VasrA1QHvQ3ELkAEM2g8OOPPxaua0CFRtSVRU/MrVq1ivkSyM3Nlco0YcIEk3JKNWvWzKSLRzSFqd2WRaNu46NyYb/4mnRaniIZhxxySEytzYkTJ1bK8QR/Tmnijzu+Jh/Si23tdmdo4FJQMBMU0lMY2q0OhNxyyy2F61r6pUGDBpX28xAOtrVdTfnr96WUlk0cNGhQSj4bQPjYdo6iPxMdtrW9nUGfJJzC0PboV6Qn29oe/YJosa395efnx6zXrl07qfcFS0IrsliivOhvIHIBIFOnTo056ZYno0ZwX83KoanDKotemGi5Ah8D69EWlnabjGD9PVVWBC7soLXqfBqQ1q5du6TeF99Wv/3225Qcz5QpU2Iy4mhtvZ2Jnk7V8cBOtrXbVJwzKQOX/sLQbv/1r3/JkiVLzPO99967WKYaRJNtbVdLHwZnbdFOgWiz7RxFfyY6bGt7O4M+STiFoe3Rr0hPtrU9+gXRYlv7a9u2bcz60qVLk3qfP+ZSUjAmUBb6G4hcAMi8efNi1nv37p30e+P3jf+sVNeK+v33383zYcOGmfIGiK6wtNtkzJo1K2Y9mUwmqF6aqm7jxo0xM2irs/1pMNTChQtjLoDr1auX1Hs1nV8w0rq6/z8gOu02VefMYFYnpJ8wtFstQ/Tcc88V1soNZgJBdNnYdn/44YeY9cGDB6fkcwGEj23nKPoz0WFb29tZ9EnCJwxtj35FerKx7dEviA4b29/w4cNj1j/44IOk3hfMZlm3bl3ZY489UnI8iAb6GyhN+YvUh8T8+fNj1lu3bp30e+P3jf+sVNGLkueff948Z2AdYWm3ydBZmB999FHhekZGBhcvIRDfZnbZZZek36uR1prlxa+9uGDBggofj0ZAayq+nTkeTZenN9D9AJJFixaZYyMTTfqxrd3urLfffjtmfc8996y2Y0Hls73dahm5m266SfLy8sz6BRdcUKy0G6LJxrb7yy+/xGRP8gf/dFDwrbfeMulQFy9eLJs2bZLGjRubmVla+/nII49MepYYgHCw7RxFfyY6bGt7O4s+SfjY3vboV6QvG9se/YLosLH99ezZU/bbbz+ZMGGCWdfSMv/973/llFNOKfE9zzzzjHz99deF66NHj5bs7OyUHA+igf4GIpkBRAf8dvZGevys22XLlkmq6ReMDqz75T7OP/980jvB+nabrKeeeiomFfeQIUOkSZMm1XY82Ln2V54MBBpwEczyEv9ZVX08Kng8OuiwevXqCh8T7GNbu90ZGqj0zjvvxJQ7Ouigg6rlWFA1bG+3TzzxhMycOdM879Kli4wZMyblPwPhZGPbDc7Yat68uckA9sILL5ga0Pfcc498//33snLlStm6davJvDh58mS577775LDDDpM77rhDtm/fnpLjAFD9bDtH0Z+JDtva3s6gTxJOtrc9+hXpy8a2R78gOmxsf+r222+PmWRw6623ypVXXinffPONmZDguq7JXDJp0iS55JJL5M477yzcd8SIEWbyDVAe9DcQyQwgwZvPfvqkZMWXGIj/rFR47LHHZPbs2YUD62effXbKfwbCx/Z2m4w5c+bIo48+GrPt4osvrpZjQdW1v/g2qAEXmr1Dsxulqv5wRY4n0echPdjWbstLA0FvvPHGwpkH6phjjmFWepqzud3q4P9DDz1UODAyduxYqVWrVko+G+FnW9vVc2gw9a8GgGiN+YcffrjM9+p599lnn5Vp06bJ448/LvXr19/p4wBgB9vOUfRnosO2tlde9EnCy+a2R78ivdnW9ugXRItt7c/XokULeemll0zgx4cffmi2vffee2YpifZD9d7gueeeayaEAeVBfwORzAAS/yVQnhN4/L6pvpGuKaoeeeSRwnUG1hGGdpsM/ZlXXHFFzEzKY489VnbfffcqPxZUbftLtH9FAy5SfTzVFRSFaLXb8vr3v/8t3377beG6Zku6+uqrq/QYUPVsbbc6G0Uz1Pnlt0aNGsV3OKxuuxr8oe3Wp+l7/eAPLQdz2mmnySuvvCJTp041mUBee+01Of30081rPn3thhtuqNBxALCDbeco+jPRYVvbKy/6JOFla9ujX5H+bGt79Auixbb2F9S0aVN54IEHzATwsjKTaKldncCgmT8I/sDOoL+BSGYA8QeufVrXK1nxsxxzcnJSdlz+BbB/g5yBdYSh3Sbbtq+77jqTASR4EcOAejTaX6I2GP95YT8e2CnM7eSjjz4yg60+zbbwl7/8hZJZEWBru3311VdNeQzVuHFjueaaa1LyuUgftrXd+MEOPxuIDuhpIMiwYcNiXt91113NcsABB5gSnP718rhx4+STTz6R/fffv0LHA6B62XaOsu14UHnC/G9NnyTcbG179CvSn21tj35BtNjW/uJLctx1112mjxmcrJDIb7/9ZrJ/DBw40JQn7dq1a8qOA9Fg8/8FRCAARAfRlixZUmmff9VVV5n0SGVF8WmK32QjAePrQGsd6VR58cUXZcqUKeZ5o0aN5E9/+lPKPhupQ7stP71pOX78+ML17OxsefDBB80jwiHRebM84s+dFU2dZ9vxwE5hbSffffeduYYJdga1XBY3H6PBxna7cuVK+dvf/la4fu2115ogEMDmtlvS+y+//PJiwR9BQ4cONbWYgzWXn3zySc7BQMjZfo6q7uNB5QnrvzV9kvCzse3Rr4gG29oe/YJosa39+WbOnClnnXWWrF27tnCi1xFHHGEmgffs2dPcJ9FJC9OnT5c33njDlIbRcUHNVnn88ceb0qRkUkc6/F+AHdK2BEx83a/yRC7F71veGmKlRf/dc889hesMrCMM7TYZDz30kKmhHvyi0G09evSosmNA9ba/RPsH6ynacDxV+X8C0W23yZg1a1bMzHN1yimnmAAQRION7fb222+XDRs2mOdDhgwxJdwA29tuou/2hg0bmtIvZdHzrpbdCt4EW7duXYWOB0D1sv0cRX8mfdnW9pJBnyQ92Nj26FdEg21tj35BtNjW/tT69evlnHPOKQz+0EwMmpXyH//4h+y1116m76nb9HHvvfc29wn1/omfsWHr1q1mXFCD6IBk0d9AaSITAFKeOl7x+6bqpuFtt91WmJZYB9Y18g+wvd2W5fnnn5f77ruvcF1rqt97772mjSM67S9+f20HFY0Yjb/4rsjxJPo8pAfb2m1ZNL3jmDFjCm+0q5EjR8rNN99cqT8XdrGt3X788ceFWbx08GHs2LEV+jykL9varmZq1M8J0sG1ZD5XU53qwJvPn3kFILxsO0fRn4kO29peWeiTpA/b2h79iuiwre3RL4gW29qfeuSRR2TFihUxWSlHjBhR6ns0C/Bll11WuK4TEjRoBEgW/Q1UawkYvTFcmXWD2rZtm3B7y5YtY9aXLVsmrVu3Tuozly5dGrPeqlUrqSgt+6J1Nf2B9VtvvbXCn4nKQ7tNzltvvWUi+32a1kxLwWhddYRP/HlTsxYlS2+aBPeP/6yqPh7/vB+8mG/atGmFjwn2sa3dlkZ/lqaCDEbzDx8+3JTdyMh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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 368, - "width": 1088 + "height": 501, + "width": 2367 } }, "output_type": "display_data" } ], "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(11, 3.8))\n", - "az.plot_posterior(idata_partial_bern, var_names=[\"mu\"], ref_val=TRUE_MU_LOGIT, ax=axes[0])\n", + "az.plot_dist(idata_partial_bern, var_names=[\"mu\", \"tau\"])\n", + "axes = plt.gcf().axes\n", + "axes[0].axvline(TRUE_MU_LOGIT, color=\"C2\", linestyle=\"--\", label=f\"true = {TRUE_MU_LOGIT:.2f}\")\n", + "axes[0].legend()\n", "axes[0].set_title(r\"Population log-odds effect $\\mu$\")\n", - "az.plot_posterior(idata_partial_bern, var_names=[\"tau\"], ref_val=TRUE_TAU_LOGIT, ax=axes[1])\n", + "axes[1].axvline(TRUE_TAU_LOGIT, color=\"C2\", linestyle=\"--\", label=f\"true = {TRUE_TAU_LOGIT:.2f}\")\n", + "axes[1].legend()\n", "axes[1].set_title(r\"Between-market SD $\\tau$ (log-odds)\")\n", - "plt.tight_layout();" + "plt.tight_layout()" ] }, { @@ -1437,7 +1337,7 @@ "id": "f7537167", "metadata": {}, "source": [ - "The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. \"Meta-analysis\" is not a binary-outcome technique borrowed from medicine. It is the same Bayesian operation regardless of likelihood.\n", + "The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. The log-odds parameterisation carries a structural advantage the probability scale does not: a given value of τ means the same degree of between-market variability in the treatment effect regardless of what the baseline conversion rate happens to be. On the probability scale, a τ of 0.05 is meaningful heterogeneity at a 5\\% baseline and negligible noise at a 50\\% baseline; on the logit, τ is scale-invariant in the way the analysis needs it to be.\n", "\n", "## Predicting the next experiment\n", "\n", @@ -1460,24 +1360,24 @@ "id": "6d7d4a17", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:29.152606Z", - "iopub.status.busy": "2026-05-28T17:00:29.152498Z", - "iopub.status.idle": "2026-05-28T17:00:29.408148Z", - "shell.execute_reply": "2026-05-28T17:00:29.407738Z" + "iopub.execute_input": "2026-06-01T17:59:37.409143Z", + "iopub.status.busy": "2026-06-01T17:59:37.409054Z", + "iopub.status.idle": "2026-06-01T17:59:37.777264Z", + "shell.execute_reply": "2026-06-01T17:59:37.776876Z" } }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 461, - "width": 761 + "height": 923, + "width": 1523 } }, "output_type": "display_data" @@ -1527,10 +1427,10 @@ "id": "645d5c5e", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:29.409530Z", - "iopub.status.busy": "2026-05-28T17:00:29.409426Z", - "iopub.status.idle": "2026-05-28T17:00:29.413573Z", - "shell.execute_reply": "2026-05-28T17:00:29.413075Z" + "iopub.execute_input": "2026-06-01T17:59:37.779232Z", + "iopub.status.busy": "2026-06-01T17:59:37.779104Z", + "iopub.status.idle": "2026-06-01T17:59:37.783309Z", + "shell.execute_reply": "2026-06-01T17:59:37.782922Z" } }, "outputs": [ @@ -1561,11 +1461,11 @@ " \n", " \n", " $\\theta_{\\text{new}}$ (true effect)\n", - " 0.935\n", + " 0.941\n", " \n", " \n", " $\\hat d_{\\text{new}}$ (observed estimate)\n", - " 0.886\n", + " 0.884\n", " \n", " \n", "\n", @@ -1573,8 +1473,8 @@ ], "text/plain": [ " posterior probability > 0\n", - "$\\theta_{\\text{new}}$ (true effect) 0.935\n", - "$\\hat d_{\\text{new}}$ (observed estimate) 0.886" + "$\\theta_{\\text{new}}$ (true effect) 0.941\n", + "$\\hat d_{\\text{new}}$ (observed estimate) 0.884" ] }, "execution_count": 19, @@ -1600,13 +1500,11 @@ "\n", "## Replication as the data-generating process\n", "\n", - "A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation rather than as eight unrelated noises, and the posterior on the population is not a number that \"summarises the experiments\". It is the population our experiments were drawn from, made audible by being listened to as a chorus.\n", + "A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population is not a summary. It is the population our experiments were drawn from, made audible.\n", "\n", - "This is the deeper reframing that the meta-analytic posture earns. Replication is often described as a verification protocol: a check we run on a result, after the fact, to see if it survives. The Bayesian version is more substantive than that. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The posterior on $\\mu$ and on $\\tau$ are the two halves of what we have inferred about the unobserved-but-real distribution of effects across the worlds the team operates in.\n", + "Replication is often described as a verification protocol: a post-hoc check. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The triptych closes here, though \"closes\" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three questions, one machinery. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior — something to argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The posterior is the constant; the question changes.\n", "\n", - "The triptych closes here, though \"closes\" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three questions, one machinery, and in each the same discipline: the quantity a conventional analysis leaves implicit, whether the believed effect, the unmeasured confounder, or the between-study variance, is made an explicit parameter with a posterior, and so becomes something a team can argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The posterior is the constant; the question changes.\n", - "\n", - "And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\\theta_{\\text{new}} \\sim \\mathcal{N}(\\mu, \\tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it.\n", + "And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\\theta_{\\text{new}} \\sim \\mathcal{N}(\\mu, \\tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it — each notebook's synthesis becoming the next notebook's prior, each conversation overheard becoming the next conversation's opening question.\n", "\n", "## Authors\n", "\n", @@ -1627,10 +1525,10 @@ "id": "2e2e3295", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T17:00:29.414902Z", - "iopub.status.busy": "2026-05-28T17:00:29.414815Z", - "iopub.status.idle": "2026-05-28T17:00:29.430081Z", - "shell.execute_reply": "2026-05-28T17:00:29.429589Z" + "iopub.execute_input": "2026-06-01T17:59:37.784599Z", + "iopub.status.busy": "2026-06-01T17:59:37.784526Z", + "iopub.status.idle": "2026-06-01T17:59:37.795615Z", + "shell.execute_reply": "2026-06-01T17:59:37.795126Z" } }, "outputs": [ @@ -1638,24 +1536,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Thu May 28 2026\n", + "Last updated: Mon, 01 Jun 2026\n", "\n", "Python implementation: CPython\n", - "Python version : 3.12.12\n", - "IPython version : 9.8.0\n", + "Python version : 3.13.13\n", + "IPython version : 9.14.0\n", "\n", - "pytensor: 2.38.2\n", - "xarray : 2025.12.0\n", + "pytensor: 3.0.3\n", + "xarray : 2026.4.0\n", "\n", - "pymc : 5.28.5\n", - "matplotlib: 3.10.8\n", + "arviz : 1.1.0\n", + "matplotlib: 3.10.9\n", "numpy : 2.3.5\n", - "arviz : 0.23.0\n", - "scipy : 1.16.3\n", - "seaborn : 0.13.2\n", "pandas : 2.3.3\n", + "pymc : 6.0.1\n", + "scipy : 1.17.1\n", + "seaborn : 0.13.2\n", "\n", - "Watermark: 2.5.0\n", + "Watermark: 2.6.0\n", "\n" ] } @@ -1680,7 +1578,7 @@ "default_lexer": "ipython3" }, "kernelspec": { - "display_name": "pymc-marketing-dev", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1694,7 +1592,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.13.13" } }, "nbformat": 4, diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md index 239bdb756..43f2d40a6 100644 --- a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md +++ b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md @@ -6,7 +6,7 @@ jupytext: format_name: myst format_version: 0.13 kernelspec: - display_name: pymc-marketing-dev + display_name: Python 3 language: python name: python3 --- @@ -22,7 +22,9 @@ kernelspec: ## The replication-as-evidence problem -Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question — "did it work?" — and gets, depending on which colleague answers, two incompatible defaults: vote-counting ("four out of eight worked, so it's a wash"), or pool-everything ("the combined estimate is positive, so it works"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that lets the markets be different, and lets that difference be part of what we are estimating. +Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question — "did it work?" — and gets, depending on which colleague answers, two incompatible defaults: vote-counting ("four out of eight worked, so it's a wash"), or pool-everything ("the combined estimate is positive, so it works"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that estimates between-market differences rather than assuming them away. + +Each experiment speaks about one market. The hierarchy is what lets us hear all of them at once. This notebook builds that model. The hierarchical Bayesian meta-analysis treats each experiment as a noisy estimate of its own market's true effect, and treats the per-market effects as draws from a population whose mean and variance are themselves the quantities of substantive interest {cite:p}`borenstein2009meta`, {cite:p}`higgins2009meta`. The structure is the one Rubin used for the 8-schools problem in 1981 {cite:p}`rubin1981estimation`, {cite:p}`gelman2013bayesian`, transposed to product experimentation. We develop it on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the third of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`sensitivity_confounding` for the interpretation counterpart. Readers wanting a deeper view of the partial-pooling vocabulary should also consult the existing PyMC notebooks on {ref}`multilevel_modeling` and {ref}`hierarchical_partial_pooling`, which we treat as predecessors rather than re-derive. @@ -50,7 +52,7 @@ warnings.filterwarnings("ignore", category=UserWarning) ```{code-cell} ipython3 %config InlineBackend.figure_format = 'retina' -az.style.use("arviz-darkgrid") +az.style.use("arviz-variat") rng = np.random.default_rng(11) RANDOM_SEED = 11 ``` @@ -162,7 +164,7 @@ anova_tbl = anova_two_way(study_df, "revenue", "segment", "treatment") anova_tbl.round(3) ``` -The interaction $F$-test reports whether heterogeneity is detectable; it does not estimate how large it is. The meta-analytic siblings of the same variance decomposition do, and because they recur through the rest of the notebook it is worth stating plainly what each one measures. +The interaction $F$-test reports whether heterogeneity is detectable; it does not estimate how large it is. :::{admonition} Three numbers for heterogeneity :class: note @@ -235,8 +237,8 @@ comparison.round(3) ``` ```{code-cell} ipython3 -fig, ax = plt.subplots(figsize=(6.5, 3.8)) -az.plot_posterior(idata_seg, var_names=["tau"], ax=ax) +az.plot_dist(idata_seg, var_names=["tau"]) +ax = plt.gcf().axes[0] ax.axvline( np.sqrt(tau2_DL), color="C1", @@ -244,10 +246,10 @@ ax.axvline( label=f"DerSimonian–Laird τ = {np.sqrt(tau2_DL):.2f}", ) ax.set_title(r"Posterior of between-segment SD $\tau$ (one study, six segments)") -ax.legend(); +ax.legend() ``` -Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor throughout was the segment, and that is the only thing about to change. Replace "segment" with "study" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution. +Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor was the segment. Replace it with "study" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution. The substitution is mechanical; what it opens is not. Once studies replace segments, τ becomes the quantity the replication programme was designed to estimate. ## The hierarchical re-framing @@ -257,7 +259,7 @@ $$ \theta_k \sim \mathcal{N}(\mu, \tau), \qquad \hat d_k \mid \theta_k \sim \mathcal{N}(\theta_k, s_k), $$ -where $\mu$ is the population mean effect across markets and $\tau$ is the between-market standard deviation. The substantive story lives in both: $\mu$ tells the team what to expect *on average* from the redesign; $\tau$ tells them how variable that expectation is when the next market is queried. Neither quantity is recoverable from any single experiment. Both are recoverable from the joint. +where $\mu$ is the population mean effect across markets and $\tau$ is the between-market standard deviation. $\mu$ tells the team what to expect on average; $\tau$ tells them how variable that expectation is across markets. Neither quantity is recoverable from any single experiment. Both are recoverable from the joint. ```{code-cell} ipython3 K = 8 @@ -299,7 +301,7 @@ markets_df = pd.DataFrame( markets_df.round(3) ``` -The per-market `z_score` column is what a frequentist replication exercise would consult: anything above 1.96 in absolute value counts as "significant", anything below does not. The columns disagree about how many markets "worked"; the underlying true effects disagree less. This is the gap the hierarchical model is built to close. +The per-market `z_score` column is what a frequentist replication exercise would consult: anything above 1.96 in absolute value counts as "significant", anything below does not. The columns disagree about how many markets "worked"; the underlying true effects disagree less. This is the gap the hierarchical model closes. The quantity no single experiment can recover is τ. ## No pooling, complete pooling, partial pooling @@ -346,8 +348,8 @@ no_pool_se = se_obs complete_pool_mean = idata_complete.posterior["mu"].mean().item() complete_pool_se = idata_complete.posterior["mu"].std().item() partial_pool_summary = az.summary(idata_partial, var_names=["theta"], kind="stats") -partial_pool_mean = partial_pool_summary["mean"].values -partial_pool_sd = partial_pool_summary["sd"].values +partial_pool_mean = partial_pool_summary["mean"].values.astype(float) +partial_pool_sd = partial_pool_summary["sd"].values.astype(float) forest = pd.DataFrame( { @@ -432,12 +434,15 @@ ax.legend(loc="lower right"); The hierarchical model returns two population-level quantities, and the conventional reporting habit of leading with $\mu$ obscures the more important one. The posterior of $\tau$ — the between-market standard deviation of true effects — is what tells the team how transportable any single result is to a new context. A small $\tau$ means the markets are nearly exchangeable, and the experiment generalises cleanly; a large $\tau$ means the markets are heterogeneous, and the next market is a meaningfully new experiment. Reporting only $\mu$ collapses this into a point and hides the variability that the next stakeholder will live with. ```{code-cell} ipython3 -fig, axes = plt.subplots(1, 2, figsize=(11, 3.8)) -az.plot_posterior(idata_partial, var_names=["mu"], ref_val=TRUE_MU, ax=axes[0]) +az.plot_dist(idata_partial, var_names=["mu", "tau"]) +axes = plt.gcf().axes +axes[0].axvline(TRUE_MU, color="C2", linestyle="--", label=f"true = {TRUE_MU:.2f}") +axes[0].legend() axes[0].set_title(r"Population mean $\mu$") -az.plot_posterior(idata_partial, var_names=["tau"], ref_val=TRUE_TAU, ax=axes[1]) +axes[1].axvline(TRUE_TAU, color="C2", linestyle="--", label=f"true = {TRUE_TAU:.2f}") +axes[1].legend() axes[1].set_title(r"Between-market SD $\tau$") -plt.tight_layout(); +plt.tight_layout() ``` The posterior of $\tau$ is concentrated well away from zero, which is itself the result of substantive interest: the eight markets disagree about the size of the treatment effect in a way the data demand be respected. The next section turns this into a prediction. @@ -497,15 +502,18 @@ with partial_bern_model: ``` ```{code-cell} ipython3 -fig, axes = plt.subplots(1, 2, figsize=(11, 3.8)) -az.plot_posterior(idata_partial_bern, var_names=["mu"], ref_val=TRUE_MU_LOGIT, ax=axes[0]) +az.plot_dist(idata_partial_bern, var_names=["mu", "tau"]) +axes = plt.gcf().axes +axes[0].axvline(TRUE_MU_LOGIT, color="C2", linestyle="--", label=f"true = {TRUE_MU_LOGIT:.2f}") +axes[0].legend() axes[0].set_title(r"Population log-odds effect $\mu$") -az.plot_posterior(idata_partial_bern, var_names=["tau"], ref_val=TRUE_TAU_LOGIT, ax=axes[1]) +axes[1].axvline(TRUE_TAU_LOGIT, color="C2", linestyle="--", label=f"true = {TRUE_TAU_LOGIT:.2f}") +axes[1].legend() axes[1].set_title(r"Between-market SD $\tau$ (log-odds)") -plt.tight_layout(); +plt.tight_layout() ``` -The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. "Meta-analysis" is not a binary-outcome technique borrowed from medicine. It is the same Bayesian operation regardless of likelihood. +The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. The log-odds parameterisation carries a structural advantage the probability scale does not: a given value of τ means the same degree of between-market variability in the treatment effect regardless of what the baseline conversion rate happens to be. On the probability scale, a τ of 0.05 is meaningful heterogeneity at a 5\% baseline and negligible noise at a 50\% baseline; on the logit, τ is scale-invariant in the way the analysis needs it to be. ## Predicting the next experiment @@ -572,13 +580,11 @@ The probability the true effect is positive in the next market is higher than th ## Replication as the data-generating process -A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation rather than as eight unrelated noises, and the posterior on the population is not a number that "summarises the experiments". It is the population our experiments were drawn from, made audible by being listened to as a chorus. - -This is the deeper reframing that the meta-analytic posture earns. Replication is often described as a verification protocol: a check we run on a result, after the fact, to see if it survives. The Bayesian version is more substantive than that. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The posterior on $\mu$ and on $\tau$ are the two halves of what we have inferred about the unobserved-but-real distribution of effects across the worlds the team operates in. +A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population is not a summary. It is the population our experiments were drawn from, made audible. -The triptych closes here, though "closes" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three questions, one machinery, and in each the same discipline: the quantity a conventional analysis leaves implicit, whether the believed effect, the unmeasured confounder, or the between-study variance, is made an explicit parameter with a posterior, and so becomes something a team can argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The posterior is the constant; the question changes. +Replication is often described as a verification protocol: a post-hoc check. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The triptych closes here, though "closes" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three questions, one machinery. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior — something to argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The posterior is the constant; the question changes. -And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\theta_{\text{new}} \sim \mathcal{N}(\mu, \tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it. +And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\theta_{\text{new}} \sim \mathcal{N}(\mu, \tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it — each notebook's synthesis becoming the next notebook's prior, each conversation overheard becoming the next conversation's opening question. ## Authors diff --git a/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb b/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb index a4308af90..4fb00cbe1 100644 --- a/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb +++ b/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb @@ -33,10 +33,10 @@ "id": "3abbded2", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:40.349921Z", - "iopub.status.busy": "2026-05-28T16:43:40.349744Z", - "iopub.status.idle": "2026-05-28T16:43:43.735989Z", - "shell.execute_reply": "2026-05-28T16:43:43.735368Z" + "iopub.execute_input": "2026-06-01T17:15:59.450621Z", + "iopub.status.busy": "2026-06-01T17:15:59.450509Z", + "iopub.status.idle": "2026-06-01T17:16:02.393024Z", + "shell.execute_reply": "2026-06-01T17:16:02.392147Z" } }, "outputs": [], @@ -63,16 +63,16 @@ "id": "f669edc2", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:43.737679Z", - "iopub.status.busy": "2026-05-28T16:43:43.737482Z", - "iopub.status.idle": "2026-05-28T16:43:43.741538Z", - "shell.execute_reply": "2026-05-28T16:43:43.740953Z" + "iopub.execute_input": "2026-06-01T17:16:02.394961Z", + "iopub.status.busy": "2026-06-01T17:16:02.394693Z", + "iopub.status.idle": "2026-06-01T17:16:02.734556Z", + "shell.execute_reply": "2026-06-01T17:16:02.733954Z" } }, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina'\n", - "az.style.use(\"arviz-darkgrid\")\n", + "az.style.use(\"arviz-variat\")\n", "rng = np.random.default_rng(7)\n", "RANDOM_SEED = 7" ] @@ -84,7 +84,7 @@ "source": [ "## What the data can and cannot say\n", "\n", - "The structural picture is small and the notation traditional. Let $T$ be the observed treatment indicator (which flow the visitor actually saw), let $Y$ be the outcome (revenue), and let $U$ be an unmeasured user characteristic: connection quality, engagement disposition, whatever drives the differential compliance. Under randomisation $U$ is independent of $T$ by construction; under compliance failure $U$ is associated with $T$ in a way the analyst cannot directly observe." + "The structural picture is small and the notation traditional. Let $T$ be the observed treatment indicator (which flow the visitor actually saw), let $Y$ be the outcome (revenue), and let $U$ be an unmeasured user characteristic: connection quality, engagement disposition, whatever drives the differential compliance. Under randomisation $U$ is independent of $T$ by construction; under compliance failure $U$ is associated with $T$ and unobservable." ] }, { @@ -93,10 +93,10 @@ "id": "dc1a9456", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:43.742885Z", - "iopub.status.busy": "2026-05-28T16:43:43.742784Z", - "iopub.status.idle": "2026-05-28T16:43:44.463213Z", - "shell.execute_reply": "2026-05-28T16:43:44.462579Z" + "iopub.execute_input": "2026-06-01T17:16:02.736974Z", + "iopub.status.busy": "2026-06-01T17:16:02.736741Z", + "iopub.status.idle": "2026-06-01T17:16:03.716153Z", + "shell.execute_reply": "2026-06-01T17:16:03.715518Z" }, "tags": [ "hide-input" @@ -105,15 +105,15 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 361, - "width": 611 + "height": 723, + "width": 1223 } }, "output_type": "display_data" @@ -160,13 +160,13 @@ "\n", "## The bias parameter\n", "\n", - "The Bayesian sensitivity analysis introduces $\\beta$ as a model parameter with an explicit prior. The data inform the sum $\\tau + \\beta$ through the observed difference; the prior on $\\beta$ controls how much of that sum is attributed to the unmeasured confounder rather than to the treatment. Three commitments are useful to compare:\n", + "The Bayesian sensitivity analysis makes $\\beta$ a model parameter with a prior. The data inform the sum $\\tau + \\beta$ through the observed difference; the prior on $\\beta$ controls how much of that sum is attributed to the unmeasured confounder rather than to the treatment. Three prior commitments:\n", "\n", "- **Dismissive prior** ($\\beta \\sim \\mathcal{N}(0, 0.05)$): \"I am confident there is essentially no confounding.\"\n", "- **Moderate prior** ($\\beta \\sim \\mathcal{N}(0, 0.3)$): \"I will allow that confounding may have shifted the apparent effect by up to a few tenths of a revenue unit.\"\n", "- **Sceptical prior** ($\\beta \\sim \\mathcal{N}(0, 0.7)$): \"I will not commit to confounding being small; the data must speak loudly to be heard.\"\n", "\n", - "Each commitment is a separable, auditable claim. The plot we are about to build shows how the posterior on $\\tau$ moves as the analyst slides this commitment." + "Each is an auditable commitment." ] }, { @@ -175,10 +175,10 @@ "id": "9d37c3cd", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:44.464947Z", - "iopub.status.busy": "2026-05-28T16:43:44.464802Z", - "iopub.status.idle": "2026-05-28T16:43:44.468035Z", - "shell.execute_reply": "2026-05-28T16:43:44.467575Z" + "iopub.execute_input": "2026-06-01T17:16:03.718053Z", + "iopub.status.busy": "2026-06-01T17:16:03.717835Z", + "iopub.status.idle": "2026-06-01T17:16:03.721488Z", + "shell.execute_reply": "2026-06-01T17:16:03.720995Z" } }, "outputs": [ @@ -217,7 +217,7 @@ "id": "ba7bfe6c", "metadata": {}, "source": [ - "The observed difference recovers the *biased* effect, as it must. The treatment effect $\\tau$ is what we want; the bias $\\beta$ is the channel we cannot observe. We now build a PyMC model that asks the data which decomposition is most plausible *given* a prior on the bias." + "The observed difference recovers the *biased* effect, as it must. $\\tau$ is what we want; $\\beta$ is what we cannot observe. The model below asks which decomposition the data find most plausible given a prior on $\\beta$." ] }, { @@ -226,10 +226,10 @@ "id": "7d543aca", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:44.469277Z", - "iopub.status.busy": "2026-05-28T16:43:44.469185Z", - "iopub.status.idle": "2026-05-28T16:43:46.724450Z", - "shell.execute_reply": "2026-05-28T16:43:46.723745Z" + "iopub.execute_input": "2026-06-01T17:16:03.722613Z", + "iopub.status.busy": "2026-06-01T17:16:03.722538Z", + "iopub.status.idle": "2026-06-01T17:16:06.091447Z", + "shell.execute_reply": "2026-06-01T17:16:06.090878Z" } }, "outputs": [ @@ -237,48 +237,30 @@ "name": "stderr", "output_type": "stream", "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" + "NUTS[nutpie]: [tau, beta]\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [tau, beta]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 2_000 tune and 2_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" - ] + "data": { + "text/plain": [ + "Text(0.5, 1.05, 'Posterior of $\\\\tau$ and $\\\\beta$ under a moderate bias prior')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 383, - "width": 811 + "height": 594, + "width": 2423 } }, "output_type": "display_data" @@ -304,10 +286,13 @@ " progressbar=False,\n", " )\n", "\n", - "axes = az.plot_posterior(\n", - " idata_moderate, var_names=[\"tau\", \"beta\"], ref_val=[TRUE_TAU, TRUE_BIAS], figsize=(8, 3.5)\n", - ")\n", - "plt.suptitle(r\"Posterior of $\\tau$ and $\\beta$ under a moderate bias prior\", y=1.05);" + "az.plot_dist(idata_moderate, var_names=[\"tau\", \"beta\"])\n", + "axes = plt.gcf().axes\n", + "axes[0].axvline(TRUE_TAU, color=\"C2\", linestyle=\"--\", label=f\"true τ = {TRUE_TAU:.2f}\")\n", + "axes[0].legend()\n", + "axes[1].axvline(TRUE_BIAS, color=\"C2\", linestyle=\"--\", label=f\"true β = {TRUE_BIAS:.2f}\")\n", + "axes[1].legend()\n", + "plt.suptitle(r\"Posterior of $\\tau$ and $\\beta$ under a moderate bias prior\", y=1.05)" ] }, { @@ -315,7 +300,7 @@ "id": "74929da4", "metadata": {}, "source": [ - "The posterior of $\\tau$ is centred well above zero but with substantial uncertainty; the posterior of $\\beta$ is also non-zero, reflecting the prior pull. The data have informed the sum but the decomposition is shaped by the prior on $\\beta$. The next move is to vary that prior and see what the data tolerate.\n", + "The posterior of $\\tau$ is centred above zero; the posterior of $\\beta$ is non-zero, pulled by the prior. The data informed the sum; the prior shapes the decomposition.\n", "\n", "### Tipping-point analysis under a Gaussian outcome\n", "\n", @@ -328,10 +313,10 @@ "id": "2f6ee70f", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:46.725894Z", - "iopub.status.busy": "2026-05-28T16:43:46.725793Z", - "iopub.status.idle": "2026-05-28T16:43:46.741266Z", - "shell.execute_reply": "2026-05-28T16:43:46.740704Z" + "iopub.execute_input": "2026-06-01T17:16:06.093146Z", + "iopub.status.busy": "2026-06-01T17:16:06.093025Z", + "iopub.status.idle": "2026-06-01T17:16:06.116375Z", + "shell.execute_reply": "2026-06-01T17:16:06.115801Z" } }, "outputs": [ @@ -370,15 +355,15 @@ " \n", " \n", " tau\n", - " 0.740\n", - " 0.304\n", + " 0.72\n", + " 0.3\n", " 0.722\n", " 0.299\n", " \n", " \n", " beta\n", - " 0.048\n", - " 0.292\n", + " 0.065\n", + " 0.29\n", " 0.065\n", " 0.287\n", " \n", @@ -387,10 +372,10 @@ "" ], "text/plain": [ - " MCMC Analytical \n", - " mean sd mean sd\n", - "tau 0.740 0.304 0.722 0.299\n", - "beta 0.048 0.292 0.065 0.287" + " MCMC Analytical \n", + " mean sd mean sd\n", + "tau 0.72 0.3 0.722 0.299\n", + "beta 0.065 0.29 0.065 0.287" ] }, "execution_count": 6, @@ -440,24 +425,24 @@ "id": "e9d91b3f", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:46.742773Z", - "iopub.status.busy": "2026-05-28T16:43:46.742660Z", - "iopub.status.idle": "2026-05-28T16:43:46.998802Z", - "shell.execute_reply": "2026-05-28T16:43:46.998294Z" + "iopub.execute_input": "2026-06-01T17:16:06.117824Z", + "iopub.status.busy": "2026-06-01T17:16:06.117649Z", + "iopub.status.idle": "2026-06-01T17:16:06.637906Z", + "shell.execute_reply": "2026-06-01T17:16:06.637365Z" } }, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": { "image/png": { - "height": 411, - "width": 1111 + "height": 823, + "width": 2223 } }, "output_type": "display_data" @@ -528,90 +513,17 @@ }, { "cell_type": "markdown", - "id": "c223dfcb", "metadata": {}, "source": [ - "Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number.\n", + "Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The question has changed from what the treatment did to which prior commitments on the unmeasured bias are required to believe that it did anything; the machinery has not.\n", "\n", - "### The sensitivity surface\n", - "\n", - "The previous sweep moved one dimension of the bias prior. The bias prior has two, the mean and the standard deviation, and an analyst defending the conclusion ought to be able to point at the region of $(\\mu_\\beta, \\sigma_\\beta)$ where the decision still goes through. The two-dimensional sweep below maps it." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c46490fc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-28T16:43:47.000450Z", - "iopub.status.busy": "2026-05-28T16:43:47.000329Z", - "iopub.status.idle": "2026-05-28T16:43:47.251663Z", - "shell.execute_reply": "2026-05-28T16:43:47.251221Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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6JbKW0utnjIjFixeb56x+jrj88svNNtLnnwZ/9Pfr81+DYrp+mmF311131QkK6ZdT2j86/rNaNq1rQzT4FHluawlv7Oci/T/9Ar0hWgHS2reRle/hmfg8lO792dre5wBkN0dYX7WBHKK9ufSDdywt99M340ToG/Ohhx5a50O6ngDqCV78CY0G5yK0hEpPBtJNP/BdcMEFKfUcTJWe6OgHLD3xbax/ldIPnYcccohZIh8IM0mDVbEZnFreox/6kqFBLs0yiaWZofGBy8a+ndYPerFl39oAPhMDk+zQg1Lp36Yl1ToxvSF6sqHbNfaDsX641tLBxmimRuyHYs1g0szeXXbZpcH7aEbC1VdfbU5U4mnJYmPtFzSD6Y477ohe15Mufc1o7G9TGhDTjI3YbDYNIGlWVGPq2556oqYZI031l0qHhr5M0cCIZqHWl2GiNIih+yb279U+Y/qFTmOBm+b0I9RgiQaaE+3rqyedWoIW/2WOPo8bakWhwwF233336DGj+12fSzo8IRF6fz0G9txzT8mWfZWuY7M5+1yfN7HZ5/p79Tmtf2tjrzHasy62z5zub/07G3v/sfo5qO1SNKATSz9n6PtXIkFQDVhoUKexzzVWb08Ngujfo+1gGqJfKOlrd4T+7VtvvbUJwOqXE1rm29BgEX1f0b8l9jPJXnvt1WQpamta1+Y8l+L7Fzb1/pcN26gl38Nb+vNQqvuzNbzPAcgtlHgj59QXQEs0g1I/pGigMTY4qR/q9dvjePElU/rhKxPiy9UzOSSjIXpioictGjTQQKV+oGvo79WSdf0Qq1lP+uFL79NYz6FUaIZQfHl57DfBiaqvhC+ZdY4/WdBv5Bvr05QNtAl+Ux/+9Zv5+KB9UyXYGtiN/8b+pptuajQ4qTSzUvsrJUuzi7QENtbtt9/e5N+mNCNN7xsbiNZsh/qa/zdFh7u0RGCkIfp81eBFQwEvpSeWOngg9jYayNCBCemmXxIlM3RMM2x1/2sz/wjNAtcsmcbeK2ID2vvuu2/CJ22qS5culpy0tfS+aoljU7N14ltj6GtHY8G0yGuMZiLFPgd1v+vz0K7PQf2cof354oOTGrBMNENTM6gaC07aYXtqsLexYJbSapDYqeQ6pEiDWRoM0y+lGpt6rNmt8WXA2pczNnM4G9fVKnbdRnZ4D8/U56F0aK3vcwCyFwFK5Jz6Jng3VX6tb95aNqXDFeIHpJxyyin1li3HB6ASzbZLNeDa0iXU8bbaaisTsNVv8Z977jk5+uijTR+h+jKaNMNDS3u0REfLb/SEKZ39KuubXtucHjr1BSirq6sTvr/+zvghStkcoNRjQLOzEqHPqdgP/9rjqbEhFC+99FKd65ohmmhGrE7+TLYHqZYGxwajNWPjwAMPTPj+Q4cOrbN+mm2gZVHJ0My22Czplqb7RzNfYnt6NnZCp+V9sbRMLpF+jy1BS57jM44bEl/6msyU6VzZVy11bMZngWsATr8IS4ROKY8PvL366qtJvYa35HNQAyCRslKl758aUElnxr3V21M/cyVStaLZmfUFP/S+ibTN0feH2LYu+uVyZKpyNq6rVey8jax+D8/k56F0aI3vcwCyGwFK5Jz4gJ5+K/v222+bDzGxiwbXtHREA2j6gUozJ+MbSGvZrpZ7xNOTi0gfsNgTnGzNoKyPniDr5EPtoamBR/0WWr9dry9AqCc2ug+0bEb7X+nJWFPTDhMRe5IX0dDgjMbUt87JZjbE73/9kJutjjrqqIQCJEozgmIneTY1hCj+xCC+9L6p/ajrlozYNg2Rcqpkxff1iy3HSoT26NK2AFbRTMVkArvx+0SD8Trh1Q40Qye2pLuxfrCxmT5KJ7raXUvvq5Y6NjVTKpZ+8ZXMl036vI19TdKT8vqG3dnhOahfhsZnf6f784PV2zOZ6d/aSzWV+2tANVb8l8zZtK5WsfM2svo9PJOfh9KhNb7PAchuDMlBzokP6Ok3q9qLJhn6DadOgdWlvg8e9ZX/ZmLSp5bkxQa69ASqsRIZq+iJz957720WbQKuZSvar1InI8Zn62h5mw5w0EXLXTQw3Fz1nVA2NA21MfWV8yR7shq//zNV1m4HmuWQDA2oxE6rb2hC84IFC0xJWISWpyY77EmzJ7TZfqL9amODNbrPk/3bIplqsZI5kVfJ9gdLN91mydCMkT59+pihArElpc0ZFJbs81RP5lasWGGeX5pBXV9GdmyGin7ppK9B9U2K1RNpfX2PlL/pQIZ77rlHzj33XNtOM23pfdUSx6bu09jnvdpnn32Sfo3R6oLYQUnaLmLnnXe21XNQM6Z0sFMs7decbdtTh4Qkqlu3bpsFcfS4be79G3p/yYZ1tYpdt5Ed3sMz9XkoXVrj+xyA7EaAEjlFM/XipzonQ09sNcimpaLxH1iaytzLxBt9fLBVe2lmYvBKOukU0si0ZT0Z08xJDVbGfiCLiJ1o2Bz1TdluTv+/+rIlE53g3VCAsjX1lkpWsv1WI5NwY3ua1Sc+q1YzDZLtKaoTMXXf1Vf+H08DNbG9mfTYbU6PvvqC8MlItEdupjSnbYTeJzbolY6M6PpooEX72OqQDg2WBAKBpO6v+1dfZ+prQ9GhQwfTjys2o037pOkxoBk1mu2tk2rjj99c2lctcWzGl3BqQCPZVg2RzKvYgFoy5bMt9RyMz17SbF8dIpJt2zO2TLcp8c8vDVAlmpFW33t1sl8OtqZ1tYpdt5Ed3sMz9XkoXVrj+xyA7GbvSAaQZnrilWh/LX0z1pNW7R2oJ3DDhg0zjeoTmTxdX5Awnb0V7dp/MlkaXNLykvqCA+mgQUHNsovd9sn0yWosmJjsh7X4wIndA8mp0JOAZMQP82joORqf9ZPMSVGEZsrpCVEipXPxvZ/0erLZ1vWJb//QlEw9PxLVnO0cf5/4fZcO2vfuzjvvTPmx9QS3oW2sw2Y0aBTbM1Yz17WHny76PNbglZ7AaaaMZpDFl8zVR7+USTT4oK81iWbRtfS+aoljM/750tyBc/G91ZJ5HrbUc1AzvuLXub7s3ta+PZN5j4j/+2NbNKTz/SUb1tUqdt1GdngPz9TnoXTK1PscADRH9p4hAwlkHOoHJS0zTvc3g/VldWUiY86u/Scbo9mlEydONCfo2gersZLrdOwX/XAYe8LdnIbj9X1bnuyHzvjAaLIZmK1JMtkQyYjPqE32xCbZfZdsIDFRyQbJrc5caM52jt/G6d6W2h/4P//5T1oeq7ETwM6dO8srr7wi1157rXz00Uf1fvGgr8O6PPvss6bHrWac6PA07cHbEC2hS3RQlgaQEg1QtvS+aoljM77Esbm/0+5/Z30BykwEAeywPVN5j8jU+0s2rKtV7LqN7PAe3hqOgUy9zwFAcxCgRE6JD+hp761MnHjoY+obeGzwLRNlGvEZlI2VnVtJAwA6LVeDkh9++GGj20KHAWhZyaGHHrpZM/Tm0JLe6dOnR68vX7486cfQUvRYXbp0Sfq4if+brc6KywbN/eAfW/LVmOb0K0Vy27g5vv/++82Ck9pCQ/voaa9A7Z+ome4aQNGeY/GDscaNG5dwcDDymnTfffeZDPzXXntNPv/88wZbhegx8/HHH5vloIMOkuuvv77ZgfRs2FeZkK4T/tYQOGgJbE9kK97DE5ft73MAWg8ClMgpLRXQ0w/8Wka6ePHi6M/iJ4CnSgNesX3DtCzE6l518bQ/lfaY1CmKjfXs0SCCBg00KKlTaNNZ/hwfoIwfQJCIpUuX1rkeP2UxkQBA/NTu5pbVtQS7lpXFZ+w0t0dpol8WxAeRtazpySeflFyj20v7VCV7n0wF5DX7MJYGJLVnVqLPqeb2dRs4cKBceeWVZtHns2bf62CQqVOnmveW+EBf5HXviSee2KxsL1f2VTrEZxE298u++NcLO5YoakuZWJkYkJFL2xO5jffw7HyfA5DdCFAiZ+g3frHT/DLds1GbzscGKHWybDrpt5yxHxQ0aJaJSeHJ0gCgfmDRbEmdFtpYEFdLQ/70pz/J/vvvn7FvX+OnQ/70009JP0b8xMZkJ6WvX79+s2/y4/t3pUt8f6fm9D616+RQHeYQqznZsBp8TfS5GB/oaU5wOxvodk426BW/b+L3XXNpXyw9WYrQEyLN+kg0OKmlaqkO34pkUWumty6R1hHvv/++OUmLzc7UzHHNRtGhYLE+/fRTyfZ9lS7x65NM9mtjXzTZLRBbX4BS17mhKfPNlUvbE7mN93Br3+cAoDnS23kbsDEdihE/wTmTAUr9FjL+96dTfLBVs4isogG45557To455hgz5VwznBoKTmpG49/+9jf55JNPTC8b/UCTydKQnXbaqc51/YAVm3maSK+h+Km2mkmXyr7SYEqmTubiS88TmVYd37PJrpND449xPcaS7ee4YMGChLdJfE9XPSFP9xcNrbE1RnPuk67Xp19//bVOhu/QoUOld+/eSa1XJgaWderUSU488UTz5cyQIUPq/J9+WZOL+ypd4isDtKdwc74siJ+Qbbe/U22//fabZTfqMZ9OubQ9kdt4D8/O9zkA2Y0AJXJGfSdumezZGN8/cc6cOWl9/PggW7JZfanSwJB+SDnrrLPMdPMbbrihTil1fFbICSecYJpwT5gwQc4555wWK3HWfazl9rHee++9hO+vPTNjsx+1HH3XXXdNah3i9338SWgmy6CTzY7R/n52pcHt2MCuBpo+++yzpB5DA+PJZEHrEkuP31yTzDZTc+fO3ez1Kf5kJpUvQ1KZWq0DujJJh19dcMEFdX6W7gBTa9lX6aLVAfGZhdr7LBn65UL8ZwCdSGs32js1PuCe7hP/XNqeyJ5J4c2RC+/hVuxPq9/nAGQ3ApTIGfEfpvVDSyZ7Ju2www51ms9rkCqdDbvje1pqKUamaUDoyy+/lMsuu0x22WUXueSSS0yASMsm4+nQiv32289M29X7XH311Zad+GpWZ6xXX3213nWuz8svv1znuv7dyU7g1l6c8cdGpmjAJnaKvJZrx2dwNub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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 511, - "width": 660 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "bias_mu_grid = np.linspace(-0.5, 0.5, 25)\n", - "bias_sd_grid_2d = np.linspace(0.01, 1.0, 25)\n", - "prob_grid = np.zeros((len(bias_sd_grid_2d), len(bias_mu_grid)))\n", - "\n", - "for i, b_sd in enumerate(bias_sd_grid_2d):\n", - " for j, b_mu in enumerate(bias_mu_grid):\n", - " pm_a, pc_a = gaussian_sensitivity_posterior(\n", - " d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=b_sd, bias_prior_mean=b_mu\n", - " )\n", - " tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0])\n", - " prob_grid[i, j] = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd)\n", - "\n", - "fig, ax = plt.subplots(figsize=(6.5, 5))\n", - "im = ax.imshow(\n", - " prob_grid,\n", - " origin=\"lower\",\n", - " aspect=\"auto\",\n", - " extent=[bias_mu_grid.min(), bias_mu_grid.max(), bias_sd_grid_2d.min(), bias_sd_grid_2d.max()],\n", - " cmap=\"RdBu_r\",\n", - " vmin=0.0,\n", - " vmax=1.0,\n", - ")\n", - "contour = ax.contour(\n", - " bias_mu_grid,\n", - " bias_sd_grid_2d,\n", - " prob_grid,\n", - " levels=[0.5, decision_threshold],\n", - " colors=[\"white\", \"black\"],\n", - " linewidths=[1.2, 2.0],\n", - ")\n", - "ax.clabel(contour, fmt={0.5: \"P=0.5\", decision_threshold: f\"P={decision_threshold}\"})\n", - "ax.set_xlabel(r\"Prior mean of bias $\\beta$\")\n", - "ax.set_ylabel(r\"Prior SD of bias $\\beta$\")\n", - "ax.set_title(r\"$P(\\tau > 0)$ under bias-prior commitments\")\n", - "plt.colorbar(im, ax=ax, label=r\"$P(\\tau > 0 \\mid d_{\\text{obs}})$\");" + "The same machinery runs on the log-odds scale. Before mapping the full topology of the bias-prior space, we confirm that the one-dimensional fragility picture is not an artefact of the Gaussian likelihood." ] }, { "cell_type": "markdown", - "id": "49fd3d5f", "metadata": {}, "source": [ - "The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right; it tells both parties exactly what they are disagreeing about.\n", - "\n", "## The same machinery on a binary outcome\n", "\n", "The conversion-rate version of the experiment uses the same structural model on the log-odds scale. The bias parameter now lives on the logit, but the framing is identical: the observed log-odds difference is the sum of a treatment effect and an unmeasured-confounder contribution, and a prior over the latter is the only thing that separates them." @@ -619,14 +531,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "97c262c4", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:47.253070Z", - "iopub.status.busy": "2026-05-28T16:43:47.252954Z", - "iopub.status.idle": "2026-05-28T16:43:47.256528Z", - "shell.execute_reply": "2026-05-28T16:43:47.255823Z" + "iopub.execute_input": "2026-06-01T17:16:07.490259Z", + "iopub.status.busy": "2026-06-01T17:16:07.490138Z", + "iopub.status.idle": "2026-06-01T17:16:07.493791Z", + "shell.execute_reply": "2026-06-01T17:16:07.493269Z" } }, "outputs": [ @@ -667,14 +579,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "e0f3171d", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:47.257811Z", - "iopub.status.busy": "2026-05-28T16:43:47.257686Z", - "iopub.status.idle": "2026-05-28T16:43:58.798096Z", - "shell.execute_reply": "2026-05-28T16:43:58.797584Z" + "iopub.execute_input": "2026-06-01T17:16:07.495232Z", + "iopub.status.busy": "2026-06-01T17:16:07.495143Z", + "iopub.status.idle": "2026-06-01T17:16:17.061666Z", + "shell.execute_reply": "2026-06-01T17:16:17.061128Z" } }, "outputs": [ @@ -682,175 +594,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [logit_p_A, tau, beta]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 1 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [logit_p_A, tau, beta]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 1 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [logit_p_A, tau, beta]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 2 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [logit_p_A, tau, beta]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 2 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Initializing NUTS using jitter+adapt_diag...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Multiprocess sampling (2 chains in 2 jobs)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NUTS: [logit_p_A, tau, beta]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 3 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" + "NUTS[nutpie]: [logit_p_A, tau, beta]\n", + "NUTS[nutpie]: [logit_p_A, tau, beta]\n", + "NUTS[nutpie]: [logit_p_A, tau, beta]\n", + "NUTS[nutpie]: [logit_p_A, tau, beta]\n", + "NUTS[nutpie]: [logit_p_A, tau, beta]\n" ] }, { @@ -884,37 +632,37 @@ " \n", " 0\n", " 0.05\n", - " 0.374\n", - " 0.067\n", + " 0.369\n", + " 0.070\n", " 1.000\n", " \n", " \n", " 1\n", " 0.15\n", - " 0.351\n", - " 0.154\n", - " 0.987\n", + " 0.358\n", + " 0.146\n", + " 0.998\n", " \n", " \n", " 2\n", " 0.30\n", - " 0.280\n", - " 0.254\n", - " 0.870\n", + " 0.273\n", + " 0.267\n", + " 0.835\n", " \n", " \n", " 3\n", " 0.50\n", - " 0.183\n", - " 0.338\n", - " 0.700\n", + " 0.191\n", + " 0.342\n", + " 0.716\n", " \n", " \n", " 4\n", " 0.80\n", " 0.110\n", - " 0.418\n", - " 0.610\n", + " 0.401\n", + " 0.600\n", " \n", " \n", "\n", @@ -922,14 +670,14 @@ ], "text/plain": [ " bias_sd tau_mean tau_sd prob_positive\n", - "0 0.05 0.374 0.067 1.000\n", - "1 0.15 0.351 0.154 0.987\n", - "2 0.30 0.280 0.254 0.870\n", - "3 0.50 0.183 0.338 0.700\n", - "4 0.80 0.110 0.418 0.610" + "0 0.05 0.369 0.070 1.000\n", + "1 0.15 0.358 0.146 0.998\n", + "2 0.30 0.273 0.267 0.835\n", + "3 0.50 0.191 0.342 0.716\n", + "4 0.80 0.110 0.401 0.600" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -978,28 +726,28 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "69190ddc", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:58.799637Z", - "iopub.status.busy": "2026-05-28T16:43:58.799522Z", - "iopub.status.idle": "2026-05-28T16:43:59.004406Z", - "shell.execute_reply": "2026-05-28T16:43:59.003909Z" + "iopub.execute_input": "2026-06-01T17:16:17.063086Z", + "iopub.status.busy": "2026-06-01T17:16:17.062988Z", + "iopub.status.idle": "2026-06-01T17:16:17.372536Z", + "shell.execute_reply": "2026-06-01T17:16:17.372017Z" } }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
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" ] }, "metadata": { "image/png": { - "height": 411, - "width": 1111 + "height": 823, + "width": 2223 } }, "output_type": "display_data" @@ -1041,18 +789,105 @@ }, { "cell_type": "markdown", - "id": "ac0cfdac", "metadata": {}, "source": [ - "The shape of the curve is the one we saw in the Gaussian case. The decision rule loses confidence in the positive treatment effect as the analyst becomes more willing to attribute the observed log-odds shift to an unmeasured confounder. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve here is the full posterior over the bias-prior commitment, and is the object the analyst should actually be reading.\n", + "The shape of the curve confirms what the Gaussian case showed: the decision rule loses confidence as the prior on bias widens, and the tipping point is identifiable on the log-odds scale just as it was on the revenue scale. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve is the full posterior over that commitment. One dimension, two likelihoods, the same fragility picture. The complete audit — both dimensions of the bias prior, the full region of commitments the conclusion can survive — is the surface below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The sensitivity surface\n", "\n", + "Both the Gaussian and the binary sweep moved one dimension of the bias prior. The complete audit requires the other: a map of the full region of $(\\mu_\\beta, \\sigma_\\beta)$ commitments the experiment can survive. The sweep below opens the line into a surface." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c46490fc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-01T17:16:06.639807Z", + "iopub.status.busy": "2026-06-01T17:16:06.639714Z", + "iopub.status.idle": "2026-06-01T17:16:07.488693Z", + "shell.execute_reply": "2026-06-01T17:16:07.487821Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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bdst1j1RM+/AnnnhCQ1lFRYVuuummlPvOOussHXTQQX2+JqA7tPPGgPLhhx/q2Wef1fLly7V69Wpt3brVS+JHIhEvrW/S+xMmTNDUqVN16KGHeun0srKWXw4AAAAAAAAMJCYkZ8KMxYGgFgTL9EB0uxrltrXhNrYmovpbdJteiddoTrBEU6yWKpImimeChu87DVoer9dKp6HtuX6ozw8LmjHKCuvknOHKbw0n9mfLaD8caCpj5slSU9ur/Sjo+KHbrLF2Tq/bjW9wmrUq3hKi7BjHNG3U/Sqfu+ODeJO2u7F26zVHKQ0ENcnO0fRgvjc+kxip12P1ejFaozVOU1uY0uqk3fdmN6p7mrd74+BgoebnlHrH6Y+W6wAAAAAAZKtt27bpb3/7W6f7TaXEm2++WaNGjdqj4x955JE6//zz9fOf/7zTx5h9ixYt0lB13XXXqbq6epftI0eO1IUXXqg//vGP/bIuoCuEKNHvotGoHnroIf3lL3/RihUrOn1cXV2dN7Zs2aIlS5borrvu8tL7Jsl/zjnn6KijjurTdQ90Jnh6yimnaN26dd0+9oc//KFOP/30PlkXAAAAAADZxA8zHhgsVG0irn/HdrZFCgOtw3y9zm3WumizV93QtKCOJFyv5fVH0bwWHZ9r9u1j5+tj4XIvQOm3/h4IJto5ygvYXjXG5Fbb5nad06y5oRIv5BjczfWayo0meFjvOnor3uBVo0wOpvrzmEqQZVbPL3+aYzqJhBfKNN8r/1h+cHVqME+j7bC33TwuHLA0N1zsjR1OVK/EavVKtFbV3nM/qkhpGn/7xzHVKs139M14vTem2nm6oGD8bp8DAAAAAACwZ0yFyVis5cOTqZx55pmaNWtWr07v1772Nf3rX//S2rVrU+5ftWqVV2BswYIFGmqefvppPfLIIyn3XXbZZV7xNGAgop03+pX5S+Gkk07SpZde2mWAsjOO43jH+MpXvqKzzz7bq16JFrfcckuPApQAAAAAAKBvHBUq1Vnh0RodCO/SJtpqHSYQaKo0bktE1Sin02MlWkODe9t5OjN3pBfw844zQMJ4JswZCljaL5jfbrv/et+K1avaje9ReNCv3Phea5XO5OMmX/AssmwvWNqTdtl++/MNTkQbnUhbpU9vX+v9fYJ5XnVLw25dg3meOf4IO6xP5g7XD4qn6FsF43RIqNB7jglM+s/3232b++HWo08L5hGgBAAAAIA+Zv4JyNizczAUinz94x//6HR/OBzWt771rV7PEwqF9O1vf7vLx9x5550aaurr63X11Ven3Ldw4UKdeOKJfb4moKcIUaJfmFT/Nddco69//eteC+90WLx4sT71qU/p3nvvVbZbuXKlbr/99v5eBgAAAAAAaOUH+UxFyuPDwzTNym+rcNgxrJd8wa5jONDfZypWHmIX6St5YzTMCg248+yHOWcEC7x21kZyXNK0+H44UtEWXtzd81jtxvRctEZVSRUjP9Iy0/7BghT7ul7v+06jKhIfVaPwz/c4K0cT7dxdQqrmaxPqNOvyX8v0YIG+mj9WPyqeqs/mjtQEO8dbg9NWkTLghWXNkQ4NFe3W6wcAAAAAAHvumWeeUW1tbaf7Tz75ZI0YMSItp/j444/XuHHjOt3/4osvqrKyUkOJaYO+devWXbbn5+fryiuv7Jc1AT1FiBJ9rqmpSeeee67++te/ZuRTA//7v/+rn/zkJ8pWpjqnKYEcj7f8EgEAAAAAAPQ/v3qiMd0u0OdyRunk0AiNDITbqkoayff9C3fJIUszplh5+q+cMTojPNJrl727QcS+ZEKU0+y8dlU3/de0LF6vxbHaHlWK9Ftom/NY58b1aLTSq9jptzVPPq6p9miCipOtnlXn9Oc37cFXx5u89uPe9qQ1m4qRw7sIq5p1+fOY74cZpgrm0TmluqRwki4rnKRjw2UqDNhtYcrpwXyNsVqqkgIAAAAAgMz797//3eX+M844I21z2batU089tdP9JtPx6KOPaqhYunSp7rrrrpT7zj///C4Dpehf27Zt80K/pnrqb3/7Wz333HPauXNn1n1bWvrPAH3EhBzPO+88r2pkJv3+97/3yixfcMEFyjZ//vOftXz58v5eBgAAAAAA6IIJP84NlXhjvdOkpfE6feA2Km7aQ0tqkOMFJoMKKK6EygMhjbbCOixYrKlWntcq29ebFt5+ADMTbcDNsc1xT8kZrtubtngVHgNJ4cSoXD0Y2an6hKMjwyUqCNhtoUY/dOofw2+h3ZRwdE9ku96NN7bN0zGcab6ebOcqP+kcdcWvBrrWadIWN9p2HH9fXsDy2qWbUGRPJJ9Ls37z1Vg7R2fkjfDG27F6PRGp0mGhogHTfh0AAAAAgGwoSGWqP3Zm5MiRmjVrVlrnPOmkk3Trrbd2ut+E1T7/+c9rKGSBrrjiipQflJ0+fbrOOeecflkXeu7DDz/Uhg0b9Nhjj7VtGz58uJ5//vmsOY2EKNGnrr/++i7/UvKZBLr5y+SQQw7R2LFjlZeXp7q6Oq1evVpLlizx0vj19fVdHuNXv/qVpk6d6h0nW5g/0H7xi1/09zIAAAAAAMBumGTnecNUWtzgNqs2EfdCkiZcaEKUJjxpAobFgd5fyvMDiiasGUyqnmiY+a0OVTN7wxzbzGfWb0KSD0V2traz/qh1uWnr/Vi0UhvdiI4MlWjfYH5be+zk6o7RhKs3WytXrnGa2oUc272+1tspdq5yexii9OdYFW9StRtPGcg07bz39Bx462oNx5qvDwwVeqOnFTgBAAAAAEDvvf32217upDPz5s1L2zUR39577+1lXjZv3pxyv8m/mIqUweDgjm/9+te/9vI8HVmWpeuuu27Qv75skki6XpVt1Sh5l6LPPPzww7r77ru7fExpaakuvvhinXbaad4fph0dcMABXrnjSy+91PtD+Pbbb5fr+k2udmWS7uY5kyZNUja48sorvXbpAAAAAABg8DGVFifbLS2oM8X/ZYCpuvhAZKfXTnpBuFTj7Vxv/o7VH9MxnzmSaWVtgpuPRCva2pV7+037KiX0VrzeGyOskPa3CzTaDqs5kfCqQG5yIl5wcpMb2SU02W6u1tBjriyNsXIU7kGI0g9rbneiWu80e2tJrpZp7BPMV6kVTMt5SK5Ome5fzAAAAAAAgM511zF17ty5GTl95rj33Xdfyn0NDQ1ep9GDDz5Yg9WqVau8brGpnHXWWZo5c2afrwm7Jzl3FUj6QHC26dnHsYFeqq2t1bXXXtvlY6ZMmaJ7771XZ5xxRsoAZbLCwkJddNFF3h/E+fn5nT7O/IVzzTXXKBuYv3RfeumlXbabczl//vx+WRMAAAAAANhz/sXKdF+0NGFG07bahBJfi9fp5sYNuqFhnZ6JVqkh4aS9xbS//uPCZTotZ4RKWytq+kFFM5ullpDhDjemZ2LV+lvzdq/V913N2/RcrNqrVPlRDHHXCpTJTOvskVa43dzded9p0vbWVt6GP9NwK+RVojSBTHMsv/15b5jzS4ASAAAAAPqPiQu5SjB2+xwMbu+8806X+zMV9jvwwAN7ta6BHr67/PLLFYvFUrZHv/DCC/tlXYPVnXfeqaVLl6qxsbFP503uBJzIwvCkj0qU6BOmxXRVVVWn+8eMGaM//elPGjVq1G4d15RTNm27v/rVr8pxnJSPMe3Dn3jiCS1atEhDVUVFhW666aZOk/1lZWV6/vnn+3xdAAAAAABgz/lBu3QF7vyqizWJuFY5LRdjQwp41RdNeNFUpjTji7mjNStUlLZvnb9+U+ny6HCpF6J8IVat1U6z90uY1s+3t7X6NhFGs91JqgrZcttyEddvB77L62u9nWDleBUtk+fuam2xhKv3441e+/Tk4xiT7FwVBWyv1bndoZqktxaqSQIAAAAAMCi8++67ne4rKCjQ5MmTMzKv6Z7alRUrVmiwuuOOO7Rs2bKU+y677DKvQBq6lhxavP7669uuV02cOFEzZsxoN0x330xIbtsdSLrWlW0fAiZEiYzbtm2b/va3v3W631RKvPnmm3c7QOk78sgjdf755+vnP/95p48x+4ZyiPK6665TdXV1p8n+P/7xj/2yLgAAAAAAMHD4Fz63uhGtc5q9+yZAaS7VBlvDlKOssHKT2vZk4mLpzFChxtphLYnV6ZVYjepaw4tqC0e2trtuDVP6QUrfIcEirXAa1JhIajXU+piSQFAT7Vzl9KCVt9+2/EMnoo1OS6vwjnOtiDd4IcvcgO2dl+nBAh0QLGhrfe4fh/bcAAAAAAAMXKay38aNGzvdv9dee2UsMGa6snblvffe02C0efNm/exnP0u5b+HChTrxxBP7fE1DJUxp7q9bt07r16/XI4880rZ99OjRXphyv/320/777+/d39OsVTJT/TKVcLil00u2IESJjDMVJlOV7vWdeeaZmjVrVq/m+NrXvqZ//etfWrt2bcr9q1at0rPPPqsFCxZoqHn66afb/aGZjGQ/AAAAAABI1pxwtMZp9tp2JwcGTdVHv4rjRCs34ydtuBXWJ3KG6YRwuV6PmzBlrT5sDXaadcW8cGeiXThytBXWMeFSFQRsLW2sS3nciXaON3oSAvWrSK6KN6rSjad8jAlqvhVvaPv6uWiNcmXpoFCh5odLtFcwj2qUAAAAAAAMcCZA2VWb4kmTJmVsblONcdiwYV6H0c7WNhhdddVVKdtO5+fn68orr+yXNQ12Ha9jdXzPbtmyRVu3btVTTz3Vts10pvVDlf6tqWLZU9FoVPfdd1/Ka2hFRenrVDMYEKJERpkftn/84x+d7jep5W9961u9nicUCunb3/62vvOd73T6mDvvvHPIhSjr6+t19dVXp9xHsh8AAAAAAHRkwoJrnCbvfvtW2VKerJb21VYw4y17/HbYpqLj7FCxN6rdmD5wmlSbcLzKmKbteHEgqJFWSCOtsIZZIUUTrv4Z2eE91+pQqdJWQNPsfJUHum/l3dba3I3rfadJkbYamC3H8UOl5r7hf22+Mk3IF8dqvTHNztO8cKkOCBUotwfVLwEAAAAAQN/rLqg4bty4jM4/fvz4TkOUlZWVamho8FqKDxYPPPCAnnvuuZT7TCfZTJ/PocoPTfrXtFJd2+oYrDTvn5deeskbPvNeMoFKf0ydOtWriNqxvboJZF5zzTVeVdFAINB2vcy/HTt2rLIJIUpk1DPPPKPa2tpO95988skaMWJEWuY6/vjjvT+IN23alHL/iy++6P3hUV5erqHCtEE3f6h1RLIfAAAAAAB0ZC6Arnaa2lp5mxBiSzPvFqPtsNcK239sJkOUfhVIwzFttSWVWiEdZrUEIDuzMt6ot1srQ/pr90OUJgC6t92zypD+a9viRlXdWoVyhBXyQqYmMJkjywtWJocnk0Ob/pwmgLm9OabVTqOODpdqTGsVTAAAAAAAMHCYkFhXhg8fntH5TSXKrpgKg3vvvbcGA5O7+eEPf5hy3/Tp03XOOef0+ZqGCj/A2FnVVLO/J8FKU5Dttdde80bHypKmcqUpeFdXV6dt27Z1egxj3333VTYhRImM+ve//93l/jPOOCNtc9m2rVNPPVW33npryv3xeFyPPvqoPv/5z2soWLp0qe66666U+0j2AwAAAACAjsxFVlPVsTQQVHUirnhrQNAEGM3lV9PG27TM9h/bV0w1SsO7SNwasEy+b0KW5jE73ahei9eqvkMrcv92VrDIq1i5O8z5+HbBeK/qpblWXGTZ2upEtcONqTHheKHK951G72s/UGl1mNc817T53uhEdVJuuaYHB0/lCAAAAAAAsoEJ/nUlXcW/9vT43a1vILnhhhtUVVW1y3bLsnTdddcpGCSK1htdhSRTBR17Gqw0TBG8VIXw/PBmR0cccYSySda8c2+66Sa98sorKfddeumlmjNnTp+vaahzHMer/tiZkSNHatasWWmd86STTuo0RGmYcsJDIURp2qRfccUVKf8QI9kPAAAAAAA6s2+wQFcV7qXNTkQvxWq0NFbntac2lRcn27kK72ZLar9iZSzheq2v/QDknoQwvYu+qe633pr22e/Fm9o/pzXMaKpIHhwqbAtk9lR5a+XLkqTLpP42E940x47J1UYnoiXROu98+a2/k9dmHmfapP+uYYtOzxvutfgGAAAAAAxQCfPv2f5exCCU4pytWrVKjY2NvTrsIYccokyrqanpcr+p0JdJ3R2/u/UNFCZz8+CDD6bcd9ZZZ2nmzJl9vqbBLrl9tqlYunPnzl32d3adrbOqlV09x39e8v6ObcT99+zChQuVTbImRGkClCtWrNglPWv6t8+ePbtf1zZUvf322175187Mmzcv7VUNTHlj8z3trBTzkiVLvIqUgz35/utf/1qrV6/eZTvJfgAAAAAA0BXXtM4OBDTWztGn7ZH6dO5IvRmr02Y3qnGtrah3JwRprrKZR74Uq1VZIKiZocK0X+8x693mRvV0tLqtGqQ/r3+V75hQmfIDdtvr213Jr9k/hh/IDMrWtGC+N050y/VqtFavxGq96pTt1il5AcuHmisUlKU5oZZfkOzJegAAAAAAGAyuvPLKXoco33vvPWVadyHFwsLCjM7f3fGrq6s10Jnv89VXX91pEbULL7ywz9c01Lzwwgvavn27l29799132243btzY7nH+NazOKlB21Q48+TbV4xKt18hMW/a8vDxlk8GdJNsNW7Zsafdm8L/pixYt6tP2RNlk8eLFXe6fO3duRuY1x73vvvtS7mtoaNDy5ct18MEHa7Ayn+T4/e9/n3IfyX4AAAAAANAVP9CX3C774FCRkq+U7M61Mqu1CuUT0Uqv/fWnEiN0VKjE227CiOZIe3rtzQ8zbnCa9US0ygtQWgrIVctx/SDlfnaBDm0NLO7pVb7kNaYKPfoXlYdZIR2XU6ZRdlhPR6q12vmoMqapT2meWZdw9EikQuPtHG/sabATAAAAAACkR3dBz/z8/Iye6u6O39TUvvPGQPTTn/5UmzZtSrnvsssuy3gQNVuYQKoZCxYsaNtmCth1DFauWbPG6xC8O629OwtX+s/1HXDAAfrGN76hbJM1Icr6+vqU2w866KA+X0u2eOedd7rcn6kyvgceeGCnIUp/XYM1ROm6ri6//HLFYu0rHRgk+7FH7ylOG9CnXFpD9MjuNZDMXqa9JLpnk1foId5P3b+XeDP1RJS/7HokbPF+6tn7ifPUN+ep5dPpexJ09MOB7zj1XoDS/Bvz8UilahxHR9ilCrYdc8//nnETrv4Vq9Bat+WXCS3Rz4+qUI4J5Ogwq1jNcakp4bS+jkQGf+5ajj1FBRoWytGj2qm3nZbrjm5SsHOnG9OfG7fqGznjFPJapA/8v2uj/I9Tj8QG/rdyQIh2qNSK1Apj7X/hhdScKOepJxIOf0B1x3W4Ig70tYDF1V4AA4PpWNqVTHczDYVCXe5PlQEZSJYtW6Y777wz5T7T8vnEE0/s8zVlE9Nae86cOd7wRaNRrVy5sl240hRla25u3q3W3r5E6+8dDz30UP3qV78a9B1+90TWvGLbtndJ4BqTJk3ql/VkA/ND2pmCggJNnjw5I/OaRHRXzB8cg9Udd9zh/eWUCsl+AAAAAACwJ/a0UqT/LBOibKvCKEdPxiu90KMJUk6ychX2QoS7H86scKN6xanRB25j21yJDrfzg2WaYrdUc+irbjN+6LTECmpRaJh2JmLa5EbatRc3r3iLG9GSeK2OCpX2yboAAAAAAMCehRQzHRjr7vgDOURp1maKfZmiX6kqbJqW7uh74XDYK16XXMDOfI9MhcrkipXvvfdej9rFz5gxQ5///Od1+umny8rSD0FkTYjS/OCaFG6qtC4yUwp548aNne7fa6+9MnZhe8qUKV3uN39ADEabN2/Wz372s5T7SPYDAAAAAIC+Zq7tVCdiWuk2eF977cFbb02I0lSnPNgu1oF2oQoCdstjkqpe+p9wN1/72xOtAUoTQHws/lEFyrY5W48flqWD7SKvlXd/vG6fae/92fAo3dq8Uc2t/R4SSWHK1+N1mh0sVkg9+9Q/AAAAAACDybXXXqvx48drsFeiNIXZMqm743e3vv7029/+1qtwmMr555+vcePG9fmakJoJP+69997eOOWUU9q279y5U+vWrdPWrVtVVVXltY8316lMC/axY8dq//331/Dhw7P+tGZNiLK0tDRlsjZVsBK9ZwKU/oXwVDJZAdT8kA8bNkwVFRWdrm0wuuqqq7xwakck+wEAAAAAQH9xE9I0K1/vu43tGlab+1sTUT0a36klTo0XeDSjOPDR5cjkUKF/v16OXovX6uV4jaKtoUT/eMmVHmfbxTo8WNK6hpbgZX9wEgkNt8LeWp6NV7XbZ1a0JRHROrdZ+9j5e9w2HQAAAACQXubfkWZg989bR/vss4/23XffAX8qu6sEmarKYjql6pybbKC2Tl69erUXokxl+vTpOuecc/p8Tdh9JiBJSLJ7A/OnMANM6+i1a9fucqHSpG2nTZvWb+saqroLKmY6iW4+6dBZiLKyslINDQ1eS/HB4oEHHtBzzz2Xch/JfgAAAAAA0F/KrZAWBssViAe8ipT+r1MCSaHHykRMT8UrvTHFytNUK1/FAVuWAioLhNSUcLQ9EfVaYq93m7yW4P4xko9n7tsK6CC7SMcGy9uCk/0VoDTs1rkPDBZqcbymrRplMhMKNSFKApQAAAAAAPSPUCjUr+20u6s02d36+oP5MKhp452qOJ2peHjdddcN2PAnsCey5t08depUPf3007ts7yxoh963nu5KphPOphJlV7Zs2eKVrx0MTOjzhz/8Ycp9JPsBAAAAAEB/G2Pl6HPh0VrhNOjleLU2JlqihH4A0rT4Nsy2NW6TN4IKeIHIiFyvNXdy1cmO1SfNMHtzZWmOXaKFwTIvODmQKjuOtXK81t4mCJoc/jQaEo4iCfM6aekNAAAAAEB/6C7sl+l22oMxRPnXv/5Vr7/+esp9Z511lmbOnNnnawIyyb+GOeTNnTs35falS5f2+VqygQn+dWXEiBEZnb+743e3voHkhhtuUFVV+3ZQBsl+AAAAAAAwkNp57Wvl62OhYTrELlaR7JSBSKt1xJXwApRmmwlQprpI6YcRTYByeCCkM0OjtGAABijNWoy9rfyWr/3tra/hQ7fZq7Y5UNYLAAAAAEC2yc9v+Td7ZxobGzM6v+mW2pW8vDwNJFu3btXNN9+cct/IkSN14YUX9vmagEzLmkqUs2fP9v7QaW5u9r42Fy3NBc5nnnlGV111VX8vb8ipqanpcn9RUVFG5+/u+N2tb6AwLbwffPDBrEn2v/HGGxltIw8AAAAAANIvua32hECuxgVytJ9VoNecWq/Ft9shFOmHC5V0P7lqpf94s61QtmbYhToqWKqSQLAtsDiQAonJFTOTX4P/ukwFzZYG5QAAAAAAoD+UlJT0KuTYW90dv7S0VAPJ1Vdf3emaL7vsMhUWFvb5moBMy5oQZU5Ojk4++WT9/e9/b3eR1aSnFy9erMMPP7xf1zfUdBdSzPQfqN0dv7q6WgOd+aSD+Yspm5L9n/vc53r96ZHB0qYdAAAAAIChyoQp97bzvVHlxrTUqdXbbr1qEi2tqzq2u06+b8KHIQWUI0sz7SLNsou9NtnevkSiLbA5kLRVxkwRAjUcJVSZiGmYBl5rLgAAAAAAskF3Icra2tqMzt/d8btbX196+OGH9fTTT6fct3DhQp144ol9viagL2RNiNI455xzdO+997Z9Yt0w92+88Ubdd999A+oT7INdd6WOuyuV3FvdHb+pqUkD3U9/+lNt2rQp5T6S/QAAAAAAYDAos0JaZA3TIg3TerdJHzhNqkrEvHbeJlhYn4irNBBSTAmVBoIaHQhrrJWrSVau8gN2u+t4AzFA6TPXFc36kitQ+prlakSgJUA5kNqQAwAAAEC2chOS438CDrt13gar8vLyLvfv3Lkzo/N3d/zu1tdXTEGyH/zgB53mcK688so+XxPQV7IqRGkq1JkWyHfeead3sdJv6b1y5Ur94Q9/0Ne+9rX+XuKQEY+3VBb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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 1023, + "width": 1320 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "bias_mu_grid = np.linspace(-0.5, 0.5, 25)\n", + "bias_sd_grid_2d = np.linspace(0.01, 1.0, 25)\n", + "prob_grid = np.zeros((len(bias_sd_grid_2d), len(bias_mu_grid)))\n", + "\n", + "for i, b_sd in enumerate(bias_sd_grid_2d):\n", + " for j, b_mu in enumerate(bias_mu_grid):\n", + " pm_a, pc_a = gaussian_sensitivity_posterior(\n", + " d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=b_sd, bias_prior_mean=b_mu\n", + " )\n", + " tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0])\n", + " prob_grid[i, j] = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd)\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.5, 5))\n", + "im = ax.imshow(\n", + " prob_grid,\n", + " origin=\"lower\",\n", + " aspect=\"auto\",\n", + " extent=[bias_mu_grid.min(), bias_mu_grid.max(), bias_sd_grid_2d.min(), bias_sd_grid_2d.max()],\n", + " cmap=\"RdBu_r\",\n", + " vmin=0.0,\n", + " vmax=1.0,\n", + ")\n", + "contour = ax.contour(\n", + " bias_mu_grid,\n", + " bias_sd_grid_2d,\n", + " prob_grid,\n", + " levels=[0.5, decision_threshold],\n", + " colors=[\"white\", \"black\"],\n", + " linewidths=[1.2, 2.0],\n", + ")\n", + "ax.clabel(contour, fmt={0.5: \"P=0.5\", decision_threshold: f\"P={decision_threshold}\"})\n", + "ax.set_xlabel(r\"Prior mean of bias $\\beta$\")\n", + "ax.set_ylabel(r\"Prior SD of bias $\\beta$\")\n", + "ax.set_title(r\"$P(\\tau > 0)$ under bias-prior commitments\")\n", + "plt.colorbar(im, ax=ax, label=r\"$P(\\tau > 0 \\mid d_{\\text{obs}})$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right. It tells both parties exactly what they are disagreeing about: which region of the bias-prior plane their prior commitments occupy, and whether that region sits inside or outside the contour of defensibility. The audit has a legible geometry." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "## Robustness as a posterior question\n", "\n", "The randomisation gap is not closed by a sensitivity analysis. It is mapped. The analyst who runs the sweep above has not proved the experiment robust; they have produced an audit-able exhibit of which bias commitments the experiment can survive, and which it cannot. The audit is the deliverable.\n", "\n", - "This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. Two analysts holding the same data and disagreeing about a conclusion are not really disagreeing about the data. They are disagreeing about which side of the tipping point their prior commitments sit on. The Bayesian sensitivity machinery makes the locus of disagreement explicit, named, and graphable.\n", + "This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. The sensitivity surface extends this from a point to a topology: the full region of bias-prior commitments the experiment can survive, with its boundary drawn as a contour. Two analysts holding the same data and disagreeing about a conclusion are not disagreeing about the data. They occupy different coordinates in the bias-prior plane, and the surface tells them precisely which commitment separates them.\n", "\n", - "The deeper move is the one we are tracing across the three notebooks in this sequence. The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In all three the same move recurs: an assumption a conventional analysis would simply make becomes a parameter the analysis estimates, and so turns from something assumed into something contestable. The posterior is the constant; the question changes. We have just seen the second.\n", + "The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In each: an assumption a conventional analysis leaves implicit becomes a parameter with a posterior — something to argue about rather than assume. The posterior is the constant; the question changes. We have just seen the second.\n", "\n", "## Authors\n", "\n", @@ -1073,10 +908,10 @@ "id": "66eea177", "metadata": { "execution": { - "iopub.execute_input": "2026-05-28T16:43:59.006072Z", - "iopub.status.busy": "2026-05-28T16:43:59.005931Z", - "iopub.status.idle": "2026-05-28T16:43:59.022107Z", - "shell.execute_reply": "2026-05-28T16:43:59.021456Z" + "iopub.execute_input": "2026-06-01T17:16:17.374153Z", + "iopub.status.busy": "2026-06-01T17:16:17.374021Z", + "iopub.status.idle": "2026-06-01T17:16:17.395009Z", + "shell.execute_reply": "2026-06-01T17:16:17.394567Z" } }, "outputs": [ @@ -1084,25 +919,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Thu May 28 2026\n", + "Last updated: Mon, 01 Jun 2026\n", "\n", "Python implementation: CPython\n", - "Python version : 3.12.12\n", - "IPython version : 9.8.0\n", + "Python version : 3.13.13\n", + "IPython version : 9.14.0\n", "\n", - "pytensor: 2.38.2\n", - "xarray : 2025.12.0\n", + "pytensor: 3.0.3\n", + "xarray : 2026.4.0\n", "\n", - "matplotlib: 3.10.8\n", - "seaborn : 0.13.2\n", - "pandas : 2.3.3\n", - "scipy : 1.16.3\n", + "arviz : 1.1.0\n", + "matplotlib: 3.10.9\n", "numpy : 2.3.5\n", - "pytensor : 2.38.2\n", - "pymc : 5.28.5\n", - "arviz : 0.23.0\n", + "pandas : 2.3.3\n", + "pymc : 6.0.1\n", + "pytensor : 3.0.3\n", + "scipy : 1.17.1\n", + "seaborn : 0.13.2\n", "\n", - "Watermark: 2.5.0\n", + "Watermark: 2.6.0\n", "\n" ] } @@ -1127,7 +962,7 @@ "default_lexer": "ipython3" }, "kernelspec": { - "display_name": "pymc-marketing-dev", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1141,7 +976,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.13.13" } }, "nbformat": 4, diff --git a/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md b/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md index 3ee4e8955..994a725ff 100644 --- a/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md +++ b/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md @@ -6,7 +6,7 @@ jupytext: format_name: myst format_version: 0.13 kernelspec: - display_name: pymc-marketing-dev + display_name: Python 3 language: python name: python3 --- @@ -51,14 +51,14 @@ warnings.filterwarnings("ignore", category=UserWarning) ```{code-cell} ipython3 %config InlineBackend.figure_format = 'retina' -az.style.use("arviz-darkgrid") +az.style.use("arviz-variat") rng = np.random.default_rng(7) RANDOM_SEED = 7 ``` ## What the data can and cannot say -The structural picture is small and the notation traditional. Let $T$ be the observed treatment indicator (which flow the visitor actually saw), let $Y$ be the outcome (revenue), and let $U$ be an unmeasured user characteristic: connection quality, engagement disposition, whatever drives the differential compliance. Under randomisation $U$ is independent of $T$ by construction; under compliance failure $U$ is associated with $T$ in a way the analyst cannot directly observe. +The structural picture is small and the notation traditional. Let $T$ be the observed treatment indicator (which flow the visitor actually saw), let $Y$ be the outcome (revenue), and let $U$ be an unmeasured user characteristic: connection quality, engagement disposition, whatever drives the differential compliance. Under randomisation $U$ is independent of $T$ by construction; under compliance failure $U$ is associated with $T$ and unobservable. ```{code-cell} ipython3 :tags: [hide-input] @@ -98,13 +98,13 @@ What we want is $\tau$, the causal effect of $T$ on $Y$. What the data identify ## The bias parameter -The Bayesian sensitivity analysis introduces $\beta$ as a model parameter with an explicit prior. The data inform the sum $\tau + \beta$ through the observed difference; the prior on $\beta$ controls how much of that sum is attributed to the unmeasured confounder rather than to the treatment. Three commitments are useful to compare: +The Bayesian sensitivity analysis makes $\beta$ a model parameter with a prior. The data inform the sum $\tau + \beta$ through the observed difference; the prior on $\beta$ controls how much of that sum is attributed to the unmeasured confounder rather than to the treatment. Three prior commitments: - **Dismissive prior** ($\beta \sim \mathcal{N}(0, 0.05)$): "I am confident there is essentially no confounding." - **Moderate prior** ($\beta \sim \mathcal{N}(0, 0.3)$): "I will allow that confounding may have shifted the apparent effect by up to a few tenths of a revenue unit." - **Sceptical prior** ($\beta \sim \mathcal{N}(0, 0.7)$): "I will not commit to confounding being small; the data must speak loudly to be heard." -Each commitment is a separable, auditable claim. The plot we are about to build shows how the posterior on $\tau$ moves as the analyst slides this commitment. +Each is an auditable commitment. ```{code-cell} ipython3 N = 4000 @@ -127,7 +127,7 @@ print(f"Observed difference d_hat = {d_hat:.3f} (sampling SE = {sigma_d:.3f})") print(f"True tau = {TRUE_TAU}, true bias = {TRUE_BIAS}, true sum = {TRUE_TAU + TRUE_BIAS}") ``` -The observed difference recovers the *biased* effect, as it must. The treatment effect $\tau$ is what we want; the bias $\beta$ is the channel we cannot observe. We now build a PyMC model that asks the data which decomposition is most plausible *given* a prior on the bias. +The observed difference recovers the *biased* effect, as it must. $\tau$ is what we want; $\beta$ is what we cannot observe. The model below asks which decomposition the data find most plausible given a prior on $\beta$. ```{code-cell} ipython3 def gaussian_sensitivity_model(d_hat, sigma_d, tau_prior=(0.0, 1.0), bias_prior=(0.0, 0.3)): @@ -149,13 +149,16 @@ with gaussian_sensitivity_model(d_hat, sigma_d, bias_prior=(0.0, 0.3)): progressbar=False, ) -axes = az.plot_posterior( - idata_moderate, var_names=["tau", "beta"], ref_val=[TRUE_TAU, TRUE_BIAS], figsize=(8, 3.5) -) -plt.suptitle(r"Posterior of $\tau$ and $\beta$ under a moderate bias prior", y=1.05); +az.plot_dist(idata_moderate, var_names=["tau", "beta"]) +axes = plt.gcf().axes +axes[0].axvline(TRUE_TAU, color="C2", linestyle="--", label=f"true τ = {TRUE_TAU:.2f}") +axes[0].legend() +axes[1].axvline(TRUE_BIAS, color="C2", linestyle="--", label=f"true β = {TRUE_BIAS:.2f}") +axes[1].legend() +plt.suptitle(r"Posterior of $\tau$ and $\beta$ under a moderate bias prior", y=1.05) ``` -The posterior of $\tau$ is centred well above zero but with substantial uncertainty; the posterior of $\beta$ is also non-zero, reflecting the prior pull. The data have informed the sum but the decomposition is shaped by the prior on $\beta$. The next move is to vary that prior and see what the data tolerate. +The posterior of $\tau$ is centred above zero; the posterior of $\beta$ is non-zero, pulled by the prior. The data informed the sum; the prior shapes the decomposition. ### Tipping-point analysis under a Gaussian outcome @@ -254,51 +257,11 @@ axes[1].set_title("Tipping point in the decision rule") axes[1].legend(); ``` -Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. - -### The sensitivity surface +Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The question has changed from what the treatment did to which prior commitments on the unmeasured bias are required to believe that it did anything; the machinery has not. -The previous sweep moved one dimension of the bias prior. The bias prior has two, the mean and the standard deviation, and an analyst defending the conclusion ought to be able to point at the region of $(\mu_\beta, \sigma_\beta)$ where the decision still goes through. The two-dimensional sweep below maps it. +The same machinery runs on the log-odds scale. Before mapping the full topology of the bias-prior space, we confirm that the one-dimensional fragility picture is not an artefact of the Gaussian likelihood. -```{code-cell} ipython3 -bias_mu_grid = np.linspace(-0.5, 0.5, 25) -bias_sd_grid_2d = np.linspace(0.01, 1.0, 25) -prob_grid = np.zeros((len(bias_sd_grid_2d), len(bias_mu_grid))) - -for i, b_sd in enumerate(bias_sd_grid_2d): - for j, b_mu in enumerate(bias_mu_grid): - pm_a, pc_a = gaussian_sensitivity_posterior( - d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=b_sd, bias_prior_mean=b_mu - ) - tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0]) - prob_grid[i, j] = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd) - -fig, ax = plt.subplots(figsize=(6.5, 5)) -im = ax.imshow( - prob_grid, - origin="lower", - aspect="auto", - extent=[bias_mu_grid.min(), bias_mu_grid.max(), bias_sd_grid_2d.min(), bias_sd_grid_2d.max()], - cmap="RdBu_r", - vmin=0.0, - vmax=1.0, -) -contour = ax.contour( - bias_mu_grid, - bias_sd_grid_2d, - prob_grid, - levels=[0.5, decision_threshold], - colors=["white", "black"], - linewidths=[1.2, 2.0], -) -ax.clabel(contour, fmt={0.5: "P=0.5", decision_threshold: f"P={decision_threshold}"}) -ax.set_xlabel(r"Prior mean of bias $\beta$") -ax.set_ylabel(r"Prior SD of bias $\beta$") -ax.set_title(r"$P(\tau > 0)$ under bias-prior commitments") -plt.colorbar(im, ax=ax, label=r"$P(\tau > 0 \mid d_{\text{obs}})$"); -``` - -The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right; it tells both parties exactly what they are disagreeing about. ++++ ## The same machinery on a binary outcome @@ -406,15 +369,63 @@ axes[1].set_title("Tipping point on the log-odds scale") axes[1].legend(); ``` -The shape of the curve is the one we saw in the Gaussian case. The decision rule loses confidence in the positive treatment effect as the analyst becomes more willing to attribute the observed log-odds shift to an unmeasured confounder. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve here is the full posterior over the bias-prior commitment, and is the object the analyst should actually be reading. +The shape of the curve confirms what the Gaussian case showed: the decision rule loses confidence as the prior on bias widens, and the tipping point is identifiable on the log-odds scale just as it was on the revenue scale. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve is the full posterior over that commitment. One dimension, two likelihoods, the same fragility picture. The complete audit — both dimensions of the bias prior, the full region of commitments the conclusion can survive — is the surface below. + ++++ + +### The sensitivity surface + +Both the Gaussian and the binary sweep moved one dimension of the bias prior. The complete audit requires the other: a map of the full region of $(\mu_\beta, \sigma_\beta)$ commitments the experiment can survive. The sweep below opens the line into a surface. + +```{code-cell} ipython3 +bias_mu_grid = np.linspace(-0.5, 0.5, 25) +bias_sd_grid_2d = np.linspace(0.01, 1.0, 25) +prob_grid = np.zeros((len(bias_sd_grid_2d), len(bias_mu_grid))) + +for i, b_sd in enumerate(bias_sd_grid_2d): + for j, b_mu in enumerate(bias_mu_grid): + pm_a, pc_a = gaussian_sensitivity_posterior( + d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=b_sd, bias_prior_mean=b_mu + ) + tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0]) + prob_grid[i, j] = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd) + +fig, ax = plt.subplots(figsize=(6.5, 5)) +im = ax.imshow( + prob_grid, + origin="lower", + aspect="auto", + extent=[bias_mu_grid.min(), bias_mu_grid.max(), bias_sd_grid_2d.min(), bias_sd_grid_2d.max()], + cmap="RdBu_r", + vmin=0.0, + vmax=1.0, +) +contour = ax.contour( + bias_mu_grid, + bias_sd_grid_2d, + prob_grid, + levels=[0.5, decision_threshold], + colors=["white", "black"], + linewidths=[1.2, 2.0], +) +ax.clabel(contour, fmt={0.5: "P=0.5", decision_threshold: f"P={decision_threshold}"}) +ax.set_xlabel(r"Prior mean of bias $\beta$") +ax.set_ylabel(r"Prior SD of bias $\beta$") +ax.set_title(r"$P(\tau > 0)$ under bias-prior commitments") +plt.colorbar(im, ax=ax, label=r"$P(\tau > 0 \mid d_{\text{obs}})$"); +``` + +The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right. It tells both parties exactly what they are disagreeing about: which region of the bias-prior plane their prior commitments occupy, and whether that region sits inside or outside the contour of defensibility. The audit has a legible geometry. + ++++ ## Robustness as a posterior question The randomisation gap is not closed by a sensitivity analysis. It is mapped. The analyst who runs the sweep above has not proved the experiment robust; they have produced an audit-able exhibit of which bias commitments the experiment can survive, and which it cannot. The audit is the deliverable. -This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. Two analysts holding the same data and disagreeing about a conclusion are not really disagreeing about the data. They are disagreeing about which side of the tipping point their prior commitments sit on. The Bayesian sensitivity machinery makes the locus of disagreement explicit, named, and graphable. +This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. The sensitivity surface extends this from a point to a topology: the full region of bias-prior commitments the experiment can survive, with its boundary drawn as a contour. Two analysts holding the same data and disagreeing about a conclusion are not disagreeing about the data. They occupy different coordinates in the bias-prior plane, and the surface tells them precisely which commitment separates them. -The deeper move is the one we are tracing across the three notebooks in this sequence. The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In all three the same move recurs: an assumption a conventional analysis would simply make becomes a parameter the analysis estimates, and so turns from something assumed into something contestable. The posterior is the constant; the question changes. We have just seen the second. +The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In each: an assumption a conventional analysis leaves implicit becomes a parameter with a posterior — something to argue about rather than assume. The posterior is the constant; the question changes. We have just seen the second. ## Authors diff --git a/pixi.toml b/pixi.toml index 284d50ac4..9eaca3712 100644 --- a/pixi.toml +++ b/pixi.toml @@ -3,19 +3,19 @@ authors = ["Chris Fonnesbeck "] channels = ["conda-forge"] description = "Add a short description here" name = "pymc-examples" -platforms = ["linux-64"] +platforms = ["linux-64", "osx-arm64"] version = "0.1.0" [tasks] [dependencies] python = ">=3.13.9,<3.14" -pymc = ">=5.26.1,<6" +pymc = ">=6.0,<7" jupyter = ">=1.1.1,<2" ipykernel = ">=7.1.0,<8" ipywidgets = ">=8.1.8,<9" numpy = ">=2.3.5,<3" -arviz = ">=0.22.0,<0.23" +arviz = ">=1.0,<2" numpyro = ">=0.19.0,<0.20" seaborn = ">=0.13.2,<0.14" matplotlib = ">=3.10.8,<4" From 507df2669191a3c7412a407b1ee299421fdf532a Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Tue, 2 Jun 2026 19:16:29 +0100 Subject: [PATCH 5/9] Update the writing Signed-off-by: Nathaniel --- .../assurance_planning_simulation.ipynb | 89 +++-- .../assurance_planning_simulation.myst.md | 39 ++- .../multiple_experiments_meta_analysis.ipynb | 30 +- ...multiple_experiments_meta_analysis.myst.md | 8 +- .../sensitivity_unmeasured_confounding.ipynb | 310 +++++++++++------- ...sensitivity_unmeasured_confounding.myst.md | 96 ++++-- 6 files changed, 351 insertions(+), 221 deletions(-) diff --git a/examples/causal_inference/assurance_planning_simulation.ipynb b/examples/causal_inference/assurance_planning_simulation.ipynb index 1ab392668..6b6abd667 100644 --- a/examples/causal_inference/assurance_planning_simulation.ipynb +++ b/examples/causal_inference/assurance_planning_simulation.ipynb @@ -14,13 +14,13 @@ ":author: Nathaniel Forde\n", ":::\n", "\n", - "Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records — an off-kilter orientation in pursuit of theory, anomalous findings and the next question.\n", + "Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records, orientated at a new angle in pursuit of theory, anomalous findings and the next question.\n", "\n", "## The question planners are actually asking\n", "\n", - "When we pursue an experiment, we implicitly assume the experiment will be useful. Our questions are \"operationalised\" as power: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold.\n", + "When we experiment, we implicitly assume the experiment will be useful. Our questions are \"operationalised\" as power heuristics: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold.\n", "\n", - "An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the conclusion is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery answers all three questions; the question being asked of it is what changes.\n", + "An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery serves all three; what changes is the problem put to it.\n", "\n", ":::{admonition} Where this lands in regulatory practice\n", ":class: note\n", @@ -49,6 +49,7 @@ "\n", "import arviz as az\n", "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as mticker\n", "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", @@ -127,13 +128,13 @@ "\\text{Power}(N) = P\\!\\left(\\text{reject } H_0 \\mid \\theta = \\theta_0,\\; N\\right).\n", "$$\n", "\n", - "The conditioning bar is the load-bearing part. The planner who has confidently named $\\theta_0$ has implicitly used up the uncertainty that motivated the experiment in the first place. Assurance refuses the substitution. Instead it integrates over the planner's prior beliefs about the effect:\n", + "The conditioning bar is the load-bearing part. The planner who has confidently named $\\theta_0$ has implicitly used up the uncertainty that motivated the experiment in the first place. Assurance refuses the substitution. Instead it integrates over the planner's prior beliefs about the effect. The decision rule is whether the posterior probability of a positive effect clears a threshold $c$:\n", "\n", "$$\n", - "\\text{Assurance}(N) = \\int P\\!\\left(\\text{decision} \\mid \\theta,\\; N\\right)\\, \\pi(\\theta)\\, d\\theta.\n", + "\\text{Assurance}(N) = \\mathbb{E}_{\\substack{\\theta \\,\\sim\\, \\pi(\\theta) \\\\ \\hat{d} \\,\\sim\\, p(\\hat{d} \\mid \\theta,\\, N)}}\\!\\left[\\mathbb{1}\\!\\bigl(P(\\theta > 0 \\mid \\hat{d},\\, N) > c\\bigr)\\right].\n", "$$\n", "\n", - "This is the marginal probability of a successful decision, where \"successful\" is whatever decision rule the team has agreed in advance and $\\pi(\\theta)$ is the team's prior over the effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in.\n", + "The outer expectation averages over effect sizes drawn from the prior; the inner averages over data generated under each effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in.\n", "\n", "## The simulation engine\n", "\n", @@ -282,16 +283,6 @@ } }, "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Posterior on $\\\\delta$ at $N=200$ (one simulated dataset)')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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", @@ -313,7 +304,7 @@ "ax = plt.gcf().axes[0]\n", "ax.axvline(0.0, color=\"C2\", linestyle=\"--\", label=\"ref = 0.0\")\n", "ax.legend()\n", - "ax.set_title(r\"Posterior on $\\delta$ at $N=200$ (one simulated dataset)\")" + "ax.set_title(r\"Posterior on $\\delta$ at $N=200$ (one simulated dataset)\");" ] }, { @@ -586,15 +577,15 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 432, - "width": 613 + "height": 431, + "width": 1233 } }, "output_type": "display_data" @@ -617,7 +608,7 @@ "\n", "prior_prob_positive = 1.0 - stats.norm.cdf(0.0, EFFECT_PRIOR.mu, EFFECT_PRIOR.sigma)\n", "\n", - "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "fig, ax = plt.subplots(figsize=(15, 4.5))\n", "ax.plot(N_GRID, assurance_df[\"assurance\"], marker=\"o\", linewidth=2, label=\"Assurance (Bayesian)\")\n", "ax.plot(\n", " N_GRID,\n", @@ -650,6 +641,7 @@ " label=rf\"Assurance ceiling $P(\\delta>0)$ = {prior_prob_positive:.2f}\",\n", ")\n", "ax.set_xscale(\"log\")\n", + "ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{int(x):,}\"))\n", "ax.set_xlabel(\"Sample size per arm\")\n", "ax.set_ylabel(\"Probability of declaring uplift\")\n", "ax.set_title(\"Assurance vs frequentist power, Gaussian outcome\")\n", @@ -758,15 +750,15 @@ }, { "data": { - "image/png": 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79llgfbBKu3as2uelfW7ac63qso0H9nggNk7+6le/cp7rHdNtPLDXsfHOxj37DmDrxNaPfbe376pu6xkAAABAE9TQaTgAAAAAaCmsikHr1q19VQA++uijCo/fc889vsdGjx4ddFlWdcuqKFlbq3Tw8ssvB6ymZFWsHn/8cc8LL7xQ5TGrxOJ9TateZFWMAlU7sf5eccUVDVIZzfterUqHW1UQ659/ZTSrLjV37tyAr79p0ybPeeed51v++PHjA7b1rxLirRAzcuRIz8KFCyu0s/X/17/+tUKlh2+++Sbgcm+99dYKba+55pqAFfFmzJjhVK2YN29elcfsPv/Katdff73rcpYtW+bJysrytTvmmGM8dWH7nHdZBxxwQLXt3333XV/7fv36hW1frImaVIDxr8birS7SrVs316pI/vujVUTzPq9Dhw6eN99806mUVplVBfOv8mXtAvXZqpL4V0jZsWNHlf17woQJvkqL1VXCCcfxa+/xsMMOq1AtcObMmVXa5efnO2Of9z2lpKR4li9fXm11GzsWre2rr75aZeyzY8K/Ss1FF10U8L3U9L2H8hyr2OZ9vFOnTs6YFIj11cYCO8ZDqW5Tn/yrJHn3E6sWZp8ZlbeRjSv+fXnxxRddl2kVA/fbbz9fu/bt23s+++yzKu3svoyMjAr7h1vFtYceesjX5rbbbnN9zQMPPND3WtW19a+uOWXKFNfPaf9KWrY+fvnllyrt7Jjzr4xo23n79u2ur+m/3uyzzI5z/2qebuNGY6iM9vHHH1eoRHbDDTcEbW+fUd62VqXTv9qfV0lJiefpp5/2fZbaWPrdd99VW+nL1puNEffdd1+V/cR/vfkfm97XsCqSlatI2Wfk4Ycf7mvbu3fvgN+hrHKff7VV+7yzyn+Vx7tHH320QrXKa6+91nV5La0yWnNaf5XZdxH/z2Pbpyp/N/SyzzX7PmDfncK9jiqPOza+22eiVWGufDzefPPNFSqUev8tYpUhV61aVaG9jXH+n+vWJpANGzZ4MjMzfW3POuss16q8VkX05JNP9rUbOnSo0y8AAAAATR9hNAAAAACIEAtN+IdSbAqyymEh/5NHgU5oeafy8bY755xzat2ndu3a+U7a7tq1q9bLCXcYzXuCKi8vz1OfLJDlXb5N5+Wm8pR5ffv2DbquTjvtNF9bCyu4sXCD/wlFC1zUlv8J9fvvvz9oW5t6yT9oESwkUx1bB/4hOLfAhj//qRr/8Ic/hG1fjEQYzS723qt7z/Ycb3DNgqjBjmljU+B5l2/byS0c4T/1owXO3IJt3gBR5amxIhlGe+WVV3xtrB/VbVObKq+648Y/UGABgE8++STg8mx6Tm9bO8lfebwNZxjtpptu8j3+97//3VMX/tuvvvkHU+xi02rafhPIpZdeWmF6Srd1aiE1/+kyZ82aFXB533//vS9obJd//vOfVdrYGOV93Pb9yixkZK9jj1988cWeAQMGBGxrnx/egJIdv27hNxubvK939NFHB9xv3AJYf/zjH13b+K9j22+DhZTDzf8YshD21VdfXeFix97pp59eZcpnC5JY0DAQC+V623bt2tUJogTjv5/Yeq4uHBTK51vlYzNYKNEbgrFAa3Wfh/7b2EJrOTk5AZdpUxd629pnvFuwtqWF0ZrT+vNnY4NN9e59zvHHH1/teBGpdWQqT4sb6DuufYeoPJXt4MGDA34W2BTG3nHbxrPK4WUvG4+9y7PPjmAsfOYfcnvjjTeCtgcAAADQNOydNwIAAAAAELEpOs866yxnuhp/Ng2OTUnmZVPmBOKd8sa0b9++1n3yLsemCbPphBqzhx9+2JkmrD5deOGFvus25WQo/vjHPwZdVxdffLHvuk2F6eaxxx5zpt30TgV56623qjZsmj+bpsnYFHc2vVIwNlXpXXfd5bv9r3/9S7Vl68CmTg1lWTYd2kcffeS77TYNY1PaF80111xTZVrByv72t7/5pty65ZZbXKeA9GdTWto0d96pAitPP2fT7toUVv7L958S1J9N3WXThzWUv/zlL77rTzzxRLXb9LbbbnOm0jOvv/667/gI5Pjjj9fRRx8d8HGbctKm5DM2xZl3Ks5IqK/xOdJsfwk25ZtNMWljiLHp4vyPaa/nnnvOd/3KK690poQLxKZ4u+yyyypMJVeZPd87nbWNp94pmL1sijfv9MQ27aZ3qky3tjbVo3fKSfusrTwtpi3nySefdK7bcWXT8Fb+nK7soYcecqblC3U8tamZbYrJxsCm3LNpYP0v9p7feustZ9pgY9Mkf/DBB860gDZlXyjHu60T77EX7LPXOx7adIA2dWYwNmViTT8n7djz/7yrzKaptul0g31e2/TB/lPS2jHgHafc2HTVNp2rsTEs2HTLLUFzXn829bZNM2lsXHzppZeqHS8aah3ZWBxo6ksb62xc8mfHcKDPgm7dujnTaRrLvPlPR+1lUzR7x8O0tDTnO28wNiWpTVFeH99NAQAAADQehNEAAAAAIALWrl3rCw2Zc88917Xdeeed57v+6quvBgxkdO/evcIJsY0bN9aqX97l5OTkOCebG6v09HRfSKcm8vLynPVuoZ3f//73zkk8CxF5LxZ68ZozZ061y7OTcxaCCWbYsGG+694TlZV9+umnvuvWD2+YoaY+/vhj33U7mWgn9KpjgSf/IEdd+O/HwU4evvPOOyooKHCu20lMC1421X3R68wzz6zR9gmlfXXb56uvvqoQ5Kku3GbBnK5duyrSNmzY4DueLESSlZUV0rE1duxYX3hx3rx5QdtXPnlemR1T++23X7XHYjj4j88WziopKVFjZwEDC3MFYwGJE0880XV/NBb+mjVrlmswN5BLL720QhgoNze3wuMW7vCGtG09Vj4mvv766wr7u/c9VNfW7b1a3zdv3uxct32xR48e1fa/c+fOvuPQ9lkLlgQT6jjQWNhxc+qpp+r666/3jeGV2br+3//+59te1r46dnx6g4MWaLGgYDC2zJoGfX71q18FDVeG8nntH2Bs165dhQB2oGCP/35f+RhpaZrz+vP/Hmd/YJKRkdFo11F1x+SQIUN81+2PPo455piQ269YsaLK4/bHHd73ZN+ZQ/kDg9GjRzt/jFAf300BAAAANA41/3MdAAAAAECN+QfL7MT1AQcc4Nru9NNPdwJTdhLHG2A74ogjXE/a2InyVatWac2aNU6VhAsuuMA5+WqVtkKtIHbGGWf4qhFYsOiNN95w7rNATKdOnRrNlt5///0DVoBys23bNqciilWX27VrV0jPqa4yi+nfv3/Q6jDek4leFqqpbNOmTRVOentPyNeGf5Usu27BtuqUz95UzvadurCAoFWfsSoYVkln+vTpzv5X2f/93/9VG8RsKvuisYpKQ4cODdpm69atWrx4se+2VQYJJXRo1c8CbR//wKSNAdWx17N2NpZEkv9+aftbKPul8VZj8r73fffdN2Db6tZ/KMdiuJx22mm6++67nTH/k08+0aBBg3TRRRc5J/jtPdVkLIsU209C2T8tpOUNi1au3GeVGr2VAC18EGz7+Y/tVlXIQmj2XFuGt+qOf3DMG/yw4IV/RTxvwKxPnz5O8NI/ZFa5rX9owy2M5r/f2udBqPutN4Bm+/q6deuCVjUKViku0qySk391UO97sFDhkiVL9N577zkVz2zbWKDbto1VMav8Gfjzzz/7QoQW/vrtb38b0uv7VyKr7rOoNuutPsYI/33cjpFQAnH+FW7t+bZOaxs4b+qa8/qz7zv18T0uEuvIW0kt2B98eFnF1+r64N/evxKo21hq34NCHUu978H+KMHGFG8lTgAAAABNE2E0AAAAAIgA/yk3/aufuZ3gsSoCVu3MO7WnWxjNwjAW8LEppuxEkIWvLOxiFztRPHz4cGcqMHt83LhxAU9S3XHHHfrmm280depU52SWTcdlF9OrVy/nufb6Vg0nNTVVDaUmU91ZQM/e++rVq2v0GqGE1my6oer4T/3mVhHJwmheCQkJTmWd2rKp8rwsuOhffS8UdsKvLuyEpQXGvFPbWXW0ymE0C0J9++23vnVjgcumvC96j9PqTtZadTB/Tz/9dI1fp/L2sdCfW/Wt6ipeRZr/fmnXbfq/mqpu36zpseidyjESLHBsU17eeOONzr5swZ7bb7/dudi+a4EuC0OdfPLJTsC1MajN/uS/P1a+be1CCd1ZG2vrnUbVLRTsHxzzr25mYQVvJTZvmw4dOjjhPwt1+re1Kpne8JMFHKyyYLD99pdffnEu9b3fNvZpW+27gk2Lat8h7GIVCC34Yp+Ptj5t+j4LWgZabxZkC8fxXpv1Vh9jhP8+HUqlPO/Upl5FRUXOumvoz6yG0pzXn/93Obdqr41pHVV3LPh/nwnluPFv73bc+I8JNu4GmrK+ujGBMBoAAADQtDW+P8UEAAAAgGZmxowZvhPtdqL3nHPOCdreP6z27rvvBgxJ2dR3VqnEpuvxP2FjJ6asYsOf/vQnHXLIIU4w4j//+Y/rMmxKHKsWYyG2ffbZp8JjNvWOhejOP/98pzKVVTvJz89XQwi10pux9esNotnJuZtuusmp5mLvx06UW/UdC4jYxb9STqApUf3VR3UO/+0ZytRFwdS12pO3ilFd+Fc6e/PNN6sE8KyKknfdWmUo/0o0TXFfDHV/rI9KXJXXpe2/Xt7prKrTECdzw/HeK2uMlXL82bSGFsK06oH+oSwLD9t4dNtttzlj8/jx4zV37lw1tNrsT5U/m/z3z5rsd8GWaaySqAWkzA8//OCrxGPBVW8Qwr8ykTeYVrmtfTYaC1f5h5Aiud/W5LOsMbDqYtdee63v9uOPP+5bj419vdXHGFGbfbpyu1CrszZHzXn91dd3uUiso5ocC/Vx3ERiTAAAAADQ+BFGAwAAAIAws+pmXhaAsooGdrIn0OWkk06qUM3lnXfeCbhsW9Y//vEPp7LCF1984UxNaSfl/U/c2hQ5p5xyijPdlhs7KW/BiaVLlzqhCKtqcuaZZ6pLly4V+vHII484y66PEFAowa/a+O6775zQgbHwggUBrd8TJkxw1pWdwPMPhjTESU5vqKLyScja8D8haVOqeUN2NbnUlU0r1bdvX+e67Yf/+9//Kjxu1dKqm6KzIffFcPHfNjZtX222zcsvv1xhmf4nvG09hMI7fV59qu749X/vNp7V5r1Xnj6wKbLAsE0vaRV0bBy3KZit2pT/GGTVDO0Y8o5bDaU2+5P/WFZ5/6zJfhdsmSYmJsZZl94A7eTJk4NOu+kNpvm39a+S5jZFZ+X91sah2uy3gZbdlFmg0suqsFaentV/vdm0q7VZb/fcc48ao9rs05Xbue3TjeU7U7g15/VXX9/lGss6qk/+Y8Jf//rXWo0J/tXfAAAAADRNhNEAAAAAIIysgohVi6qvKT4DsfCZVdi59957nXDD1q1bnak+99tvP18bq8Szbt26oMsZMmSIJk6cqNdff92ZXtFOOl9yySW+xy3c5TYFV3VTU4ajaoKbSZMm+a5bmMUqD1U3pWekZWZm+q4XFhZWmc6xtsuyqQAbin+1P5s+1mvevHn6+eeffVXqfvWrX4W8zNrui42F/7bZvn17lSkNa8N/qrpQp6Fds2ZNtW3q+/htLPtlY5GRkaFTTz3VOSlv1bosnGYVpux+Y6HKK664okH7GOr+5N/O23+3/dP2u1DCrhYS8d9HKy/Tyz/k5Q2heQNmNtWp/3TH1tZb3cfbNlBwzR/7rTurRhnsc9N/vS1fvrzJBqfqa8z1Xz82bXrloFBNx9twfmcKt+a8/vz3e6ve2pjWUUNjLAUAAABgCKMBAAAAQBh98MEHTiUR7wk0q4ATymXEiBG+ZXzzzTdauXJljV7XwmlWDc1OwHfs2NEXjLPp4WrCqpy88MILuvzyy333/fe//63Szv9EmAXhqhOuaenWr1/vuz548OBq29s0eg1xks6/4oOFB2vL9hWvmm7b+uRf8ez999/3Ve3wD6addtppSkxMrPVrhLovNqYAR/fu3X23P//88zov09aBl03FWx0LA1lorzr1ffyOGTPGd33+/PlOmLCxaojpPi1wZVMf+u+/tp4syNNQbH8KJTzmv99ZlTd/Fn62KmbeSkGhjPM21bR3vLDn+geo/fkHyCyEZs+ZNWtWlce869c7/lduaxV7Ro4cWe14ap+7FhZG1ap5/pX9vONSQkKCc92mRZ02bVqzWW3Dhg3zXf/+++9Dmtrav8qhPb/yGOM/3ubk5FR73FlAyTvdbFPTlNZfTT8L/D/n6vI9LhzrqKE1lu+mAAAAABoWYTQAAAAAiNAUnccff7xzIj+Ui5049059aCfaXn311Vq9fnp6ug488EDfbavIUxvW92DL6NWrl+/6nDlzgi6roKDACemFg/9J8uqmnbPgWkOFmY455hjfdavuVdvpMv23i1WFC1fIrzr77LOP78SsBT+8U4ZaVbNQp+isr32xMTnuuOOqTFVVF97pB42NEYsWLQra3k6QhxIEq+/j15Y3cODACu+9sfIPSBYXF0f0tceOHau2bdsG3Z8j1T/bT/ynsnRjFf4sbOq2P3qnmzvggAN8tytPM+vmpZde8l0fNWpUhend/FlA26orGquS+OGHH/qqIlXuh39AzdvWu+4OOuggxcbGur6GPWZT6nrDdH//+9+r7X9L8OOPP1a47T9tsjf8fvjhh/tuP/bYY2ou7PuTN2hn1S0/+uijoO1tjPf/3ue/XrxsP/Ye9/Y9xaZSD+att95SU9WU1l9Nx1r/73FvvPGGsrOz1VjWUWOY2tc7ztq06zYGAwAAAGh5CKMBAAAAQJjYSaVPPvnEdSrDUPi3rzxVZyjVi9ym/fGfDsiqvtgJ97oswz9E4GUnnYKdlLvrrrtqfdKuOr179/Zd9w9NVGaVJ6zCVkNVvrn++ut9wTmrIvPwww/Xajm23r2hCztBaYGvUCuoWKU8qypSX/zDZlYRzarOefebrl276pBDDgn43PrcFxuTm266yVcpysJjNo1uqDZu3FjlvkGDBlWoxmL7UaAp8Sw0dvPNN4f0WuE4fm+99Vbf9b/97W/64osvVJf3Hi7t2rXzXa9uGuNQhTq+2fHnv9+77c/h6F8gN954o7PfBPLb3/7WV8XMKv/5hy29/KcbtaCtd5peNxYUe/bZZ323r7zyyoBt7TjKyspyrts+f//99weddtMbUKvc1i245mWBEDumvG6//fYaBXwbezi2Nizw9+STT1YIAvkHDt2Od5siPJQgYkMc7zVl4cQzzjijwjGwa9eugO1tn7dqf8Y+4y+77LJqx9xg68pCog899JCaqqa0/mo61lr14R49ejjXbRy/6KKLQp42NBLrqCFZYNX/O6GN7aF+ftmYXR/TmgMAAABoeITRAAAAACBM/vWvf/lOTNkJXLcT96GG0ayygP+0PE888YQzNdbTTz+tDRs2uD7fTmbZCWLv9GR2Mt+qFXjZ87p16+YEZmxqIDcWcPr000+dAIrXscce63pi0KpjeU/KnXXWWVWCTlbB4pZbbtGf//xnXxWI+mbr2DtdkU2zZmGc/Pz8Kie+Tz31VKf6RKAqPOHWr18/Z7173Xbbbc60fd4pXSubOXOmLrzwQmcqv8psX7CKRMaCH7YtggV/bF964IEHnOpV/vtUXZ155pnOVLTGXv8vf/mL77Gzzz67ytRu/upzX2xM7Jj4/e9/77ttYTTbjoGqlVlI0tbd+eefX2UKRC/bdv7TX1nbygHEzZs366STTnKqnMXHx1fbz3Acv3Yi2luxxcZBOzYtdOkNM1Vmr2uV9Ow5dixEytChQ33XraKfhTTr6vTTT3fe79tvvx3w/Vqo0o4Z7+tZJcw+ffoE7V84KyTZfmL7ywknnFDlM8XCohZUs2lyve677z7XCmP2ueWdatPem33m2HTRlVklx6OPPtpXgcj2d9vvgvEPnc2bN8/5aRX4bOrjyiz86v0s8LatvAw3NgZ5p/i0z1ALwFmFtED7hQXDbb1Y5TY7NuqThWzsPXgvkWb7gU2v7F8ZzY5N7zhfeX1fcMEFvtsXX3yxE6oJFJy3fcoC4yeffLKzzzVmd999t+8z1qpw2T5deUpdC9DYZ7F/mPHqq6+uUHXSn30metlnpQX4KrMqubZebSx2W+dNRVNZf/5j7TvvvFNtJVMb/ywY5j02Lcht7y1QxdKVK1c6318q/3FJuNZRQ3vwwQed0LKxIJpNj2zrNVCA3tpYcH3AgAF68803I9xbAAAAAOHgXpceAAAAAFBn/tPoWPjJfwqgUFgwwUIi3nCOLc+mEfOyygh2Iuqaa65xgiRDhgxRRkaGc3LfpqD87rvvKgQhfve73zmBn8rTrtmJPLvYtEfDhg1zKhpY2MQCLRZuWrFiRYUQ1W9+85sqfbWTcVZ9wkIYxgI1doJs/PjxTp8sAGaVsuz1Onfu7PT7jjvuUH2zk1jnnXee72Tfo48+qtdee805CdahQwfnZKD1w4IFrVu3dsIDwarxhPtEnZ209E55aNVnnn/+eWfKpp49ezonOm29/fDDD75wiP9JSC/b7hbiscoaFhj65ZdfdOSRRzrb2t63rX97v1ZpwvaZUKZtrA2rKmInUL3T5/lPgRrKFJ31tS82NnaS2fY773hgP61ynL0/21/tBLSFyVatWuVsH2+lLP8qLf4srHXDDTf4psKz0Kuta7vf9nE7oWvTc1qFK9uPTjzxROcEbzDhOH4t/GrhKdsXrQKW7YM2Bv3hD39wqrt1797dCUBZUMBOvi9YsMAXTLLxMlJsqrXk5GTn2LH1b+EmCyxZtRpvyGDChAnOJVR2sv3jjz92LhaCsGPU9te0tDQn4GTb2oIS3pPytq4ef/xx12XZurAQprH1Z9ctLOUfCLRtYVMy18XEiROdcND//vc/Z9vbOrBtZAFZC5P5B2WtT5dcconrcmyb2nhkIRAbc2zfsX3TAmoWoDYWevNW9jG239pzqguMBKuAVpkdPxYu8a/MVnkaUTfWxo6nI444whlv7Ni0KpoWrLJpVW1Msv3C1sfChQud8da7HYNVXWuMbBzyhtX92fcGOybtu4d/pSf7bLJqcYE899xzzmfV559/7gR5HnnkEWe/ts8h+45i03nu2LFDy5YtcyrOeavwWZCvMbOKqxY4tKClBYatmmn//v01btw4533Z+po8eXKFz1Yb4/70pz8FXKYFL+37iR0HNjZa6M8CmXaM2GvYfmvjprnnnnuc6Wxt3GiKmsr6s0pn9ocBtu/aHyvsu+++zj5v3xW9LEDsP4ZY6Ng+O21sNvbZaxVMbbyzcdrGExsrrD82VgSaxjYc66ihWRDNPlPsjwasWqiNDb/+9a+d8X706NFOiNjGTgusWmDYwnd1ncocAAAAQCPjAQAAAADUu59++snOqPguX3zxRa2W8/jjj/uWkZaW5snPz3fuf+SRRyosP9glPj7ec++991ZZ9tq1az0JCQkhL+fQQw/1bNiwIWh/7XWCLaN///6eefPmeV566SXffRdccIHrskJp4yY3N9czYcKEoP3o2rWrZ8qUKZ6vvvrKd98hhxziurxQ2lTm/1rBlJaWem6//faQtkNMTIxn4cKFAZc1Z84cz4gRI0Lenj179vTMnj3bU5/eeOONKq+z7777Vvu8cOyLoajJtl2xYoWvbY8ePWr8Wk888YQnPT09pPcXFRXlOeGEEwIuq6yszHPTTTc57QIto2/fvp758+d77r77bt99dj1Sx69XXl6e58orr/TExsaG9N6TkpI8Dz74oOuybL1729n2qI71zdve+hzI888/74mOjg7Yp8rrrbrlHn/88SHvyx06dPC89957AftWXFzsOeyww4IuI5R14abyvmHjy4ABA4K+1nnnnecpKiqqdtm//PKLZ9iwYdW+/+HDh3uWLl0aUn9LSko8qampFZ7/9ttvB2x/3XXXVWh71FFHhbxutm7d6vn1r38d9Bjzv7Rp08bz8ssvuy7Lv11NvPjii77n2f5ZH/yPoZpcLr74Ys/OnTtD2kZ33nm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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 432, - "width": 613 + "height": 431, + "width": 1233 } }, "output_type": "display_data" @@ -812,7 +804,7 @@ " ]\n", ")\n", "\n", - "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "fig, ax = plt.subplots(figsize=(15, 4.5))\n", "ax.plot(\n", " N_GRID_BERN,\n", " assurance_bern_df[\"assurance\"],\n", @@ -843,7 +835,16 @@ " alpha=0.7,\n", " label=r\"Power at $\\mu - \\sigma$\",\n", ")\n", + "ax.axhline(\n", + " prior_prob_positive,\n", + " color=\"C0\",\n", + " linestyle=\"-.\",\n", + " alpha=0.6,\n", + " label=rf\"Assurance ceiling $P(\\delta>0)$ = {prior_prob_positive:.2f}\",\n", + ")\n", + "\n", "ax.set_xscale(\"log\")\n", + "ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{int(x):,}\"))\n", "ax.set_xlabel(\"Sample size per arm\")\n", "ax.set_ylabel(\"Probability of declaring uplift\")\n", "ax.set_title(\"Assurance vs frequentist power, Bernoulli outcome\")\n", @@ -881,17 +882,7 @@ }, { "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Posterior on conversion lift at $N=4000$')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" ] @@ -899,7 +890,7 @@ "metadata": { "image/png": { "height": 262, - "width": 527 + "width": 519 } }, "output_type": "display_data" @@ -931,7 +922,6 @@ " idata_bern_demo = pm.sample(\n", " draws=1000,\n", " tune=1000,\n", - " chains=2,\n", " target_accept=0.95,\n", " random_seed=RANDOM_SEED,\n", " progressbar=False,\n", @@ -941,7 +931,7 @@ "ax = plt.gcf().axes[0]\n", "ax.axvline(0.0, color=\"C2\", linestyle=\"--\", label=\"ref = 0.0\")\n", "ax.legend()\n", - "plt.title(r\"Posterior on conversion lift at $N=4000$\")" + "plt.title(r\"Posterior on conversion lift at $N=4000$\");" ] }, { @@ -998,15 +988,15 @@ }, { "data": { - "image/png": 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LzNBNW3rsBg8ebB5/SgNNPfZ14czjEAAAAIDrIZgl+GpqIViLiBATbh3NKZSjOY5b1Dhyad928t+bz5GmlnnYQ2UZ6k2Dsl/cNMOGu53MK/bZfauPAQMGmIqumgKnBx54wIRlGq6sXLnSDDV0lVaCadijNJCrHpRZwh8d8jlp0iQTUGnw9vvf/77G7QUGBsrrr79uNyizpcHba6+9dtrwSf1d+6RpWJadnW2eDP/973/XuL4OjfzrX/9qqtm0Usy2Kmzz5s0mKFMvvvhijUGZOvvss+Wee+4xl/Hmm29WCcu08k2DMqX7VD0oUx06dJAZM2bIDTfc4PA6AwAAAPB8CHbE+rfGH4K1jg4zP9vEhEl8dMXP1tafYRIWXPF5asWeo3LTzJ/qfbmTBieKPyEsAxohbSBvrxebBmkWe/furdP2tVJK6WX86le/srucNuvXQEkDKV3HXlimQx5tm/nXZty4cWaIZU20UszimmuusTubZ79+/azn9+3bVyXs++yzz6yh2qWXXlrrvmgIpmGZVujpNxSWbyssx8cySYI9V199tRmievJk0wtsAQAAgIYMwczPaiGY/i2/uLTRhmAaesXHVIRg8TZhWE0hmLOGdImTHm2iZGd6Tr1GVw3uUvNntKaKsAxohHr16mX3f7ZBk1Zg1YVWXykNuGz7mFUXGhpqKrB0KKNlHUchlyPao8weDZ5cXa76MVi7dq35qdVwOqzRWYcPH7aGZZZZQPX613bdNMzT46MzewIAAADZBcVyOLNAcotKJTI0SNrGhkt0eM1fAPtXCFbRBN82BNP+YBlNLASzVIBZwrD4mLqHYM7SAoinJ/SXCa+urNMEehGhQTJjQr8mOXFebQjLRCQlJUWef/55009Jz+tQsW7dusmECRNMU3StQKkvnXHv5ZdfNqGC9lIqKCgwQ8O00bkOk9MG5dqHCXBGbfdJHfJo2+erLo4fP25+asN7RywzblrWqUmLFi3cft3qegwyMjKkLjRcszhx4oQ1mHQUuDlzDAEAANC0AyGdwGzOyv2ycGu6lJadahseFBggF57Zxgxx0wqgphJIVA/BLP2/bEMwS7P8xhqCaX/sNrWEYPpTQ7DwkIafQbJPh1h59ZYBMm3OOpcCs4jQILOeru9v/D4s04Ds5ptvNsPIbD8Ur1mzxpy0z9JXX31lt6+RM7Rv0R/+8AfTQ8qWzvCn4ZmedOa9zz//3KUKHF+hJZnupC8eG9NO3R71dVZCrHkR8tXr68ucebF2Zo6Q6v3HGpIlPNMJDPQx5yzLpAi219ldxwcAAABN0+YDmfLAvPV2h8DpZ5+vNh02Jx0qpxVAvhxM6HvbnMKSyj5g/hOCWYZE+loI5ooR3VvLvGlDar0/Vv/cO2NCP5++P3qSX4dlOkueVo9pOKYz/j388MNmtsH8/HyZO3euzJw5U3bs2GH6Gmlwpsu4at68efK73/3OOmRL+zuNHTvWzBq4Z88eeemll2T58uWm2kxnMdy2bVuVZuSNgSdmhrz73XXmBaMpz9jhyyxDOXXooSPp6elV1vF1cXFx1v3W4ayuDMW0sFzXY8eOmfCttjCwrpVsAAAAaNyW7TriUiWPBhg6VE4reTTY8IUQzPqzyYdgFX9rjCGYKzT4WjB9pKzae1zmrEqWBVuqVjoGm0rHtqbSUXuUNZVKx7rw67Bs+vTp1r5FOnvgkCFDrP8bM2aMdO/eXR566CHZvn27PP300/LnP//Z5cuwnUHv448/rtJQfNCgQXLjjTfKtddea/536NAheeONN8xshv5OH5zuCMv8bcYOd9HhwTqTZnJysgl77PUtKy4ull9++cW6TmOgPcS0WlQf+z/++KOMGjXK5W307dvXBOpFRUUmdD/nnJoDWa0mXb9+vRv2GgAAAI2toszVIW9Kl9f1tALIHRU9VUIwaz+wqmGY6RXWhEIw0yDfz0IwV2gANqRrnDlpDz29H+QUlkpUWJA5Zv7cQ8+W34ZlWimmwx/VbbfdViUos3jwwQdl1qxZptrr2WefNZVn9mbfq0lWVpa16bl+mLY3895f/vIXE5apFStWEJYxY0eD0+pHrazUF9c333zTDCOuyUcffWQdwqzrNAZXXnmlPPHEE+a8znRZl7BMr+ujjz5qzr/11lt2w7JPPvnE2t8MAAAA/kHfQ+tQt7o0U1e63oPzNsg300fYreypKQSzVH7ZhmD6s6774QshmLXyyyYEszTFJwSrPw3GCMdq5rdh2aeffmo9P3XqVLtNwm+99VYTkukHXg3Xxo0b5/RlaNWJRW09z7p27Wo9X1hY6PT2mzJm7GhYV199tbRv314OHjwo//jHP+Tiiy+Wfv36VVkmNTXVOsRYm+3bexz5moEDB8r48eNNNalWmGlY/fjjj9tdXqvrtMpOq0Btq0I1IPv555/NxB3XX3+9DB8+vMp6WilqOT4AAADwH9rM35meULXZkZ4tbyzfJ62iwqwh2KkhkU0nBGtd+ZMQDL7Gb8OyZcuWmZ+RkZEyYMAAu8vZVp1obzFXwjLtS6a9jXSWwL1799pdTnuXWfTo0cPp7Td1zNjRcLSC8rXXXpPLL79csrOzTRD0+9//Xi644AIzbFkrIP/5z39a+3E99dRT5v7eWGjF6LnnnmsCrb/+9a+yYMEC+dWvfmWGV4aHh5teZBs3bpRvvvlGFi9eLFdddVWVsExpv0E9LjoUVZ8Xfvvb38oll1xiZtP96aefTMiok3hoyKhDNQEAAOAf3lm13y3b+fv8bdKYxDYLsc4IaQnBLKEYlWBobPw2LNOhlapbt261NvjWBuDV13HFHXfcYUIFrUD5+uuvTYWOvb5m2iT89ttvd/ky0tLSav2/BgKNFTN2NBwdNqyh0rRp0yQnJ8dUYOnJlt5n9f571113SWOiVXNaLaYVYTokW8MtPdkTExNz2t/OO+88efvtt2XKlClSUFAgTz75pDlZ6POKVp1pyE5YBgAA4B+0B5Q2TW9KLCGYdSikTQhmWyFGTzA0JX4ZlukHW634UAkJCbUu26JFC1N9lpuba4aduUr7Gq1du1YWLVpkhrbde++9pjpHq3C02kw/TC9dutSEDs8//7z07t3b5cvo2LGjNGXM2NFwJk+ebKortWefDltMSUmRsrIyEzbpJBj33XefqcZqjBITE01A9tlnn8kHH3xgzusMmVop1rx5czPBh/YyvOKKK2TEiBE1bkOrzbRyTAPx7777zjyvtG7dWoYNG2Z6D2qgpmEZAAAA/EPaibwqsws2phDMMkMkIRggElCunQH9zJEjR6yz+91www1mVrvatGnTxgw309n+Nm3a5PLl6Yx4s2fPNh+obYdcWlxzzTVm1k39YF0XrkznqoGfo4DQ1q5du8z+a5WMhge+gBk74G988XEIAAAAMcHYtkNZsmLPUVm555is2nu8wWeV1BDMWvlFCAY/kJaWZi0icjXzsMdvK8ssQkNDHS6vPYhUfn5+nS5PK8vef/99u33LtOpMAzmtKqtpuJcjjiredBimNiRvKpixAwAAAEBDKCsrl50Z2SYYW7HnmPy095hkFZR4PwSrNiukba8whkMC9eeXYZk28K5pxkp7LDNUNmvWzOXL+uijj2TSpElmG2eddZaZdW/kyJESHR1tQi4d/qU9n3Q45g8//GCCs7Zt27p0Ge5ITQEAAAAAVelArD1Hcs0Ml6v2HDM/j+c6/gzpDoEBIrOnDpSkuChTIUYIBniPX4ZlGlRZaONyR7RfmYqKinLpcrT/kTb/1qDszDPPNDMIav8ziy5dusjDDz9sqr50Nr0tW7aYHlAffvihS5cDAAAAAHBPOJZyPM9Ujmkwpj8zsiuKJ7zt4j7tZGSPivZBALzLbyvLtMG+NuN2NJPkiRMnrGGZq430tReaZd1HHnmkSlBmSxv+60mryj7++GNzmTqxAAAAAADAsw6ezDdDKit6jh2TAyfr1n7H3SYNTmzoXQD8ll+GZUr7gy1btkx2795tbZxdk+3bt1dZxxXbtm2znj/nnHNqXXbAgAEmLNOZBnfu3FnnZv8AAAAAAPsysgsqKscqq8f2H8tzy+EKCgyQsxJiZXDnlvLFhkOSVo/QrWebaBncpaVb9guA6/w2LBs+fLgJy7Tya926dXbDqaVLl1rPDxs2zKXLsA3gNJCrTXFxcY3rAQAAAADqTnuMacVYRVP+o6YHmTsEBIj0aR8rQ7rGyZAucTKwc0uJCqv4LHfpWe1lwqsrJa/I9ZkxI0KDZMaEfhKgFwCgQfhtKnPVVVfJk08+ac7PmjWrxrBMq7zefvttc7558+YyevRoly6jc+fO1vMazPXp08fustrcX+kTYlJSkkuXAwAAAACokJlfbGaptPQc2344222HplfbaGs4dl7nOImNCKlxuT4dYuXVWwbItDnrXArMNCjT9XR9AA0nUPyUNtUfMWKEOf/GG2/IypUrT1tmxowZ1qGU999/v4SEVH0inD17tgm39PTYY4+dtv6ll15q/TbgiSeekAMHDtS4L6+99pqsXbvWnB88eLDExcW54RoCAAAAQNOXU1gi3+/IkH98tU0uf2G5nP3XhXLHnHUy68fkegdlXVtHyi2DE+Wlm8+RdX8cK99MHyl/ufxMGX9mW7tBmcWI7q1l3rQh0qNNlNNDL3V5XQ9Aw/LbyjL13HPPmaGV+fn5Mn78eNOEX6vH9Hdtzq8hlurRo4c8+OCDLm+/V69eMnXqVHnzzTdNUHb22WfL9OnTTUinM3Kmpqaay3nvvffM8kFBQfKPf/zD7dcTAAAAAJqK/KJSWbf/hKzce9Q05t+YlimlZeVu2XZiXISpGrNUj8XHhNdre1ohtmD6SFm197jMWZUsC7akV9nX4MAAufDMtqaZv/YoY+gl4Bv8OizT8OqDDz6QSZMmSVZWlgnLqtOgbP78+SbcqouXXnrJ9EXTyzly5Ig8+uijNS6nM2VqOHf++efX6XIAAAAAoCkqLCmVX1JOWpvyr089KUWlZW7ZdofmzWSwJRzrGmd+dzcNwCzbzy4olvSsAskpLJWosCBpExMu0eG1V6gB8D6/DsvU5ZdfLhs3bjRVZhqKpaWlSWhoqHTr1k2uv/56uffeeyUiIqLO2w8LCzPVY9OmTTPDNletWmWqzAoLCyUmJkZ69uwpY8eOlTvuuEMSEhLcet0AAAAAoLEpLi0z1WIr9xw1fcfWJp+QwhL3hGPx0WHWqjH92allhFeruTQYIxwDfF9AeXm5e+pV4bM0AOzYsaM5r0M/XQnldu3aZWby1Bk6u3fv7sG9BMDjEAAA+CMdlrjlYGblbJXHZE3y8TrNIlmTlpGhJhgbXBmQaQ8yhjoCTUtaPTIPe/y+sgwAAAAA4D1lZeWm8X7FbJVH5ad9xyW7oMQt244JD5bzusTJ0Mphjz3ioyUw0HuVYwCaBsIyAAAAAIDH6GCm3Rk5leHYMVm195icyCt2y7YjQ4NkUOeWJhgb2rWV9G4XI0GEYwDqibAMAAAAAODWcGz/sTwzpNISkB3NKXTLtsNDAmVgUkvTlF+rx/p2iJXgoEC3bBsALAjLAAAAAAD1knYizzpbpQZkhzIL3HJEQ4MC5exOzU3VmFaP9esYK2HBQdxaADyKsAwAAAAA4JL0rILKhvwVM1amHs93yxEMDgyQfh01HKtoyH9OYgsJDyEcA+BdhGUAAAAAgFrpMErtNWapHtt7NNctR0zbi+lQSp2tUqvHzk1sIZFhfEwF0LB4FgIAAAAAVJGZVyyr9p0Kx3akZ7vlCAUEiPRuG2OGVGrl2KAuLSUmPISjD8CnEJYBTcTs2bNl6tSp5vy+ffskKSmpoXcJAAAAjUR2QbGsST4uK3ZX9BzbeihLysvds+0ebaJMMKYB2Xmd46RFZKh7NgwAHkJYBviQJUuWyOjRo51eftasWTJlyhSP7hMAAACanryiElmbfMIEYzpr5eYDmVJa5p50rHOrSGvlmM5a2To6zC3bBQBvISxD41OQJZJ1UKQoVyQ0UiSmvUh4TEPvlV/QYO6tt96SxMRESU5ObujdAQAAgJMKikvl55QTsqpytsr1qSeluNQ94VhCi2YmGBvarSIcaxfbjNsFQKNGWIbGQWvAk5eJrJ4psn2+SHnpqf8FBIn0vkxk4O0iSSMqGiE0AXfddZfcfffdtS6TkJDgtf0BAABA41FUUiYb0k5ae46tSzlh/uYObWPCrZVj+rNjywi3bBcAfAVhGXzfwfUin9wpcmRbzf/X4GzrZxWn1r1Frn5FpH1/aezi4+OlT58+Db0bAAAAaARKSstk88EsWbHnqAnHdIhlfrHNF8z10Coq1FSMWQIyHWYZ0ES+oAaAmhCWwbftWSwyd5JIsZNTU2ugNusSkYnviHQd4+m9AwAAABpEWVm5acK/am9F5djqfcclu7DELdtuHhEi53VuKUO7tjIBWff4KMIxAH6FsAy+XVHmSlBmocvrelO/ahIVZu5SVlZmJhD46quvZOXKlbJjxw7JzMyUyMhIM3Pm2LFj5Te/+Y106tTptHUfe+wxefzxx62/79+/v8Y3TOXumjIJAAAAp73P2pWRIyt2HzU9x37ad1xO5hW75ShFhwXLoM4tKyrHusZJ77YxEhhI5RgA/0VYBt+koYsOvXQ1KLPQ9T69S+SuFU2mh1l9/fWvf60SeFloYLZhwwZzevnll+Wdd96Rq6++ukH2EQAAAKfCsX1Hc81MlSYc23tMjuYUueXwNAsJkoEajmlT/q5xcmb7GAkOCuTQA0AlwjL4Jm3mb69HmbMytookLxfpPMJde9WolZSUSLt27UwQNmTIEOnSpYuEh4dLamqqrFixQl566SXJycmRm266SX7++Wfp3bu3dV2daOC6666TP/7xj/LZZ59J+/btZcGCBQ16fQAAAJqa1ON5Zkil6Tu295ikZxW6ZbuhwYFybmILa0P+sxKam78BAGpGWIb6+/w+kYx6BlvVHdvtnu3Mu0Ukrpu4VXxvkSteEE/LyMiQzZs329+N+Hhzctbtt98uf/nLXyQkJKTK38855xy58sor5b777pPBgwfLgQMH5B//+IfMmTPntMtq3ry5+V23weQDAAAA9XMoM986W6VWkB04me+WQxoSFCD9OzaXIdpzrEucnN2puYSHBHFzAYCTCMtQfxqUpa3xzSOZf8J3980BHRKpJ3s0+NJeYs7SvmS1SUhIkN///vcyffp0+fzzz03pP7McAQAAuM+R7EJTMabhmDbm12GW7hAUGCB9O8SaIZVaOTYgsYVEhPJRDwDqimdQwE9lZWXJsWPHJC8vz9qYPyIiwvq/ffv2maGaAAAAqJsTuUXy076KqjENyLRBvztoS17tM1bRc6yVnJvUQqLDq44eAADUHWEZ4KNcrRxzhs5i+dRTT8kXX3xhztfm6NGjhGUAAKDRyy4olsOZBZJbVCqRoUHSNjbcY8FSVkGxrN573FSPaUC2/XCWmbfKHXq1jZbBlT3HBneOk9gIwjEA8BTCMsBPfP3116ZJv1aSOSM/3z09MwAAALxNq+Y1sJqzcr8s3JoupWXlVYYsXnhmG5k0ONFUZtWn7URuYYmsSa4Ix1btOSabDmSKzUXVS5fWkRXDKru0ksFdWkpcVJh7NgwAcIiwDO5peO9OZaUiB39208YCRNqfLRIY5LvX1wt0uKXOcqlBWVRUlPzud7+TCy+8ULp27SqxsbESGhpqllu8eLFccMEF5rxlaCYAAEBjsvlApjwwb73sTK95yKMGZ19tOmxOPdpEydMT+kufDrFObbuguFTW7T9R0ZR/7zHZkHpSStyUjnVqGVExrLJbnKkgaxMT7pbtAgBcR1iG+vPEzJDzbhXZ+ln9t3PGlSIT3hJ/9+GHH8rJkyfN+Y8//ljGjRtX43InTpzw8p4BAAC4z7JdR2TanHWSV1Tq1PIaqE14daW8essAGdG99Wn/Lyopk/WpJ2XFnqMmIPsl5aQUlZa5ZV/bx4bLYFM5VjG0MqFFRe9YAEDDIyyDbxp4u3vCMt0OZMuWLeYotGzZ0m5QptauXVvr0WJ2TAAA4MsVZa4EZRa6vK43b9oQ0xds44HMisqxPcdk7f7jUlDsnnCsdXSYNRjTn4lxEby3AgAfRVgG35Q0QqR1b5Ej2+q+jfgzRJKGu3OvGq2SkhLzs7CwUMrKyiQwMPC0ZXSI5ttvv13rdsLDw63bAQAA8BXaPkKHXroalFnoeje8utJsJ89N4ViLiBBrMKY/u7aOIhwDgEaCsAy+SRutXv2KyKxLRIpzXV8/JFLkqpcrtgPp3r27OQq5ubny0UcfyYQJE6ocldLSUrn99tvl4MGDtR6tdu3amZ8ZGRmSnZ0t0dHRHF0AANDgtH+YvR5lztLZMusjOjxYzuscV9GUv2uc9GwTLYGBvBcFgMaIsAy+q31/kYnviMyd5FpgpkGZrqfrw9Bw7JFHHjEVYVOmTJH169fL2LFjJSYmxgzRfOGFF2TdunUybNgw+fHHH+0etaFDh5qfWp125513yn333SdxcadmkerWrRtHHAAAeN07q/Z7/TIjQ4NkYOeWFU35u7aSM9rHmJk2AQCNH2EZfFvXMSJTvxL55E7nhmTq0EutKCMoqyIhIUFefvllUz2Wn58vTz75pDnZuuGGG+TXv/61CdHsGTNmjAwePFhWrVol7733njnZYgZNAADgbdkFxbJgS7rHLycsOFAGJrWsGFrZNU76doiVkKDTW1sAABo/wjL4Pg2+7l4pkrxcZM1MkW1fipTblMkHBov0uqyimb/2KGPoZY2mTp0qPXv2lP/85z+mekxnx2zVqpX069fP/E+rz5YsWVLrTaG9zhYuXCj//ve/5YsvvpA9e/aYoZ2EZAAAwJtKSstkV0aObErLNDNglpaVu/0yQoMCpX+n5hXDKrvEmfNhwUFuvxwAgO8JKOdTbpOXlpYmHTt2NOdTU1NNlZGzdu3aZZrDBwcHW/teNbiCLJHsQyKFOSJhUSLR7UTCYxp6rwCP8cnHIQAA3g7GDmSacEx/bjuUJYUl7mnEX931AxLkqrM7yDmdWkizUMIxAGjKmYc9VJah8dFgjHAMAACgSQdjmw9kysY0zwZjNZk2qot0i2cSIwDwZ4RlAAAAABo8GNOfWw96NxirLjgwQNrEhDfY5QMAfANhGQAAAAC/C8ZqcuGZbSU6PKShdwMA0MAIywAAAAB4NBjToZQFxZ4LxlpGhsjx3OJ6b2fS4ES37A8AoHEjLAMAAABQ52Bs95Ec01vMW8FYu9hw6dMhVvpWnvR8q6hQufDZH2Rnek6dt9uzTbQM7tLSrfsKAGicCMsAAAAAOB2MWWakbKhgrHV0WI3LPj2hv0x4daXkFZW6fDkRoUEyY0I/CQgIcMNeAwAaO8IyAAAAAI0qGKuJLv/qLQNk2px1LgVmGpTpero+AACKsAwAAADwY7bBmA6l3OiFYKxtTLj0Tah7MGbPiO6tZd60IfLAvPVODcnUoZdaUUZQBgCwRVgGAAAA+GkwZmal9EIwpmHUWQnuDcbs0e0vmD5SVu09LnNWJcuCLelSWlZu/X9wYICZ9VKb+WuPMoZeAgCqIywDAAAAmqCGDMb6VoZjng7G7NEAbEjXOHPKLiiW9KwCySkslaiwIGkTEy7R4SFe3ycAQONBWAYAAAA0cv4cjDmiwRjhGADAFYRlAAAAQCMLxvYcyZWNaScbJBjrmxBjzsdHh3vs8gAAaEiEZQAAAICPB2NmRsq0kwRjAAB4AWEZAAAA4AMIxgAA8A2EZQAAAEADBmM6lFKHVHp6KGWbmDDp26E5QykBAHCAsAwAAADwYjCmP7cczPRCMKY9xprTYwwAABcRlgEAAAAeDMa2HsyS/OJSjwdjfWxmpaT5PgAAdUdYBgAAANRBaVm57M7IIRgDAKCJISwD0OglJSXJ/v37ZfLkyTJ79mzxNb6+fwAA54KxPUdyZGNaw1SMVQypjJX4mHBuLgAAPIywDPBBubm58u6778pnn30mGzZskKNHj0pwcLDEx8dLmzZtpF+/fnL++efLqFGjpF27dg29uwAANMlgbFNaRShGMAYAgH8hLAN8zOrVq+WGG26Q5OTkKn8vLCyUffv2mdOqVavk1VdfNcHZ4cOHpSnSMHDp0qUmEFyyZElD7w4AoIlqiGAsPjrM2luMijEAAHwPYRkanZyiHEnPS5e84jyJCImQNhFtJCo0SpqC3bt3y7hx4yQrK8v8fsUVV8h1110nPXr0kNDQUFNhppVm3377rXz//fcNvbs+o3qwCABATQjGAACAMwjL0CiUl5fLmsNrZO6OubI4ZbGUlp/6tjcoIEjGdBojE3tOlIFtB0pAQIA0Vo8++qg1KHvzzTdl6tSppy2jYdrvfvc7OXLkiMybN68B9hIAAPuyC4rlcGaB5BaVSmRokLSNDZfo8JAGD8a0z9gWL1SMmUqxBCrGAABozAjL4PO2Htsqjy5/VHaf3F3j/zU4+3b/t+bUrXk3eWL4E3JG3BnS2JSWlsqXX35pzp977rk1BmW2WrduLffcc4+X9g4AgNq/1Fq595jMWblfFm5NN0GVRVBggFx4ZhuZNDhRhnSJ88iXWg0ZjOlQSh1SSfN9AACaDsIy+LQVB1fI9O+nS35JvlPLa6A25Zsp8uzoZ2Vo+6HSmGilWF5enjnfrVu3em+vqKjIzLyokwSsX7/eDOGMjY2Vjh07ypAhQ0xftOHDh9v90KJDPd966y358ccfTV+0kJAQ6dy5s1x44YXy29/+1u7EAo899pg8/vjj1g9PJ0+elGeeeUY++ugjMyOkDic966yz5Ne//rXcfPPNp60/ZcoUc7kW2res+j4mJiZWGXrp7GyTus7LL78sixYtMud1IgW9Hrr+ZZddJtdff7106tSpyjq6jIaYejzWrl1resbp7dS8eXM544wz5PLLL5c777xToqKaxlBgAHCVBlMPzFsvO9Nz7AZZX206bE492kTJ0xP6m4CprgjGAACApxGWwacrylwJyix0eV1v9kWzG1WFmYZIFtu2bavXtjQcu+aaa0ywUz2Q09PPP/8s//3vf83/NSiqHg7dcsst8sknn1T5e0FBgWzcuNGcNHB6//33TcBUG92+Dhvds2dPle1rAKanTz/91GxHZ/r0tKeeekoeeeQRKS4urvJ3Dc30pJMIfPHFF6dNJnDppZeafa1Ow8cffvjBnF566SX56quvpFevXh6/HgDgS5btOiLT5qyTvCLnKrg0UJvw6kp59ZYBMqJ7a6eCsb1HcmSjTcXY1kNZTl9efSvGLEMq28SEe+zyAACA7yEsg0/SiiQdeulqUGah6+n6H1/xcaPpYdayZUtTMaUVUtrE/1//+pf8/ve/l8DAQJe2s3XrVhkxYoTk5FR8w3/11VfLxIkTpUuXLmao544dO0yVVPUwTOn/tVJKJw/Q46braeimFWUaMulMnTNmzJCUlBS59tprZcWKFTJgwAC7+6LVaxqYaeWVTlSglW0atul127lzp6k208qu559/3rrOE088YXqy6TBUreTSIamzZs2yGyw6429/+5v8+c9/Nue1Iuzuu++W0aNHS1xcnKl80/BQj0dN95WSkhLp27evmWxB96V9+/bm/qm3k66jfeP0Ol511VUmpAwP5wMVAP+gwZUrQZmFLq/rzZs2pEqFmSUY01BMwzFvBGOtdVZKgjEAAFANYRl8kjbzt9ejzFm6/tr0tabpf2Nx3333maBI/eEPfzAVXBpe6bDJ8847T7p27epwG5MmTTJBmYZs7777rgm8bOl2br31Vjl27JhERERU+d+zzz5rgjIdcqnDNy+++OIq/x88eLCpOtMwbsuWLTJ9+nRZtmyZ3X1Zs2aNvPfee3LjjTda/6aBkw531G1oKKgVbjokUwMp1aFDB3OKjIw0v+vPPn36SF1pEKZDQ5XOKvrdd99JQkJClWU0OHvwwQclLS3ttPU1qOvevftpf9fjOGHCBLntttvM0FQNIfV46+8A0NTplwY69LKuQZaud+97P8t9Y7rJ5oNZptcYwRgAAPAVhGWot8dWPCa7Tu5y65Hcn7nfLdv57ZLfSmJMorhT9+bd5bGhFeGLu2kvMK0M05kwlVYvvfjii+ak2rRpI+eff77p9aVDIKtXQi1YsEB++eUXa/BWPSizpVVVtrRyTKvG1L333ntaUGbRokUL+c9//iOXXHKJLF++XHbv3m23x5ruo21QZhEdHS2vvfaaCZzKysrklVdeMaGZJ+i+6mXosZo7d+5pQZmtmv5XU1Bma+zYsabqTIeU6omwDIA/0Gb+9nqUOSv5WJ48+OFG8QQqxgAAQH0QlqHeNCjbeMQzb3brK7Mw02f3rSZaDfbGG2+Yyqunn37aVEFp0GORnp4uH3zwgTlphZaGP7bVZvPnz68SvLlCh1geOnTInNeKqdqMHDnSen7lypV2w7LaZvQcNGiQnHnmmaZCTRvue4Ieu2+++cacHzVqlJx99tn13qb2fNOhm4WFhVVmJlVaKQcA/uCdVe75UstdwZjpLUaPMQAA4CaEZSKm/5L2TNKgQc+HhYWZD/8aGGhvo+pD1ZyljcJ1eJcr9AN99Qbj8D8XXXSROZ04ccLMRqm9u9atW2eGPGZmZppl9G86lFH/bpmZ0lJVpjM6av8zV+j2LHTYp7N0pkx7Bg6sfQisBmYalu3atcvM3ulqLzJHtJeYBlvVAz5X6W2gzxEa6h0/ftzuctr0HwCauuyCYlmwJb1BgzHtdaa9xmi+DwAAPMHvwzINyHRImyWAUHl5eabXkp5ef/11M8udNkf3hp49e3rlctA46JBHHcpomXVSq5m0B5j219IgTSvB/vSnP5n7qW1YYwnPXJGRkVGnfdTHiz3x8fG1rqvDSi29b/T6WH53F9vwqi7HRGm/s8cff9ypZfPz6zYhBQA0JoczC0wzfm8GY/rzLGalBAAAXuLXYZkOmdLqMf2wHxUVJQ8//LCpBNMPvDq8bebMmaZp96WXXmqCM13GFVpVs2nTJofLaX+opUuXmvOTJ0+u8/VB06dVjzq0UWdk1Moz9fHHH5v+X7azZtZlBlCdCdNCqxur9zSrSyDmaD80JPOWuhwTHQZrCco0MNfJF4YPH24q9/T5ICgoyPxPZ9rUGTcBwB/kemB2SoIxAADgS/w6LNOZ/DQoCw4OloULF1YZejZmzBjT2Puhhx6S7du3m/5R+oHYFc7M4qdDxFatWmXO69DPoUOHSmOjDe/dqbSsVLYc2+K27Z0Zd6YEBVaEGr54fetCZ1/s2LGjpKammoosndlS+2a1atXK/P/gwYMub9M2HNPhkPWZgdK2x5rup6NqNg2ytIrO3SzHo67HRANz1bx5c9ObzV4wqLcBAPiLiJBTX864w/u/HiyDu7Ss05caAAAAnuC3YZlWill6g+nsdTX1aNKhbrNmzZJt27bJs88+ayrPQkJC3Lof2qjd0ij8lltukcbIEzNDPrDkAfl2/7f13s74xPEy4/yKGR6bGq0u07BMWarKzjnnHDNDpfbe05k0XelbZtv8vnp4XJ/HWW1hmf5faTBdvV+ZOz40de7c2YRwGmb98MMPLq+v/dQs4XltFXS2/d4AoKnSauAlO47Ivxdsd9s2gwMDpE+HGIIyAADgU9z71WAj8umnnzqcsU8DiFtvvdWc1w/bnmi8//bbb1uDgcYalnnCxJ4T3bOdXu7Zjq/RisitW7ea8zExMdKyZUtz/vLLL7cu88wzz7i0TR1eaNnOK6+8IllZWfXez7feeqvWgGnz5s3m/NixY0/7f3h4uPlpO+ukq/QxfMkll5jzOtTZMgGCs0pKShz2ZVu/fr21OhQAmqoVu4/KtS+vkKmz18i2Q9lu2+6FZ7aV6HD3fhEJAABQX34blumsgpahkgMGDKh1dkoLrdhxpz179siKFSvMeZ3VUKtgUGFg24HSrXm3eh0OXf/cNuc2mkOak5Mj5513nnz55ZdSVlZmdzn933333SfZ2RUfVq644grrN/IaOlnuzy+88ILpvWePzupo25BewyntyWWZ4XLixImSm5trd329/BdffLHW6/T555/LvHnzaryud9xxhzXQmjZt2mnLWBry7927t169zfQ66WXoNvQ6paWl2V22+v+04s3y2Nf9qO7IkSMyadKkOu8bAPi6tcnH5cbXVslNr/8kP6dUzC7sTpMGuzZzMwAAgDf47TBMHVpp6ROmPcvs6dWr12nruLuqTNHYvyoNf54Y/oRM+WaK5Je4PsNgs+BmZv3G1v9k9erVpjqsQ4cOctVVV5mhkDqUMjo62vS308qoN9980zpxRGxs7GmN5efMmSODBg0ygdSNN94oH374oQmJtEG9NvHfvXu3fPvtt/LRRx+Z7SQlJVnX1R592tReT19//bWcccYZcuedd5r90L5dGpDppBdaZanVmRqw6QQV9px77rly0003maqu6667zlTBbdy4Uf71r3+Z7ah77rlHzjrrrNPW1f59Ogxa+5o98MADJpTS66t0OLSzQ0z79+9vmvTrrKE7d+6Uvn37msvUyTy0T5seV60O04kStGH/999/b11XK0u/+OILcyw1OP+///s/E0Zq8KZBt/Yy1GBRj4/2NAOApmJTWqbM+HaHGXbpKT3bRJteZQAAAL4moNyb09H5iIKCAmnWrJk5rzNdaiVPbXTWO62wGTx4sFs/EHft2tVUq+i+6AduDRLqorZKGXXo0CETnijtcZWQkOD0tnft2mWGommgaKmy8aYVB1fI9O+nuxSYaVD27OhnZWj7oY3ufqnVhXpfcIbeHu+//36NlZHr1q2Tq6++2trTzJ59+/ZVCcuUVptpQGYb5tqj+1u94uqxxx6zziCp/7vgggvM5dTk2muvNdVvNQXWGlD169evxoouDcqSk5Otv+t10B5tGjrPnj27xst68sknzSQdlqGVNdFArPpw61/96lcmtKuJhmszZswww7Qt17mmp1Rn9s+XH4cA/Mf2w1ny9MKdsnBrulPL61dSdXkjGREaJPOmDZE+HSq+BAEAAKgrzUQsvbJdzTzs8cvKMsvwNUsQ5ogO1dSwTD+8u3MYqCUE0FCjrkGZqq2BemOngdfsi2bLo8sfld0ndzs19FIrys6IO0MaG63SOnDggOl/tWjRIvNTq690RkkN0vR+qE39NUC68sorTdBUvSm+hQZouu7rr79uKsC0N5gGOlpJpVVr2p9Mq82qB2VKw1vtNfab3/xG3njjDdMYX5989DGgjxddR7d/8cUXy2WXXeYwTNPg7qmnnpJPPvnEBEZaFabXQYdh3nzzzXbX1cvS6i0NuXTCAV23tt5hjugEHVrd9tJLL5njq5MgFBUVmWOq+6kVfTfccMNp62klnzb4f+2110wFmq7Ttm1bGTlypKmq0yBaA0IAaMz2HMmRZxftki83HhRnvkaNjw6Te8d0k4QWzeTe936RvKJSl4KyV28ZQFAGAAB8ll9WlmnS2KlTJ3Nem+o7qqDRZXUdrQTTIWzuoEHBzJkzzflvvvlGLrzwwjpvy5Whho2tssxC76Zr09fK+9vfl8Upi6W0/NSb8uCAYBnTaYxp5q89yhrb0MumxrayzA+fXjzCVx6HAJqe1ON58tx3u+Tjn9OkzImn7JaRoXL3+V1Nr7HwkCDzt80HMuWBeetlZ3qOU0MvZ0zoR1AGAADchsoyN7HMsqe0SsQRy2x8lqGb9aXb0z5SSqtaapoJ0BWOhtrZDsNsrDQA06b/esopypGMvAzJLc6VyJBIiY+Il6hQxxWCAACgwqHMfHlx8W75YE2qlDiRksWEB8u0UV1l8tAkiQqrOjBBh1IumD5SVu09LnNWJcuCLelSarPN4MAAM+ulBmzao4wvtQAAgK/zy2GY2izdwpmhlZYZAZ0ZsumMzz77zDQVVzoMTfse1Yc7xuM2JhqMEY4BAOC6I9mF8vKSPfLOT/ulqMT+zMsWkaFBctvwznLbiC4S2yzE7nIagA3pGmdO2QXFkp5VIDmFpRIVFiRtYsIlOtz+ugAAAL7GbyvLWrVqJUePHnXYHF/7PFnCMnf1BrMd9qmz7QEAAHjSidwiefWHvfLWimTJL3bcXyw8JFAmD0ky1WQ69NIVGowRjgEAgMbML8My1bt3b9NkX3uQWXoB1WT79u1V1qmvjIwMWbBggTl/zjnnSJ8+feq9TQAAgJpkFRTLG8v2yRvL90lOof3ZgC1CgwLlpvM6mb5k8TGn2lYAAAD4E78Ny3Q2QA3LtGpMZ+s777zzalxu6dKl1vPDhg2r9+W+9957JpxTVJUBAABPyCsqkdkrkuW1H/bKybxih8trX7Hrz02Qe8d0lw7N3dOjFQAAoLEKFD911VVXWc/PmjWrxmXKysqsQyabN28uo0ePrvflWranlWw33XRTvbcH+OJsmDoLJjNhAoD3FRSXmiqykf/+Xv79zQ6HQVlggMg153SQ7x4cJU9ecxZBGQAAgD+HZTo75IgRI8z5N954Q1auXHnaMjNmzJBt27aZ8/fff7+EhFRtTjt79mzT0FZPGhA4smXLFvnll1/M+Ysvvlhat27tpmsDAAD8mTbrf2fVfjn/P0vkb19ulaM5jmf7vvSsdrLwtyPl6Qn9JTEu0iv7CQAA0Bj47TBM9dxzz5mhlfn5+TJ+/Hh55JFHTPWY/j537lx57bXXzHI9evSQBx98sN6X99Zbb1nPT548ud7bAwAA/q2ktEw++eWAPPfdLkk7ke/UOmN7t5EHxvWQM9rHeHz/AAAAGiO/DsvOPvts+eCDD2TSpEmSlZVlwrLqNCibP3++REdH1+uydEin9itTLVq0kMsuu6xe2wMAAP6rrKxcvtx0SJ79dqfsPVoxa7cjI7q3kgfH95T+HZt7fP8AAAAaM78Oy9Tll18uGzduNFVmGoqlpaVJaGiodOvWTa6//nq59957JSIiot6X891338mBAwfM+RtuuEHCwsLcsPcAAMCfaD/IhVvT5emFO2VHerZT6wxKaikPju8h53WJ8/j+AQAANAUB5XThbvI0AOzYsaM5n5qaKgkJCU6vu2vXLjN7p05I0L17dw/uJQAehwDs0bdrS3YeMSHZpgOZTh2ofh2by+/G95Dh3VqZ/qoAAABNUVo9Mg97/L6yDAB8Hd9pAP5txZ6jMmPhTlm3/4RTy/duF2NCsjG94gnJAAAA6oCwDLUKCgoylWWlpaWm71pgoN9OoAo0CH3s6cnyeATgPzQcm7Fwh6zYc8yp5bvFR5nG/Red2VYCA6kkAwAAqCvCMtQqPDxcCgsLTWVLTk6OxMQwcxbgTSdPnrSed0f/RAC+b/OBTBOSfb/jiFPLJ8ZFyPSx3eWKfh0kiJAMAACg3gjLUCsNxzIzK3qjHD582PyMioqiwgzwIA2nNaTWWXqPHTtVUaIz6QJounYczpZnvt0p32ypeL11pH1suPzmgu5y7YAECQmi8hsAAMBdCMtQq8jISGnWrJnk5+eboWA6o6c2CWY4GOA5+lir3qcsNjaWWXSBJmrvkRx57rtd8vmGg1LtoV+j1tFhcu/objJxUEcJC2Z4NgAAgLsRlqFWGox16tRJUlJSTGCm9EO89jED4B2tW7eWuLg4DjfQxKQez5Pnv9slH/9yQErLHKdkLSND5a5RXWXS4ERpFkpIBgAA4CmEZXBIm/onJiZKbm6uZGdnW6vMAHjuMRcaGmoqO3XYs54H0HQcziyQF7/fJR+sSZXiUschWXR4sEwb2UWmDOssUWG8dQMAAPA03nHB6Qoz/dCuJwAA4LqjOYXy8pI9MmfVfikqKXO4fGRokPxqeGe5fXgXiY0I4ZADAAB4CWEZAACAB53MK5LXftgrs1ckS16R48rssOBAmTw0yVSTxUWFcdsAAAB4GWEZAACAB2QXFMuby5Pl9WV7JbvQca/PkKAAuWlQJ7lndDeJjwnnNgEAAGgghGUAAABulFdUIm+v3C+vLN0jJ/OKHS4fFBgg1w9IkHvHdJOEFhHcFgAAAA2MsAwAAMANCopL5f3VKfLf7/eY/mSOBASIXNW/g9x/QXdJahXJbQAAAOAjCMsAAADqQZv1f7guVV5cvFsOZRY4tc4lfdvKb8f2kO5tojn2AAAAPoawDAAAoA5KSsvk0/UH5bnvdkrq8Xyn1hnbO15+O66HnNk+lmMOAADgowjLAAAAXFBWVi7zNx2SZxbtlL1Hcp1aZ0T3VvLAuB5ydqcWHGsAAAAfR1gGAADghPLycvl2a7o8/e1O2X4426ljNiippTwwvocM7hLHMQYAAGgkCMsAAAAchGQ/7DoqMxbukI1pmU4dq34JsfLg+J6moixAO/kDAACg0SAsAwAAsGPV3mMmJFuTfMKpY9SrbbQJybQ3GSEZAABA40RYBgAAUM3PKSdMSPbj7mNOHZuurSNN4/5L+rSTwEAqyQAAABozwjIAAIBKmw9kmp5ki7dnOHVMOrWMkOlju8uV/TtIECEZAABAk0BYBgAA/N7O9Gx55tud8vXmw04di3ax4fKbC7rLdQMSJCQo0O+PHwAAQFNCWAYAAPzWvqO58tyinfLZhoNSXu54+VZRYXLv6K4ycVAnCQ8J8sYuAgAAwMsIywAAgN9JO5EnL3y3Wz76OU1KyxynZC0iQuTOUV3l1iFJ0iyUkAwAAKApIywDAAB+Iz2rQP77/W55f3WKFJc6Dsmiw4Ll1yO7yNRhSRIdHuKVfQQAAEDDIiwDAABN3rGcQnll6R55e+V+KSwpc7h8RGiQ/GpYZ/n1iC4SG0FIBgAA4E8IywAAQJOVmVcsM5ftlTd/3Cd5RaUOlw8LDpRbhySaIZdxUWFe2UcAAAD4FsIyAADQ5GQXFMusH5NNUJZdUOJw+ZCgALlxUCe5Z3Q3aRMT7pV9BAAAgG8iLAMAAE1GflGpvL0y2Qy5PJFX7HD5oMAAue6cBLnvgm6S0CLCK/sIAAAA30ZYBgAAGr3CklJ5/6cUefH7PXI0p9Dh8gEBIlf2ay/3j+0hnVtFemUfAQAA0DgQlgEAgEaruLRMPlqXJi98t0sOZhY4tc7FfdrKb8f1kB5toj2+fwAAAGh8CMsAAECjU1pWLp+tPyDPLtolKcfznFpnTK94eWBcD+nTIdbj+wcAAIDGi7AMAAA0GmVl5fLV5kPyzLc7Zc+RXKfWGdYtTh4Y11MGJLbw+P4BAACg8SMsAwAAPq+8vFwWbcuQGQt3yPbD2U6tc25iC3lwfE8Z0jXO4/sHAACApoOwDAAA+HRItmzXUROSbUjLdGqdsxJiTUg2snsrCdBO/gAAAIALCMsAAIBP+mnvMZmxcKesTj7u1PK92kabnmTjzmhDSAYAAIA6IywDAAA+5ZeUE/L0tztNRZkzurSOlN+O7SGX9m0ngYFUkgEAAKB+CMsAAIBP2HIw0zTu195kzujYspncf0EPuap/ewkOCvT4/gEAAMA/EJYBAIAGtSs9W55ZtFO+2nTYqeXbxYbLvWO6yfUDOkpoMCEZAAAA3IuwDAAANIjko7ny3He75NP1B6S83PHyraLC5J7RXeXGQZ0kPCTIG7sIAAAAP0RYBgAAvOrAyXx54btd8uG6NCktc5ySNY8IkTtHdZVbhyRKRChvXQAAAOBZvOMEAABekZFVIP/9fre8vzpVikrLHC4fHRYst4/oIr8aniTR4SFe2UcAAACAsAwAAHjUsZxCefWHvfLWimQpLHEckjULCZKpw5LkjpFdpHlEKLcOAAAAvIqwDAAAeERmfrG8vmyvvLl8n+QWlTpcXpv13zI4Ue46v6vpTwYAAAA0BMIyAADgVjmFJTL7x33y2g97JaugxOHyIUEBMnFgJ7lndDdpGxvOrQEAAIAGRVgGAADcIr+oVN5ZtV9eXrpHjucWOVw+KDBArj2ng9w3prt0bBnBrQAAAACfQFgGAADqpbCkVOauTpUXv98tR7ILHS4fECByRb/2cv8F3aVL6yiOPgAAAHwKYRkAAKiT4tIy+d+6NHn+u11yMLPAqXUuOrOt/HZcD+nZNpqjDgAAAJ9EWAYAAFxSWlYun284IM8u2iX7j+U5tc7onq3lgXE9pW9CLEcbAAAAPo2wDAAAOKWsrFy+2XJYnv52p+zOyHFqnaFd4+TB8T1kQGJLjjIAAAAaBcIyAABQq/Lyclm8PUNmLNwpWw9lOXW0BiS2MCHZ0K6tOLoAAABoVAjLAACA3ZBs+e6jJiRbn3rSqaPUt0OsCclG9WgtAdrJHwAAAGhkCMsAAMBpVu87Lk8t3GF+OqNnm2h5YHwPGX9GG0IyAAAANGqEZQAAwEoryGYs3CHLdh116qh0aRUp08f1kMv6tpPAQCrJAAAA0PgRlgEAANl6MMs07l+0Ld2po5HQopncf0F3ufrsDhIcFMgRBAAAQJNBWAYAgB/bnZEtzyzaJfM3HnJq+bYx4XLvmG4y4dyOEhpMSAYAAICmh7AMAIBGLrugWA5nFkhuUalEhgZJ29hwiQ4PqXWd/cdy5bnvdsmnvxyQsnLHl9EqKlTuOr+b3HxeJwkPCXLfzgMAAAA+hrAMAIBGOlPlyr3HZM7K/bJwa7qU2iReQYEBcuGZbWTS4EQZ0iWuSsP9Ayfz5cXFu+TDtWlS4kRKFtssRKaN6iKThyRJZBhvGwAAAND08a4XAIBGZvOBTHlg3nrZmZ5T4/81OPtq02Fz6tEmSp6e0F/iY8Lkpe/3yHs/pUhRaZnDy4gKC5bbR3SWXw3vLDEOqtQAAACApoSwDACARmTZriMybc46ySsqdWp5DdSu+u+PosVlxaWOK8mahQTJlGFJcseILtIiMtQNewwAAAA0LoRlAAA0oooyV4IyC2eGW2qz/knnJcpd53eV1tFh9dhLAAAAoHFjGisRSUlJkd/97nfSu3dviYyMlJYtW8qgQYPkqaeekry8PLce8EWLFsmUKVOkW7du5rJiY2OlR48ect1118nLL78sOTk1D6kBAPg37VGmQy9dDcocCQ4MME37l/7+fPnz5WcQlAEAAMDvBZTru28/Nn/+fLn55pslMzOzxv/37NlTvvrqK+nSpUu9LufEiRMydepU+eyzz2pd7pdffpH+/fuLO6WlpUnHjh3N+dTUVElISHDr9gEAnrdiz1G5aeZPbtteYIDINeckyP0XdJeOLSPctl0AAADAmzyRefj1MMwNGzbIhAkTTPVYVFSUPPzwwzJ69GjJz8+XuXPnysyZM2XHjh1y6aWXypo1a8wydaFB3Lhx42TdunXmd93exIkTTXVZaWmp7N+/32z/o48+cvM1BAA0Fe+s2u+2bV3Rr73cP7a7dG1dt9c1AAAAoCnz67Bs+vTpJigLDg6WhQsXypAhQ6z/GzNmjHTv3l0eeugh2b59uzz99NPy5z//uU6Xc99995mgTC/nnXfekRtuuKHK/4cNGyY33XSTuQwNzwAAsJVdUCwLtqS7raLsiav7SDQzXAIAAAA18tueZVrJtWTJEnP+tttuqxKUWTz44IOmj5l69tlnpbi42OXLWb58ucyZM8ec/+Mf/3haUGYrICDABGoAANg6nFkgpU406XeGbiY9q4ADDAAAANjht2HZp59+aj2vvcRqEhgYKLfeequ155glXHPFiy++aH7qEE4N3wAAcNXRnEK3HrScQqqYAQAAAHv8NixbtmyZ+akzUg4YMMDucqNGjapSJeaKoqIia0P/iy++2NrzrKSkxPQp01k4dRkAAGqScixP/v7lVrntrbVuPUBRYUEccAAAAMAOvw3Ltm3bZn5qk/3ahj726tXrtHVcmUCgoKBiqIsO8zx8+LCpYmvevLkkJSVJYmKixMbGyiWXXCIrVqyo83UBADQdOkn1j7uPyu1vrZVRT30vry/fJ3lF7qsECw4MkDYx4W7bHgAAANDU+GWDLA2wjh49as47mlK0RYsWpvosNzfXTEHqiq1bt1a5zL59+1ov1/bvX3/9tSxYsEBmzJhhJh2oyzSptTl06JDL2wQAeFdeUYl88ssBeWtFsuxMz/HY5Vx4Zlua+wMAAAC18MuwLDs723reMjSyNpawLCfHtQ8vx48ft55//PHHpbCwUC677DJ57LHHpE+fPpKZmSn/+9//5A9/+INkZWXJAw88ID179jRDNl3RsWNHl5YHAPiO1ON58s6q/TJ3Tapk5rs+kYyrJg1O9PhlAAAAAI2ZX4ZllqGRKjQ01OHyYWFh5md+fr5Ll6MBm4UGZZdffrmZWEAnDlDx8fFy1113mYoz7Y1WVlYmDz30kFx00UVmZkwAQNMdarlq73GZvWKffLs13cxQ6Q0920TL4C4tvXNhAAAAQCPll2FZePipXi3ONNjXoEs1a9aszpej/vOf/1iDMlvDhw+Xa665Rj766CPZvHmzOWmA5ixHw0N1GOagQYNc2HMAgCcUFJfKp78ckNkrkmX74VNVzo6EBAXIZWe1lxHdW8kfP91cpx5mEaFBMmNCP76MAQAAABzwy7AsOjraet6ZoZWWCjFnhmzau5zOnTubIZb2XHjhhSYsU2vWrHEpLHPUdw0A0LAOnsyXOav2y/urU+RknvNDLVtFhcmkwZ3kpvM6SXx0xRcwraPDZNqcdS4FZhqUvXrLAOnTIbZO+w8AAAD4E78My7Tiq1WrVqbZvqPm+CdOnLCGZa72BrNd3lGgZbtsRkaGS5cDAPDNoZZrkk+YoZYLtqRLqQtjLfslxMrUYZ3lkr7tJDS4akXyiO6tZd60IfLAvPVOTQSgQy+1ooygDAAAAHCOX4Zlqnfv3rJs2TLZvXu3lJSUSHBwzYdi+/btVdZxxZlnnmk9X1paewWA7f/t7QsAoHEMtfx8w0GZ/WOybD2U5fR6wYEBJhybMixJzunUotZlNfhaMH2k6Xs2Z1XyaWGcbktnvdRm/tqjjD6YAAAAgPP8NpXRPmEalmnV2Lp16+S8886rcbmlS5dazw8bNsyly0hMTJROnTpJSkqK7Nmzp9Zlbf/foUMHly4HANDwDmcWmFkt31udIsdzHffDtIiLDJWbz+skNw9OlDYxVXtd1kYDsCFd48wpu6BY0rMKJKewVKLCgsx2osND6nhNAAAA4BcKskSyDooU5YqERorEtBcJj2novfIJfhuWXXXVVfLkk0+a87NmzaoxLNPZKd9++21zvnnz5jJ69GiXL+faa6+VZ555RtLT02XFihUydOjQGpf7+OOPredHjBjh8uUAABpmqOXPKSdk1o/J8s3mw1LiwlDLPh1iZOrQznLpWe0kPCSoXvuhwRjhGAAAABwqLxdJXiayeqbI9vki5Taj4AKCRHpfJjLwdpGkEfrtrN8e0NOnZvQTOjukJZR64403ZOXKlactM2PGDNm2bZs5f//990tISNVv6WfPnm2+2dfTY489VuPlTJ8+3Tor5m9+8xtr/zNb77zzjixZssScv/TSS2nYDwA+rrCkVP63Lk2uePFHufbllfLlxkNOBWVBgQEmHPvoziHyxb3D5doBCfUOygAAAACnHFwv8tIQkbcuF9n2edWgTOnvWz+r+L8up8v7Kb+tLFPPPfecGVqZn58v48ePl0ceecRUj+nvc+fOlddee80s16NHD3nwwQfrdBk6DPOvf/2rPPTQQ2a4p4Z0er5Pnz6SmZlpKspeeeUVs2xMTIypQgMA+KaMrAJ556cUee+n/XI0x/mhli0iQsyMltpDrF1sM4/uIwAAAHCaPYtF5k4SKT69gKdGR7aJzLpEZOI7Il3H+N0B9euw7Oyzz5YPPvhAJk2aJFlZWSYsq06Dsvnz50t0dHSdL+f3v/+9HD9+XP71r3/J1q1bZcqUKactEx8fL59++ql07969zpcDAPCMX1J0Vstkme9kBZlF73YxMnVYklzRrz0VZAAAAGgYWiHmSlBmocvrelO/EmnfX/yJX4dl6vLLL5eNGzeaKjMNxdLS0iQ0NFS6desm119/vdx7770SERFR78vR/mhXXHGFvPzyy2ZigUOHDpnhmRrG6d/vu+8+iY2Ndct1AgDUX1FJmXy9+ZC8+WOybEg96fR6gQFiZqKcMjRJBnVmJkoAAAA0cI+yT+50PSiz0PU+vUvkrhV+1cMsoFy7E6NJ0wCwY8eO5nxqaio90QCgFkeyC+W9n1LknZ/2m/POah4RIhMHdpJbhiRKh+YMtQQAAIAP2PdDRQ+y+pr8pUhn35yM0BOZh99XlgEAoDamnZTZPyabZv1FpWVOH5SebaLNUMsr+3eQZqE06wcAAIAPWfO6+7bT2TfDMk8gLAMA+K3i0jL5ZvNhmfXjPvk5xbWhlmN7t5Epw5JkSJc4MysyAAAA0ODKykQyU0SO7BQ5vEFk6+fu2e62L0QKskTCY8QfEJYBAPzOsZxCeX91isxZtV/Ss5wfahkTHiwTB3WSWwYnSseW9e9nCQAAANRJSaHIsT0iR3dUBGOWn8d2iZQUuP+glpeKZB8iLAMAoKnZfCDTzGr5+YaDpoG/s7rHR5kqsqvP7iARoXzPBAAAAC8pzLYJw3aIHN1Z8fNEckWA5U2FOeIveMcPAGjSSkrLZOHWdDPUck3yCafX05GVF/SKlylDO8uwbgy1BAAAgIfovIu5RyrDsGqVYtkHfeewh0WJvyAsAwA0Scdzi2TumhSZs3K/HMp0vhQ9OixYJgzsKLcOSZTEuEiP7iMAAAD8tJ9Y9UqxAuf75zaIwGCR6HbiLwjLAABNyrZDWWZWy0/XH5BCF4ZadmkdKVOHJsk15yRIZBgvjwAAAKijkiKR43uqhmEajh3dLVKS3zgPa6/L/KZfmeLTAACgSQy1XLQtwwy1/GnfcZfWHd2ztUwZ1llGdGslgTrNJQAAAOBsPzEThtkMm9Sfx/d5v59YUKhIXHeR1j1EWvU89TP7sMi719Z/+wNvF39CWAYAaLRO5hXJB2tS5e2V++XASee/pYsKC5brBiTI5KFJ0rkVQy0BAABQWz+xo6cPm9SfWQe8f9jCYkRa9RBp3bPqzxZJIoFBpy/f5kyR1r1Fjmyr+2XGnyGSNFz8CWEZAKDR2XE428xq+ckvaVJQ7PxQSw3GJg9JlGsHJEh0eIhH9xEAAACNrZ9YatVhk5ZKsXznJ4lym6g2NmGYTaVYdNuKmaicpcte/YrIrEtEinNd34+QSJGrXnbtMpsAwjIAQKNQWlYui7dXDLVcseeYS+uO7NHa9CMb1aM1Qy0BAADE3/uJ7a026+QOkWO7RYrzvLwzASItEquGYSYc6y7SrIX7LqZ9f5GJ74jMneRaYBYSWbGeru9nCMsAAD4tM79YPlybKm+tTJbU484PtYwIDTJDLW8dkiTd4v1nmmsAQDUFWSJZB0WKckVCI0Vi2vtVk2rAb9XYT2xnRVDWIP3Eup0+fFL/FtLMO/vQdYzI1K9EPrnTuSGZ8WdUVJT5YVCmCMsAAD5pd0bFUMv/rTsg+cXOv6Hp1DLC9CK7/twEiWGoJQD4b4+h5GUiq2eKbJ9f9YNxQJBI78sqmlUnjfC7oUVAk+Jr/cRCo09vsK+hWPNEkSAfiF80+Lp7pUjycpE1M0W2fVn1+TEwuGLWy4H6/Djcr58ffeDWAgCgQllZuSzZqUMtk2XZrqMuHZbh3VrJlKFJMrpXvAQxqyUA+K+D62uvnNAPhls/qzhp02vt5eOnlRNAo+Fr/cQi409vsK8/o9v5fsCk+9d5RMVJK2+zD4kU5oiERVXsP5W3BmEZAKDBZRUUy0dr08xQy/3HnO8V0SwkSK45p4MJybq3ifboPgIAGoE9i13ryaOBmja91p48OkQJQMPytX5izTtVC8UqK8bc2U+sIWkwRjhWI8IyAECD2XMkR95ekSwfrUuT3CLnh1omtGgmk4ckyYRzO0psBLNaAgAqK8pcbV6tdHldT3v5UGEGeIdWMmmVmO2wSf15Yp9IWYl3b4XAkIreYVUa7Ov57t7rJwafQ1gGAPD6UMsfdh0xQy2X7jzi0rpDusTJlGFJMrZ3G4ZaAgCq9i3SoZeuBmUWut6nd4nctcL3h1ABjYn2E6s+bFJ/ZqU1TD8xDcCqV4q1SPKNfmLwKdwjAABekVNYIv9blyZvrUiWvUed/zATFhxohlpq0/5ebZm9DABQA23m78zsbrXJ2FrR9Fr7+ABwrZ+Yhl+2wyYtlWL5xxu2n5gJxSorxnQmXMJwOImwDADgUclHc00vsg/XppnAzFntY8Pl1qFJcsO5HaVFZKhH9xEA0EiVlVZUriyb4Z7tLXxUZMi9IqFRFc2u9Wf184GB7rksoLEpLa7oJ1a9UuzoLt/pJ6aVYxEtvbwvaIoIywAAbldeXi7Ldx81Qy2/35FhRsc4a1DnljJ1aJKMO6ONBAfxgQQA/PIDuQZguRkiOUcqf2aI5B6p/Gnzd11OXHiRceTQBpGPf137MiGRIqGRpwK0sOjKIM3yt2ibcC3y1P8J36B09sGsgyJFuRX3D6128rUG67pvpjKsWqWYBmW+0k9M/xYa4d19gV8hLAMAuE1uYYl8/MsBM9Ryd0aO0+uFBgfKVf3bm6GWZ7aP5RYBgKY4w52GXZagKyfdfhjWEMO2XO1vpifdb3fQ8M0EaZFVwzfr32zCt5oCN7N85bpUvvkm/dZQhwqvnimyfb5Iuc2kRgFBIr0vExl4u0jSCO8OE7T2E6vWaD8zVbxO77vWCjH6iaHhEZYBAOot9XieCcg+WJsq2QXOf+PYNiZcbhmSKDcO6iQtGWoJAI1LcUENgZft7zZ/LzjZ0Hvr++GbuxC++d4srTr5hL2eehqcbf2s4tS6t8jVr7h3Vlaf6yfWurJCzFIpRj8x+CbCMgBAnYdartxzTGatSJZF29JdGmp5bmILM6vlhWe2lRCGWgKA7yjKs1/xZarBLEMhj4gUZjX03qImhG++Y89ikbmTnA9DNVCbdYnIxHdEuo5xUz+x3e4NY52l/cRsh01aftJPDI0EYRkAwCX5RaXyyS8HZPaKfbIz3YWhlkGBcnm/9jJlaJL0TWCoJQB4TWFOtaDLXi+wIyJFzj+vw080ePgWbb9HnC8PO9WKMleCMgtdXteb+lXNFWY+10+sa9Vhk1opFtedfmJo9AjLAABOSTuRJ3NW7Ze5q1MlM7/Y6aMWHx0mkwYnyk3ndZJWUWEcbQCoLy3l1aouR83vLX/3+ix1HhQWUzGMKyq+8mebivPbv6xozl9fXS8QGf2oSFF2RchYVHkqtP2ZXRFY2PtbSb47rmnT5bHwzYkJFbwVvuljVIde1vV66no60cRlz/pQP7Hu1YZP9hRpkSQSFOL9/QG8gLAMAFDrUMuf9h2X2T8my8Kth6XMhaGWZ3dqbqrILu7TzjTwBwA4+HCtfb2sQVe6/eov/Vla2HQOZ3jzyvArXiSqdbWf1f4eEl7zNjoNFnnr8vrvy/DfiiQMqN82SkuqhWy5VcO3wspgzTZosy5Xw98I35wM39LFZ8K3w5vt9yhzlgZjsy8Rr4podfqwSf0Z08G7Ew8APoCwDABwmoLiUvls/QGZ9WOybD+c7fQRCgkKkMvOqpjVsn/H5hxZwB8VZIlkHaz4kK8fIGPai4THiF/Sxtr5J5yr/tJTaZE0Gc1a2lR/2QReWglmG37p/4ND6395OougNkevT0ARf4ZI0vD670tQsEiz5hUnd3AqfKv8e5Wgzc7fSgrcs19NlbvDN19k+olVa7CvoRj9xAArwjIAgNXBk/nyzqr98v7qFDmR5/xQSx1eefN5ncwpPsbOt/4AmnZVVPIykdUzRbbPr5jdzSIgSKT3ZSIDb68INBp7dUJZqUjecQcBWGVVWN5R7/cQ8pgAkchWNVR9VQvDTADWyvtDs/R+pbMIanP0ugx902qiq172zfunx8O36kNNawnfagrrCN98E/3EgHohLAMAP6dDLdfuP2GGWn6z5bCUujDW8qyEWJk6LEku6dtOwoKDPLqfAMR3m1hrbx57FT0anG39rOKklT8aaNTUtLohaXigwZaj5vf6U5crL5MmQYPM0wKwGoIwrQaLiBMJ9PHneb1f6SyCrjZV16BM1/O1+2VjDt+qDDXNrqUqjvCt3vT+a60Oo58Y4C6EZQDgx0Mtv9hwUGavSJYtB7OcXi84MEAu7tvOhGRnd2wuAb74LTwA79iz2LVgQgM1rfzRYKLrGM/uW2nx6UGXvSAs75imetIkBAZXhFy2ze9rqv7S33WopK/OJFhXer/SWQRrC3CrD73UijJ/CcoaTfhWfRIF22o32/CtpgkYqp1vKpVv9BMDvIqwDAD8zOHMAnn3p/3y3k8pcizX+f44cZGhZkbLm89LlLaxDLUE/J5WlLlawaN0eV1PAw1XA4qSwmoBWHrN1V8ahmmvsKYiKNRx9Zfld22W39QCMFfp/erulSLJy0XWzBTZ9mXVocEaKPayDA0e7ptDL8Xfw7cWFSd3BedVhpXmuB642VbKeTt8u/o1ke7j6CcGeBlhGQD4yVDLn1NOmiqyrzcdkhIXhlqe2T5Gpg7rLJed1U7CQ3x8CA4A7/Uo08qduvSGUrrep3eJ3LWi4oOnM9Vf+ntBpjQZweF2ArA2p4dh4bEEOq7SAKzziIqTTjqRfagi+NAZC6Pb+e+kE/5I++d5InyrMoOpTfiWe1Rk0Z8qnifrS4PdnhdzfwUaAGEZADRhhSWlMn/jIROSbUxz/kNmUGCAXHRmWzPUckBiC4ZaAqhKm/nXZ9ZBlbFV5Il2IiX5Tat3UI1VX9Wqv/T3sGgCMG/RYIxwDN4M3w6sqejTWF9aAcl9F2gQhGUA0ARlZOlQyxRzOppT6PR6LSJC5MZBnWTS4ERp37yZR/cRQCO25nX3bKcxBGWh0c4Nf9RTaGRD7y0AX6BDfN0Rlul2ADQIwjIAaELWp56U2T/uk/mbDklxqfPl/73aRsuvhnWWK/q3Z6glgNrpkDbtAdWYhcVWBF865NFe83vL30P44gCAi5JGVMz+W58KXJ18QnvqAWgQhGUA0MgVlZTJ15sPyawfk01Y5qzAAJHxZ1QMtRzUuSVDLQE4lndc5Oe3qzZL9xU6JKrW4Y+VQZj+LYRJSgB4uGfe1a9UzP5bl96OOqRbZ2ll8gmgwRCWAUAjdSS7UN5fnSLvrNovGdnOD7WMbRYiEwd1lFsGJ0pCiwiP7iOARk4bVGdsE9n5jcjOBSJpq0XKy7x04QEVs79Vr/iq3vtLf0a0EgkO9dJ+AYCTs7JOfMf1WYM1KNP1XJ0tGIBbEZYBQCOzKS1TZq3YJ19uOCRFpc5/aO3ZJlqmDEuSq/p3kGahzGoJwI7ifJF9y0R2LagIyDJTPXuo+t0kEt/r9FBMA7Ag3qoCaMS6jhGZ+lXF7MHODMnUoZdaUUZQBjQ43oEAQCNQXFom32w+bGa1XLf/hNPrafX+2N5tzFDLIV3iGGoJoGaZB06FY3uXeq/xfmCwyMX/YrY3AE2XBl93rxRJXi6yZmZFz0fboez6PKizXmozf+1RxtBLwCcQlgGADzuWUyhz16TKnJX75XBWgdPrRYcHy8SBHeXWIUnSsSVDLQFUU1YqcmBdRTimp/RNDXOI9ANieEzDXDYAeIsGYJ1HVJx0kpTsQyKFOSJhUSLR7XgeBHwQYRkA+KAtBzNl9o/J8tmGg6aBv7O6xUfJlKFJcvXZHSQyjKd4ADbyT4rsWVwRju3+ViTvWMMfHq2kAAB/ol8Q8CUB4PP4JAUAPqKktEwWbk03Idnq5OMufVk5pme8TB3WWYZ1Y6glAJvm/Ed3nRpembJSpKyk7odHhwolDhPpcZFI9/EiH0xyrgdPbb15dMgRAACAjyEsA4AGdiK3qHKoZbIczHRhqGVYsFx/rg61TJSkVpEe3UcAjURJocj+H08Nrzyxr37b09kmNRjrcaFIl9FVqyGufkVk1iWuzfJmO9ubNrGmNw8AAPBBhGUA0EC2HcqSt1Ykyye/HJBCF4ZadmkVaWa1vOacBIliqCWA7HSRXQtFdn4jsneJSFFO/Y5Ju34i3S+sqCBrf7ZIYKD9ptUT3xGZO8m1wEyDMl2P2d4AAICPIiwDABdlFxTL4cwCyS0qlcjQIGkbGy7R4SFOrVtaVi7f6lDLFftk1V7nh1qq83u2NkMtR3RrJYGBAdxugL8qKxM5tL6ickyHWB78pX7b0/Cqy/kV1WNaRRbTzvl1u44RmfqVyCd3OjckU4deakUZQRkAAPBhhGUA4ITy8nJZufeYmZVS+4pp6GURFBggF57ZRiYNTpQhXWruGZaZVywfrE2Rt1bslwMn850+5hrGWYZadmkdxW0F+KvCbJE931f2H1sokptRv+01T6yoHOsxXiRxuEhIeN23pcHX3StFkpeLrJkpsu1LkfLSqr3OdNZLbeavPcoYegkAAHwcYRkAOLD5QKY8MG+97EyveWiTBmdfbTpsTj3aRMnTE/pLnw6x5n8707Nltg61/PmA5BfbfHh0ICkuQiYPTZLrBiQ4XbUGoIk5vvdU7zENosqK676tgCCRToMrq8cuFGnd072hlW6r84iKU0GWSPYhkcIckbAokeh2zPwGAAAaFcIyAKjFsl1HZNqcdZJX5FzQpYHahFdXyrSRXcyMlj/uPubS8R3RvZX8alhnGdWjNUMtAX9TWiySsqqi95gGZMd21W97zVpUDKvUU7cLKn73Bp0EwHYiAAAAgEaGsAwAaqkocyUos9Dln1nk/IfciNAgufacBJk8NFG6xUdzewD+JPeoyK5vK4ZX7l4sUphZv+3Fn1lRPaZDLBPOFQkMcteeAgAA+A3CMgCw06NMh166GpS5omPLZjJ5SJLpSRbbjKGWgF8oLxc5vKmy99gCkbS1+se6by84XKTzqIreYzq8snlHd+4tAACAXyIsA4AaaDN/ez3K6mtYtziZOrSzjO4VbyYHANDEFeWJ7FtaOXvlQpGsA/XbXkxCRTim1WNJI0RCI9y1pwAAACAsA4CavbNqv1sPTXhIoFxzToJMGZokPdow1BJo8k6m2DTnXyZSUlCPjQWIdBxU0XtMA7I2ZzKjJAAAgAdRWQYA1WQXFMuCLeluOy4PjusutwxJkuYRoRxroKkqLRFJW1PRnF+rxzK21m97YbEVTfk1HOs2ViQyzl17CgAAAF8Ly8rKymTJkiWycuVKOXz4sOTl5cnf//53adeunXWZoqIiKSkpkaCgIAkLC/P2LgLwc4czC6S0rB49hKq5uG87gjKgKco7LrJncUVAtnuRSP6J+m2vVc9Twys7nicSRC9DAACAJh+WzZ8/X37zm99IcnJylb8/+OCDVcKyN954Q+69916JioqSgwcPSmRkpDd3E4Cfy3VzU/+cQs9NEgDAy835j2yvCMd2LhRJXSVSXlb37QWFiiQNrwjHdIhly87u3FsAAAD4elj2+uuvy7Rp08wMc6pVq1Zy9OhRCQg4vbn1bbfdJn/84x/l5MmT8sknn8ikSZO8tZsA/NzPKSfkqQU73LrNqLAgt24PgBcVF4gkL68cXrmgohdZfUS1OdV7rMv5ImFR7tpTAAAANKawbPfu3XLPPfeY82PGjJEXX3xRevXqJYGBgTUuHxoaKtdee60J2BYuXEhYBsCjNMRfsuOIvLx0j6zed9yt2w4ODJA2MeFu3SYAD8s6eGrmyr1LRIrz6re99udUhGM6xLJtPxE7738AAADgR2HZs88+K8XFxdKnTx/56quvTBjmyIgRI0xYtn79em/sIgA/VFxaJl9uPCivLt0r2w9ne+QyLjyzrUSH03cI8GllZSIHf64cXvmNyOFN9dteaJRI1zEiPS4U6TZOJLqNu/YUAAAATSUs++6778xwy+nTpzsVlKmuXbuanykp9RzuAADV5BWVyLw1qTJz2T45cDLfo8dn0uBEjj/giwoyK5vzL6yoIMs7Wr/ttexyqvdY4jCRYGa/BQAAaKy8Epalpqaan/3793d6HUtTf50tEwDc4URukby1MlneWpEsJ/KKPX5Qe7aJlsFdWnr8cgA46ejuU9VjKStFykrqfugCg0USh4p0v7AiJGvVjZsBAACgifBKWGZp4m9p7u+MI0eOmJ8xMTEe2y8LrV57/vnnzWydej4sLEy6desmEyZMkLvvvlsiIiLqvO3HHntMHn/8caeW/f777+X888+v82UBqFnaiTx5fdk++WBNquQXOzczZUx4sFzUp618seGQ0+vYiggNkhkT+tU4iQkALykpEtn/Y0XlmAZkx/fWb3sRrSqb818o0nW0SHisu/YUAAAA/haWtW/f3jT537lzpwwYMMCpdZYuXWp+JiUleXTfNCC7+eabJTMz0/o3rWZbs2aNOWnfNO2z1qVLF4/uBwD32344y/Qj+3zDQSktcy6sbxsTLreP6CwTB3WSqLBgubxfe5k2Z53kFZW6FJS9essA6dOBD9KA1+VknArH9iwRKapnP8K2Z1WEY1o9po36ac4PAACaiJyiHEnPS5e84jyJCImQNhFtJEp7r8I7YdnIkSNl165d8t5778mNN97ocPmjR4/Kq6++aioydPZMT9mwYYOpHtNwLCoqSh5++GEZPXq05Ofny9y5c2XmzJmyY8cOufTSS01wpsvUx6ZNtTcM7ty5c722D6CignVN8gl5eclu+X5HRYWqM7rFR8m0kV3kyv4dJDT41Ex1I7q3lnnThsgD89bLzvQcp4ZeakUZQRngxeb8hzdU9B7TgEwb9ddHSIRIl/MrKsj0FNvBXXsKAADgG5+XDq+RuTvmyuKUxVJafqooICggSMZ0GiMTe06UgW0H+vUoGa+EZXfccYe88cYbpkJr1qxZMnXqVLvLpqWlyTXXXGMCs+DgYLOup+iEAxqU6eUsXLhQhgwZYv2fhnTdu3eXhx56SLZv3y5PP/20/PnPf67X5elsoAA8o6ysXBZtS5dXlu6Rn1NOOr3eOZ2ay52jusrY3m0kMLDmFwMNvhZMHymr9h6XOauSZcGW9CqVasGBAWbWS23mrz3K/PlFBfCKwhyRvUsqwrFd34rkHK7f9pp3OtV7LGm4SEi4u/YUAADAZ2w9tlUeXf6o7D65u8b/a3D27f5vzalb827yxPAn5Iy4M8QfeSUsGzhwoNx5553yyiuvyO23325Cs+uvv976/40bN5qqKw2stKKroKDAfNh88MEHTe8wT9BKsSVLlpjzt912W5WgzEIvX8O9bdu2ybPPPmsqz0JCQjyyPwDqpqikTD5df0Be+2Gv7M5wXPllMaZXvAnJBia1cCrc0mWGdI0zp+yCYknPKpCcwlKJCguSNjHhEh3OcwPgUcf3nRpembxcpLSo7tsKCBTpOLhyeOWFIq176YPcnXsLAADgU1YcXCHTv58u+SX5Ti2/++RumfLNFHl29LMytP1Q8TdeCcvUCy+8ILm5uTJnzhz5+OOPzcnyAVV7hllYJgGYMmWK/OMf//DY/nz66afW8/Yq3QIDA+XWW281IdmJEydMuDZu3DiP7RMA5+UUlsjc1Smmcf/hrAKn1gkKDJAr+7WXO0Z1kV5t6z55iAZjhGOAh5UWi6T+VDl75UKRozvqt73w5iLdx1VUj3UdIxLBTLUAAMB/KspcCcosdHldb/ZFs/2uwsxrYVlQUJC89dZbcsUVV8iTTz4pP/9cc0+RM844Q/74xz/KxIkTPbo/y5YtMz8jIyNrnXRg1KhR1vPLly8nLAMa2JHsQnlrRbK8vTJZsgpKnFqnWUiQTBzUUW4b3lkSWtR9dlsAHpZ7TGT3tyI7F4js/k6k8NTkO3USf0ZF5ZgOsUwYKBLktbc9AAAAPkELknTopatBmYWup+t/fMWpgid/4PV3jddee605HTx4UNauXSsZGRlSWloqcXFxcvbZZ0vXrl29sh86tFLpME/tWWZPr169TlunrrQqTUPC7Oxsad68uQkGL7roIpk2bZq0aNGiXtsGmrqUY3ny2rI98uHaNCksKXNqnRYRITJ5aJJMHpIkLSJDPb6PAFyk1eTpWyp7jy0USV2tf6z7YQwKE+k88tTwSu1FBgAA4Me0mb+9HmXO2n1yt6xNX2ua/vuLBvuKtX379qbKrCFoTzSdQEAlJCTUuqyGWFp9pkNIU1NT63W5ixYtsp4/cuSILF261Jz+9a9/yezZs+XKK6+s03Z1UoTaHDp0qE7bBXzB5gOZpmn/V5sOiU1P/Vp1aN5Mfj2is0wY2FEiQqkkAXxKUZ5I8rJTwyuzan8Ncyi6/alwTIOy0Eh37SkAAECjp7NeumU72+cSljV1WtllERUV5XB5S1iWk+N883Bbffv2lauuukoGDRpkQsLi4mLZsWOHvPvuu2ZSg5MnT5pquy+++EIuvvhil7ffsWPHOu0X4Mulwiv2HDMh2bJdFcG2M3q1jTZN+y89q52EBAV6dB8BuOBkqsiuBRXh2L6lIiXO9RmsWYBIwrmnhle27UtzfgAAgBrkFOXI4pTFbjk236V8Z7YXFeo4Q2kKvFJyocMsf/zxR3O+X79+EhsbW+vyGh7pDJlq5MiRHqksswgNdTw0KywszPzMz3d9jO/06dPlscceO+3v5513npk84NVXXzUzheox0plCd+/eLc2aNXP5coCmoLSsXL7ZfFhe/WGPbExzvlfRoM4t5a7zu8r5PVr71Th6wGeVlYqkranoPaanjC31215YjEi3CyrCMW3SH9nKXXsKAADQZKXnpUtpealbtlVaXioZeRmEZe6kww+1Ykr7kqWkpDhcXgMsrbQ6fvy4WXf06NFu3Z/w8HDr+aIix1PPFxYWmp91CbG0N1lttF+Z9m57/fXXTR83nSXUdnZQZzgaHqrDMLWqDfBVBcWl8vHPB+S1H/ZI8rE8p9bRTGxc7zZy5/ld5ZxO9PwDGlz+iYqm/Np7bNe3IvnH67e9uO6VwysvEuk0WCQoxF17CgAA0KQdLzgu36d8L5/s/sSt280tzhV/4ZXKsnnz5pmf119/vVOBU0REhNxwww3y0ksvyQcffOD2sCw6Otp63pmhlToE09khm3WhgZmGZUp7mLkaljnquwb4qqyCYnln1X55c3myHM2pCKUdCQkKkKvP7iB3jOwq3eL9owQY8Nnm/Ed2VA6vXCCSskqkPt9cBoaIJA2vHF45XiTOOxP+AAAANAWHcw+boZKL9i+SnzN+lrJy5yZFc0VkiP/0hvVKWLZu3TozNGrMmDFOr6MBmYZlq1frzFji9sqyVq1amSb/jprjnzhxwhqWeao3mM6KaXHgwAGPXAbgS9KzCuTN5fvk3Z9SJKewxKl1IkOD5ObBifKrYZ2lbeyp6lAAXlRcILJ/+anhlSf31297UW0qhlVq9ViX80XCTn2ZBQAAgNolZybLopRF8t3+72Tzsc0ePVzBAcESHxHvNzeJV8IySyDVuXNnp9dJSkryaHjUu3dvWbZsmekRVlJSIsHBNR+K7du3V1nHU83MAX+w90iOvPbDXjPksqjUuW86WkWFytRhnWXS4ESJbcYwLKBGBVkiWQdFinIrZoOMaS8SHuOeg5V1qGJopYZje5eI1Lf8vv3ZFb3HtIKsXX+RQCbjAAAAcDY72HFih6ke0yqy3Sd3e+3Ajek0xm/6lXktLMvLy3M5FLIsm5WV5ZF9Gj58uAnLtGpMK9+04X5NdFikxbBhwzyyL1u3brWe19kygaZmfepJeWXJHlmw9bAZueWMxLgI+fWILnLdgAQJDwny9C4CjY8+mJKXiayeKbJ9ftUhkAFBIr0vExl4u0jSCNdmiywrEzn4S+Xwym9EDm2o337qmyqtGtPqMa0ii25bv+0BAAD4ER1OufHIRhOQaRXZgZyGGY02sddE8SdeCctat25tqsu0Suvcc891ah1LRZdOCuAJV111lTz55JPm/KxZs2oMy8rKyuTtt9+2Nup3d+80C50R02LUqFEeuQzA2zTwXrrziLyydI+s2ut8o+8+HWLkzlFd5eI+7SQokJktgRodXC/yyZ0iR7bZeQCWimz9rOLUurfI1a+ItO9fe2Xa3u8rqse0iiz3SP0OfIskkR4Xi/QYL5I4TCS4YlZpAAAAOFZcVixrD6811WN6Opp/tM6HrVeLXpKRn2Ga/tdVt+bd5Nw2zmU5TYVXwrKBAweaGRs1eJo0aZJT68yePdv0OTvnnHM8sk86O+SIESNMddkbb7whkydPliFDhlRZZsaMGbJtW8UHkfvvv19CQkJO28epU6ea83/5y1/kscceq/L/TZs2mQkNunXrVmtQppev2rZtK1dffbXbriPQEEpKy2T+pkPyytK9su2Q85Whw7u1MiHZsG5x5rEPwI49i0XmTnJ+OKQGarMuEZn4jkhXm96hx/ZUVI5pQLZ/hUhZcd0PeWCwSKchlc35LxRp1d21ajYAAAA/V1BSICsPrjTVY0tSl0hWUd1G2QVIgPSP7y8XdLrAnBKiE2Trsa0y5Zspkl+S7/L2mgU3kyeGP+F3n9G8EpZdd9118vHHH8t3331nAqgHH3yw1uV1mcWLF5sbQ2fQ9JTnnnvODK3Mz8+X8ePHyyOPPGKqx/T3uXPnymuvvWaW69Gjh8N9rokO77z99tvNNi+++GLp27evqZTTHmlaOffOO+/It99+a5YNCgoywVlkpP/MLoGmJb+oVOatTZWZy/ZK2gnnnoS1cOzivu3kzpFdpW9CrMf3EWgSFWWuBGUWuvzcm0XGPyFybHfFEEv9WR8RcSLdtDn/hRUhXLPm9dseAACAn8kpypEf0n4w1WPLDiyrU5hlab4/sO1AGZs4VkZ3HC2tI1pX+f8ZcWfIs6OflenfT3fpMpoFNzPr6fr+xith2Q033CD/+te/ZMOGDfLQQw/JihUrZPr06TJ48GBrtZYGSCtXrpRnnnlGPvvsMxOU9enTx+lKtLo4++yz5YMPPjCXob3RNCyrToOy+fPnS3R03WboKi0tlUWLFpmTPRqgaXXZFVdcUafLABrSidwimbNqv8xekSzHc4ucWic0OND0IrtjRBdJakVADDjdo0yHXta1wX5xnsj839bvYLfpWxGO6anDAJFA+gkCAAC4QodDauWY9iBbdWiVGXJZF2FBYTK0/VATkI1KGCWxYbUXH+iysy+aLY8uf9SpiQG6Ne9mKsr8MSjzWlimwdcnn3xiqrgOHTokn376qTlpUNayZUvz/2PHjklxcbG115E2ureEZp50+eWXy8aNG02VmYZi2lstNDTUDJ3UqrZ7771XIiIi6rTtSy65xIRgGgL+8ssvkp6ebq6nXj+93v369ZOLLrpIpkyZIjExbpq1DPCSgyfz5fVl+2TumhTJK7JpLF6L6PBguWVwokwZliTx0eEe30egSdFm/vZ6lHlKcLPK5vzjRbqPF4lN8O7lAwAANAGHcw9b+4+tS19nmvbXRWRIpIxMGCljO42V4R2GS0SIa1mFBl8fX/GxrE1fK+9vf18WpyyWUptJorRCTWe9nNhroulR5m9DL20FlLsyRWU9ZWRkyLRp0+Tzzz+3OzOm3hjat+ull16S+Ph4b+1ak6YBYMeOHc157R2XkMCHHdTdzvRs07T/8/UHpaTMuaePNjFhctvwznLjoE4SHV619x8AJ827taJhv6fFdjzVe6zzCJGQZp6/TAAAgCZmf9Z+Uz2mAdmmo5vqvJ0WYS1kdKfRpv/Y4HaDJTQo1K3DQDPyMiS3ONcEcfER8RKlM5k3Mp7IPLxSWWah4ZdWmO3YsUO++uorU2119GjFrA6tWrUyzfwvvfRS6d69uzd3C4AT1iQfl1eW7JHvtmc4fby6tI40/ciuPLu9hAUzXAuosxP7RbZ94ZkDGBAo0vG8isqxHheJxPemOT8AAICLtCBo54mdpkG/hmTODHW0R0MrrR7TIZZnx58twTqZkgdoMNYYwzFv8GpYZtGzZ09zAuDbysrKZfH2DFNJtnb/CafX69+xudx1flcZ17uNBGoXfwDOyT0qcmR7xSmj8ueRHSK5zofUTtE3RRqM6anbBSIRLbmFAAAAXKTDKTce2WiqxzQgS8tJq/MxTIxJNNVjGpKd2epMCdQvNOFfYRkA31ZUUiafbzgory7dI7sycpxe7/yereXOUV3lvM4VvQgB1EDbEOQeEcnYVhGEWQIx7UeWd8w7h2zSxyKdzuPmAQAAcJE25Ne+YxqOac+vI/lH6nwMe7XsZQ3IujbvymcoH0JYBsAqt7BE3l+dIm8s3yeHMgucOjJBgQFy+VntZNqortK7HRNVAFVCsezDVcMwSziW73ylpkc0a96wlw8AANCIFJYWysqDK01AtiRtiWQWZtZ5W/1b9zfDK7WRfsfoij5b8D1eD8vKyspk69atsnfvXsnOzpbSUsez6N16661e2TfAXx3LKZTZK5Ll7ZX7JTPfuamLw0MCZeLATqZxf8eWdZsxFmgyoVjWwVPDJ63h2HaRgrq/kfIY7XkR3a6h9wIAAMCnadP7ZWnLTA8y/ZlXklen7QQFBMnAtgNN9Zg26td+ZPB9XgvL8vPz5e9//7vMnDlTjh1zfpiJDuUiLAM8I/V4nsxctlc+WJMqhSXOTV/cPCJEJg9JkslDk6RlpPtmYgEaRSiWmWozdNLSV2yHSFG2NBq9LhMJpwoUAACguhMFJ2RJ6hLTg2zFwRVmyGVdhAaGytAOQ01Adn7H8yU2LJaD3cgEeysoGzNmjKxevdrMEAGgYW05mCmvLt0r8zcdktIy5x6T7WPD5fYRXWTioI4SEcoIbjRhZWUimSkVIZhtX7GjO0WKnO/h5xbR7UXie4m07lUxa+XKF+u/zYG3u2PPAAAAmoT03HRZnLpYvtv/naxNXyul5Y5Hv9UkIjhCRiWMkgsSL5ARHUZIRAijbxozr3zifeaZZ+Snn34y5/v06SP33nuvDBgwQFq2bCmBgczwAHiDBtUr9x6TV5bulR92Ot+EsmebaJk2qotc3q+9hATxeEUTUlYqcnJ/1VknLaFYcd3K7OsstqNI654VoZj11EMk3OZbSP2yafd3Fb3P6ir+DJGk4W7ZZQAAgMYqJSulYgbLlEVmNsu6ah7WXEZ3HG16kJ3X7jwJCwpz636iiYdlH3zwgfk5dOhQWbx4sYSGMnQL8BatHFu45bC8snSPbEhzvn/SoKSWcuf5XWR0z3hmZUHjVloiciK5Wk8xDcV2iZQ4N5GF2zTvVC0QqwzFwqIdr6szzF79isisS0SKc12/7JBIkatertgOAACAnxUO7Dyx0wRketLzdaU9xywzWJ7T5hwJ1n6waHK8cqvu2bPHfNh+6KGHCMoALyksKZWPfz4gM3/YK3uPOv/BemzvNnLX+V1kQGJLj+4f4HalxSLH91WddVJ/aihWWujFAx4g0iKpMgirrBbToZSteoiERtZv0+37i0x8R2TuJNcCMw3KdD1dHwAAwA+UlZfJpqObzPBKrSBLzU6t87Y6RXcywys1IOvTqo8EansMNGleCcu0kkz7lnXq1MkbFwf4tayCYnnvpxR5c/k+ych2LiAICQqQK/t3kGkju0j3Nk5UuAANqaRI5PieqkMndSjlsd0idWzCWif6JqlF51NhmCUci+suEurBHhVdx4hM/UrkkzudG5KpQy+1ooygDAAANHElZSWyLn2dLNq/SBanLJaM/Iw6b6tHix4mHNOQrHvz7oy28TNeCct69eplepYdPnzYGxcH+KWMrAJ588dkeXfVfskuLHFqncjQILlxUCe5bURnaRfbzOP7CLikpLAiALPOOlkZjmlQVubcfdwtAoJEWnaxCcRsQrGQcGkQGnzdvVIkebnImpki274UsW1Gq8MBdNZLbeavPcoYegkAAJqowtJCWXVwlake05ksTxaerPO2+rXuVxGQdbpAOsZ0dOt+onHxSlg2ZcoUWbVqlXz44Ydy0UUXeeMiAb+x72iuvPbDHvnfugNSVFrm1DpxkaEydViS3DI4SWIjQjy+j0Ctigsqmupbh05WnnRIZR1nI6oTDZjiup3eaD+uq0iwDzZr1QCs84iKU0GWSPYhkcIckbAokeh2IuExDb2HAAAAHpFbnCvLDiwzQyx/SPtB8krqNjlTUECQnNv2XBOQaaP+NpFt3L6vaJy8Epb9+te/Nk3+3377bRk7dqzceOON3rhYoEnbkHrSNO3/ZsthM0meMzq2bCZ3jOgi15/bUcJDgjy9i0BVRXk1h2LafL/cuaDXLQJDRFp1rwzFep8KxzQUC2qk4bEGY4RjAACgCTtZcFKWpC0xAdmKgyukqKyoTtsJDQyVoe2HmuGV5yecL83Dm7t9X9H4eSUsS01NlRdeeEHuuOMOmTRpknzyySdy0003meGZERGO+7rQ6ww4NYvLsl1HTUi2Ys8xpw/LGe1i5M7zu8olfdpKcBDNKOFhWt1kQjGboZMmFNuv92LvHX6dulub6lsrxXqKxPeu6DMWxKxFAAAAvi4jL8P0HtMhlmsPr5XSOo46iAiOkJEJI01ANqLDCInUyY+AWnjl00JSUpK1GZ5+2P/f//5nTs7Q9UpKvNibBvBBJaVl8tXmw/Lq0j2y5WCW0+sN7Rond47qKiO6t6IhJdxPh/7ZhmKmr9gOkcwU7x7t4PDKUMwmENPzzRMJxQAAABqZ1KxU+S6lYgbLDUc21Hk7sWGxZmilDrEc3H6whOkXqYCTvPbVuoZkNZ0HYF9Bcal8uDZVXlu2V1KP5zt1qDSXvrhPW5k2sqv060hJMdwg/+SpUMy20X5WmncPb0hERShmwjCbajENxQIZVgwAANAYaT6w++RuE47pEMsdJ3bUeVvxzeJlTKcxMjZxrAxoM0CCtSctUAdeuefMmjXLGxcDNBmZecXy9spkmb0iWY7lOjcWPzQoUK4d0EF+PaKLdGkd5fF9RBOUf6JqGGapGNPG8d4UGlU1DLP0FYvtKBLIMGIAAIDGrqy8TLYc3VIRkKV8J/uztF1H3SREJci4xHFmiGXfVn0lMID3i2gkYdnkyZO9cTFAo3coM1/eWLZP3ludInlFzo3Hjw4LlpsHJ8qvhiVJfEy4x/cRTUDusaoN9i3hWE66d/cjLKYyDLMEYpXhWGxCRYkkAAAAmoySshL5JeMXWbS/IiBLz6v7e8/uLbqb4ZUXdLpAerToQcsZuB01iYAP2J2RLa8s3SufrT8gxaXODVOOjw6TXw3vLDed10liwhvpDH6NuVdX1kGRolyR0EiRmPa+NxOhDnfPPSpyZJtNldgOkYxtInlHvbsv4bFVZ5209BWLbkcoBgAA0IQVlRbJqkOrTDj2fcr3cqLwRJ23dVars0z1mAZkiTGJbt1PoDrCMqABrdt/XF5eslcWbXP+W5UurSLljpFd5OpzOkhYMH2avBo+JS8TWT1TZPt8EduZeAKCRHpfJjLwdpGkEd4NgHS/tCLMduikZShl/nHxqmYtToVitn3FotoQigEAAPiJvOI8WX5guRli+UPaD5JbnFun7ehwynPbnGvCMe1D1jayrdv3FbCHsAzwsrKycvl+R4a8snSPrEl2/psVbdZ/16guMu6MthIUyBA1rzq4XuSTOyuqtGqiwdnWzypOGhZd/YpI+/7uD8W0d1j1Jvv6s+CkeFVEq4oQLL6XTV+xXiKRrQnFAAAA/FBmYaYsSV1iArKVB1dKYWlhnbYTEhgiQ9oPMUMsz///9u4DPKoq/eP4O+kdCJBQEmrovXcQXLCiIKK4oIBdRGGXXV1g1/W/K7rWVVFEUUHQpSgICqigINKkV6mhJUBIICEhvc7/OYdkTEiZSTIzmcl8P88zz1wy9945cyeB4Zf3vCf8JqnlU8vqYwUcMixLTk6WH3/8UQ4cOCBXrlyR9PT0MlfHNBgM8sknn9h1jIAtZOfmyTf7L8qHv5ySE7EpFh83sGVdeXJQM+nTrDZz8avCqQ0iS8aJWPobMRWozb9dZMznIs2HlP/51N+H1y4UCsQKBWOZ18Su/ENuCMTyq8X869h3HAAAAHA4l9Muy4aoDTog23Vpl+QWnnlRDr4evjIwbKCuIBvQcIAEqMWeAFcJy/Ly8uTf//63vPnmm5Kaatl/OlWIRlgGZ5eWlSNLdkbLJ1vOyIXEdIuOUYVjd3ZsIE8MaibtGtSw+RhRRkVZeYKyAmp/ddzEtaVXmOXliSRFF111siAUy7I8TLUK1TvM1E+sUDjmF2zfcQAAAMChRSdHXw/Izv0oBy4fEKNY1m/5RkFeQTI4fLD8ofEfpHf93uLjwUJlcNGwbMKECfLFF1/oAMzd3V1q164tcXFxOgwLCwuTq1evSkrK9f8gqq/VqVNH/Pz87DU8wOoSUrNkwbazsnD7WUlMy7boGG8PN7m/R7g8NqCZhAfz/V+lVIWXmnpZwR4L+riVT4k8sUUkKapoGKbvT1T83BUV1LBoGFZw71vTvuMAAACAU1D/fz+VeEpXj6km/ccSjlX4XHV96+reYyog6xbaTU+5BFw6LPvhhx/k888/1yGYCs1UddmFCxekY8eO+vFz587p+xMnTsgHH3wg7733ntSqVUtWrlwprVu3tscQAauJTkiTjzeflqW7oyUjO8+iY2r4esr4Po1lfN8mUjvAm3fDEahm/qX1KLNU3BGRWfVE8rLErmo0yg/D8gMx1Wy/TkvHW7ETAAAADhmQ/Rb/m64eUwHZ2WtnK3yuhgENZWjjoXqKZce6HXXTfsAZ2CUsmz9/vr5v166dfPrpp3r74sWLxfZr2bKl/Pe//5Wbb75ZRo4cKbfffrvs27dPatRgGhoc39GYa7pp/+qDMZKbZ1k5cv0aPvJI/6byQM9G4u/NehsOZdfH1jmPLYOymo2LN9pXoZh3oO2eEwAAANVObl6u7I3bq8MxdbuUeqnC54qoGaGrx1ST/pa1WtJ3GU7JLv87//XXX/UPyNNPP23R/nfeeaeMHz9eh2zvvvuu/OMf/7D5GIGK/tZlx5kEHZL9fPyyxce1CAmQJwY1l7s6NRAvD3674nAyrokcXS2OwSAS3PSGqZOtReq0EPHyr+rBAQAAwEll5WbJjpgdOhzbGL1REjISKnyuDnU66OoxdWtSo4lVxwlU27BM9SYrqBwroPqWFcjMzBRv76JTz+69915dhfb1118TlsHh5OUZZd2RWB2S7Y9OtPi47o1ryZODmsuQ1iHiprr4w/FCspgDIie+F6ngaj4VpkrSg5sVb7KvQjFPX/uOBQAAANVSWnaabL24VU+x/OX8L5KSXbGFpdR0StV3rCAgq+dfz+pjBaqSXed9BQf/vrJaYGBgkTAtPDy8yL4hISH6/uzZis+PBqwtMydXVu67IB/+clpOX7a8Ofsf2oTokKx7E1YXdBhZaSKXDolc3Cdyca/Ihb0i8Sdt/7wGd5HazYs32q8dIeLJKkAAAACwrqTMJB2MqYBMBWWZuZkVOo9qyK9WrlRTLG8Kv0mCffi/Daovu4RloaGhEhUVJQkJCUW+5uXlJdnZ2XLw4MFiYZnaX8nIyLDHEIEyJWdky/92RMmnW89I7DXL/nHxcDPIXZ0b6JCsZSg9pKpUTpZI3G/XAzEdju0TiTtqv+qx7o+KNO1/PRQLbi7i4WWf5wUAAIBLupJ+RTZEbdBTLHfG7JQcY06FzuPr4Sv9G/bX/ccGhA2QQC/+XwPXYJewrEOHDjr8OnLkiAwePPj6E3t4SJcuXWTnzp26N9kdd9xR5JgPP/xQ3zdu3NgeQwRKFJecIfO3npXPfz0nyRmW/QPj5+UuY3o0kkcGNJWGNZk+Z3e5OSJXjl8PxArCsdjDIrl2XpGygJuHyB/+yUqUAAAAsKkLKRfkp3PXG/Tvi9snRrFs0bEbBXkF6coxNb2yb4O+4uPB7Ae4HruEZTfddJOsXr1afvzxxyJN/seNGyc7duzQfckeeughuf/++yUtLU0WLlwoP/zwg14U4O6777bHEIEizl5JlY82n5av9pyXrJw8i65OsL+XTOjbRB7q01hq+lE5ZBd5eSIJp69PoywIxy4dFMlOE4fR+k6CMgAAAGgpWSkSmxare4f5efpJqF+oBHgFVPjqnEo8padXqoDsaMLRCp+njm8dU/+x7vW66ymXgCszGNVyfjZ25swZad68uW7ir3qQqSmYSk5OjvTu3Vv27t1bbDlZNSxVVaYeq1Wrlq2HWK2dP3/eNM01OjpawsLCqnpIDuvQ+STdtP+7wzGSZ+FPRlgtX3l8YDMZ3S1cfL1+X7gCVqb+qkqM+r3HmL4/IJKZZLuG+0bLgtIyjV8t0nSANUYEAAAAJ6T+b7vr0i5ZcnyJnhqZW6gViLvBXYY0GiJjWo2RHvV6FPt/cUnnOhJ/RH6M+lGHZGevVbzHd8OAhnp6pepB1rFuR920H3BG522Qedilsqxp06Zy+vRpyc3NlaCgoN+f3MND1q9fL88++6wsW7ZM9y9T1F8QalrmBx98QFAGm1P/4GyJvKJDsq2R8RYf16Z+kDw5qJnc0aG+eLjzD4vVJV8q1GMs/z7N8venfAzXG+036CLSoOv1+9B2IvOGiFyu+G/oJKStSJP+1hwoAAAAnIgKtmZumSmRiZElPq6Cs/Xn1utbRM0ImdV/lrSt3bboPnm5elqlqh5Tt5jUmAqPRz2Hqh5TAVmrWq3MhnOAq7JLZZklkpOT5eTJk7raLCIiosjKmagcKstKlptnlLWHYuTDX07J4QvXLL6evZsF66b9g1rW5R8Xa0lLyF+RMr/5vtpOrviHALNqNRVp2PX3cKx+RxHvEpqVXtwvMv92kWzLVz418fQXmbhWpEFnqwwZAAAAzmXbxW0ydeNUSc9JL1dD/bcHvy09QnvIjks7dPXYxuiNkpDx+2J55dW+dnu5ufH1KZZNazSt8HkAR+W0lWWWCAwMlK5du1b1MOACMrJzdS+yeZtPy7l4y3pbqV+43NK2njx5U3PpHF7T5mOs1jKuicTsL9qAP/Gc7Z4vKOx6YFUQjtXvLOJnYRivjhvzuciSceULzFRQpo4jKAMAAHDZirLyBmWK2n/Sj5PE291b0nIq1odXTafsGtJVV48NCR8i9QPqV+g8gCuzS1jm5uamby+//LI899xz9nhKoJik9Gy9quX8rWfkSoplKyN6ubvJPV0bymMDm0nzuhVvvOmystJELh36vVpMhWPxJ233fP51f59GWRCOBYRU7pzNh1yvEPv6ScumZKqplyM+ICgDAABwUWrylpp6Wd6grPDUzPIGZR5uHtK7fm/dg0ytZFnbt3aFnhuAHcMyLy8v3Y9swACaXKNikjOy5VJShqRm5Yq/l7vUq+EjgT6WrdCijvtky2n5344ofbwlArw9ZGyvRvJw/6YSGsRSyRbJyRKJ+61Qn7F9InFHRQo1MLUqnxpFe4ypcCyo4fUyQGtTFWKTtouc3SKya57I0dVFX5ebx/VVL3s8er1HGb0fAAAAXJZq5l9ajzJrUlM2+zfsr6dXDgwbKIFeJbQVAeC4YVmDBg3k3Llz4u7OSoEo329ktp+Ol0Xbz8m6I7G6x1gBdzeD3NIuVMb1bix9mtUusXdYZFyyfLjptKzcf0Gycy1rzVcnwFse7t9ExvZqLDV8WS65VLk5IleOF51KGXtYJNeyir1yU9MaVWClw7H8W3Az+4ZS6rnUqpbqpqaSqp5qmSki3gEigfVFfH5fvAQAAACuQ1WQqZ5iCekJcjXzqszZP8dmz6UCsZvCbtI9yPo26KsDMwBOGpYNHDhQFi1aJHv37pWePXva4ynh5A5fSJI/L9svJ2JTymjOf0nfWoYGyFv3dZb2DWvox/ZGXZW5P5/SAZulmtT2k8cHNtdTLn08CXWLyMsTSTj9+4qUKhy7dFAku2I9FMxy9xap16FoA/46LUTcHOh9UcEY4RgAAEC1lJ2brcMvFXypACw+I/56GKa+lnHVtF1wq+h0S0vV9qktQxoN0VMse9TrIZ7u/FIfqBarYe7Zs0f69OkjjRo10oFZUBAVGPbkbKthbj55WZ5YtEfSLJwyqfh5ucukm5rLLyevyM4zlq8U0zGshl7Z8pZ29XS1mstTfx0kRv3eY0zfHxDJTLLNpVHTF1WPr8I9xtSf+QAAAAAAK8nNy5WkrCQdfBUEXCoAuzH4Un9WX0/OSnaYa/9K/1fktqa3ibsj/eIYcDBOuxpmt27dZPbs2TJ58mQZNGiQvP/++9K3b197PDWcsKKsvEGZovZ/Y90Ji/cf0KKOPDWoufRpXvIUTpeRfKlQj7H8+7R4Gz2ZQaRuq6I9xkLbiXhSOg4AAADLqXqP5OxkU/hVEHLdGHwV3jaKzWtEbKJxUGOCMqAK2CUse/jhh/V9q1at5MCBA7rRv0r9OnbsKLVq1Sqzl5kKMj755BN7DBMO8I+emnpZ3qDMUqpw7PYO9XUlWcGUTZeSlpC/ImV+8321rfpu2YrqKVa4AX/9Ttf7ewEAAAA3flTNTisWdBUEYEUqwFRAlpkgOXk5LnEN/VXvXgDVMyxbsGCBqXpH3atQJCoqSpfHlUXtR1jmOlQz/9J6lFWGt4ebjO4eJo8NaCaNa7vIPzaqAX3M/qIN+BPP2e75gsKuN+DXUynVrbOIby3bPR8AAACcou9Xmbf8hvj26PvljDwMHhLiF1LVwwBckl3CMtWrzKWnusEin/9q3TAnyMdDHurTRCb0a6JXuay2stJELh0q2oA//qTtns+/btGplOo+gH/EAQAAqnvfr8TMRIuCL7WtpklWR97u3rrhfi2fWhLsE3z95htc5GsLflsgO2J2VPq5VFP/AC9mZgDVNiw7e/asPZ4GTiw5I1t++M3y1SvN+euwljK+X1MJ8LbLt7j95GSJxB4u1GNsv0jcURGjbaauik+NolMpVTgW1FCViNrm+QAAAKwsJStFYtNi9TQ/P08/CfULJYDIn8VzLetayVMfbwi+1L0Kypy175e56q0bgy/TdqGb2kcFYr4evmYLQbzcvKwSlo1pPabS5wBQMdUsSYCzupSUIbl51vvH95b29Zw/KMvNEblyvGgD/tjfRHKzbPN8qh+Cmj6pw7H8m+o7RjAGAACcMAjadWmXLDm+RDZEbZDcQr9YdDe464qdMa3GSI96ParNDBj1mtVUxpJWeSzS76tQMJZjrH59vwxikJreNU3BVy3vWkWqvwqCr4IQLMgryOrfA+r7KqJmhEQmRlb4HOr47qHdrTouAJZz8jQB1UWqlZv6p2TaqNLKVvLyRBJO5zfgzw/HLh0UyU6zzfO5e4vU6/D7NEpVOVanhQhLUgMAACd3JP6IzNwys9SgQgVn68+t1zcVSMzqP0va1m4rjigrN6tY4GVa+fGG4EvdMnIzpDoK9AwsFnyVVP2lbjW8a4iHW9X+N1eFb+r7asL3EyrUi01Vr6njq0uQCzgjwjI4BH+v0ldErYgAb+uez6qMRpHEqEJTKdX9AZHMJNs8n/qwENK2aI8x9Wd3T9s8HwAAQBXZdnGbTN041eKAQgVqKtB4e/Db0rdBX5uPT63gWLjvl2nqY3oJqz5mJEhKtvUXv3IEPu4+xaY9FkxzLDztsWDby91LnI0KYNX3VXm+HwuCMnWcowa4gKuwS1imVr6s7AIBqN7q1fARdzeDVaZiergZJDTIRxxG8qWiUynVfVq8jZ7MIFK3VdEeY6HtRDx9bfR8AAAAjlNRVt5gQlH7q+MW3Lqg3AFFnjFPkrOSi059zK/4KumWlJlUbft+FZn2WKjyq1gzfJ9g3TvOFagAVn1flVXpWJijVzoCrsQuYVnTpk0rfKwqPc3JqX5z6VFUoI+n3NIuVNYeulTpS3NLu3r6fFUiLSF/KuW+38Ox5BjbPZ/qKVa4AX/9TiLerJgDAABci+rXpQKJikx5U9Rx6vjlw5dLem568R5fmVdN1V+Fq8LUrbr2/VIB143B143VXwVfs0Xfr+pCBV8r7lohu2N3y+Jji4v10FNBo+6h13qM7lHGdQRcKCxT/3gB5ozr3dgqYZk6j11kXBOJ2X89FCuoHEs8Z7vnCwoTadilUDjWWcS3lu2eDwAAwEmoZv6VaaauqOO7f9FdsvOypTr3/SppmuON1V+qQb47vWytRgVgqum/uqnVWePS4iQ1O1X8Pf0lxC+E1VkBVw3L5s+fb3af1NRUOX78uCxfvlwuXrwoffv2lccee8wew4OD6NOstrQMDZATscV7MwRImtQzJIi/ZEiq+MglY7CkSPHy7VahgdK7WbD1B5eVJnLp0O/TKFU4Fn9SbMa/7vVAzNSAv4tIQIjtng8AAMCJqVUvrcGZgjLV96u2b+1i1V8l3VQQ5ox9v6qjAK8AwjHACdglLBs/frzF+77xxhvy7LPPykcffaQDs9dee82mY4Nj/cblrfs6y30fbpc0vTqmUfq4HZEH3dfLMLfd4mHIM+2bY3STH/K6y+e5Q2V7nprTbxA/L3d5875OlS9dzskSiT1cqMfYfpG4oyKFyqWtyqfm74FYQTgW1FBdENs8HwAAQDWiKnXU1DZnp1ZwDPYuvtJjaY3vXaXvFwBUBYPRQedIDh48WH755RdZu3at3HLLLVU9HKd2/vx5CQ8P19vR0dESFhYmjmzzycvy9qIv5WV5X1q5nTe7//G8MJkhT8vUB0fLgBZ1y/dkuTkiV44XbcAf+5tIbpbYhKf/9emThcOxWk0JxgAAAMopMSNRr3753Znv5OfzPzts36+Spj0Wm/roG6ynSdKvCgAcI/OwS2VZRTz11FOyadMmmT17NmGZixngdkj6ev1b3HPSLNpfBWrLPP4t7m6tRWRI6Tvm5YkknM5vwJ8fjl06KJJt2fOUm7u3SP2ORRvw12khQv8HAACAclMrT6oVLzdf2CxbLmyRw1cO66/ZU6BXYImrOxZrfO8bLDW8atD3CwCclMOGZS1atND3u3fvruqhwJ7UlMcl4ywOygro/ZeME5m49nrlliqYTIwqNJVS3R8QyUyyzbjdPERC2hbqMdZVJKSNiHsVrcoJAABQDajVJlX1mArH1L1ahdLW7om4R5rVbFZi43tPPtsBgEtw2LAsKSmpyD1cgAq4vn5SJDu1Yser4z4fJVK/0/VVKtPirT3CfAaRuq2L9hgLbS/i6WOj5wMAAHANqlLstyu/6XBM3Q5dOSRGsV/XGA+Dh/y1x19pwA4ALs5hw7LPPvtM39evX7+qhwJ7ObtZ5PLRyp0j7YrIqZ/EqoKbFZ1KqcI47wDrPgcAAIALV49tvbj1evXYhW1yNfNqlY1lSKMhBGUAAMcLy06ePClvvvmmDstUg8vbb7+9qocEe9n1cdVf66AwkYZdCoVjnUV8a1X1qAAAAKqN3Lxc+S3+9+ox1XusstVjdXzqyJWMK5Ue25jWYyp9DgCA87NLWNasWTOz++Tl5UliYqIkJyebvhYSEiIzZ8608ehEoqKi5N1335U1a9bobW9vb4mIiJD77rtPJk2aJH5+1l+WOSYmRtq0aWOaZjpo0CD5+WfHW8XHbjKuiRxdbd/n9K97PRAz9RnrIhIQYt8xAAAAuADVa2zrhfzqsYvbJDEzsVLn83Lzkh71ekj/hv31rVFgIxn17SiJTIys8DkjakZI99DulRoXAKB6sEtYdvbs2XIf07t3b5k/f77Np2GqgGzs2LFFeqOlpaXJrl279O3jjz+WtWvXWhT4lcczzzxDP7bCrl0UMeaKzfjU/D0QKwjHghqKGAy2e04AAAAXrh47HH/4evXY+S26kqyy1WPhgeGmcEwFZb4evkUen9V/lkz4foKk56SX+9zqXOp4NbMFAAC7hGXjx483u4+bm5sEBgZK06ZNdZVV586dbT6uAwcO6OoxFY4FBATI9OnTZfDgwZKeni5LliyRefPmyfHjx+WOO+7QwZnaxxq+/fZbWb58ua6ci4uLs8o5nV5WBZv6l6ZeR5GmA38Px2o1JRgDAACwofj0eF01tvnCZtl+cXulq8e83b2le73uMqDhAB2QNQ5qXOb+bWu3lbcHvy1TN04tV2CmgjJ1nDoeAAC7hWWqQswRTZ06VQdlHh4esm7dOunTp4/psSFDhkiLFi3kueeek2PHjslbb70lL7zwQqWfMyUlRZ5++mm9/cYbb8hDDz1U6XNWC17+1j3fqI9F6ray7jkBAABQpHpMrVZZ0HtMVY9VlppOWbh6zMejfKuN923QVxbcukBmbplp0ZRMNfVSVZQRlAEACjMYjUb7rcXsQFSlWM+ePfX2E088IXPnzi2xj1r79u3l6NGjUqtWLYmNjRVPT89KPe+zzz4rs2fP1hVsGzZsMJV627Jn2fnz5yU8PFxvR0dHS1hYmDhkz7JXm1hnKqabh8hzZ0R8gqwxMgAAAOS7kn5FV4+pqZXbYrZJUubvrUwqWj1W0HtMVZA1CmpklWut/ouzO3a3LD62WDZEbZDcQp8xPQweetVL1cxf9Shj6iUAOLfzNsg8HG41THtZuXKlaXvixImlTg1VlV9qeubVq1d1mDV06NAKP+fOnTvl/fffFy8vL/nggw8qfJ5qSQVbbe4UObKq8udqfSdBGQAAgBWrx9TUSlU9diT+SKXPqaZTFlSPqbCqvNVjllABmArh1C0lK0Xi0uIkNTtV/D39JcQvRAK8rNNeBQBQPTlMWKb6h3311Vdy5coV3bds3Lhx0qBBA5s93+bNm/W9v7+/dOvWrdT9VMVXgS1btlQ4LMvJyZHHH39cV6s9//zz0qoVUwSL6fGodcIydR4AAABUuHqs8MqV17KuVepK+rj7FKkeCw+6/tt/e1HBGOEYAMDhwjI15VH16VK9wdTKkjVr1izy+IcffqgfLzwjdNasWbJixQq5+eabbTImNbVSiYiI0OMqTevWrYsdUxGqP5kKBJs3by4zZswQa5ccliUmJkacQpMBInXbiFyu+HWWkLYiTfpbc1QAAADVWk5ezvXqsfPXq8eOJlTis1i+JkFNTNVj3UK72aR6DAAApw7L1OqPu3fvlttuu61YUHbmzBndx0tVXBWWnJysV6pUq1HWqVPHquPJyMjQFWyKubmsqleZqj5LTU3Vc18r4vTp0/Kvf/1Lb8+ZM0d8fKz7YaFgbq7TU/3bRs4VmX+7SHYFVsf09BcZ8QGrXgIAAFhQPVbQmF9VjyVnJVe6eqxn/Z6mgCw8sJp8PgUAuCS7hGWq15fqG6DCshup8Cg7O1t8fX3liy++0JVkP/zwg4wfP14SExN14/2///3vVh2PCuIKBASY71dQEJaplSwrQi0gkJ6eLvfff78MGzasQudwGQ06i4z5XGTJuPIFZiooU8ep4wEAAFCseuzg5YOmgMya1WNqamW3et10s34AAKoDu4RlFy5c0PdqZcmSGu2rIE0FSiNGjNBfu/fee2X79u3y3//+V7777jurh2WqsqyAarZvjrf39X/4VeBVXgsXLpQff/xRgoKC9OuxBXMVb2oaZsHKn06h+RCRiWtFvn7SsimZauqlqigjKAMAADC5nHbZFI5tj9le6eoxXw9f6Vnv9+qxsEAHXGEdAABnCcsuX76s74ODg4uFaKdOndJh2ejRo4s8piqwVLikpmFaW+FpkFlZWWb3z8zM1Peq+q081FTPadOmmXqw1a9fX2zBGsuiOhwVfE3aLnJ2i8iueSJHV4sUWvJb3Dyur3qpmvmrHmVqCicAAICLV48duHzAFJAdSzhW6XM2rdG0SO8xqscAAK7ALmFZQSB14zTGghUp/fz8ilU+hYaGFpsyaS2BgYGmbUumVqopmJZO2Szsz3/+sw7MunfvLpMmTarASF2cCsCaDrh+y7gmkhwjkpki4h0gElhfxCeoqkcIAABQpeLS4vTKlZsvbJZfL/4qydmVrx7rVa+XDsf6NexH9RgAwCXZJSyrW7euXLx4UVeR9e3b1/T19evX6/vevXuLu7t7iVMla9SoYZPKMrVogAqyzK0kefXqVVNYVp5G+ur1Llq0SG8PGTJEli1bVub+cXFxsmTJEr3dtGlT6dWrl8XP5RJUMEY4BgAAXFx2XrYciPu9euz41crPwmhWo1mR6jEvd/NtSgAAqM7sEpapyqpVq1bJJ598ImPHjhU3NzeJj4+XFStW6CmYqqn/jVSwVrjCzNratGmjK9siIyMlJydHPDxKvhTHjh0rcoylCk/vfO2118zuf/ToUXnggQf0tlrcgLAMAAAASmxqrGy9uPV677GL2yUlu2KLThWpHqvfSzfmV9VjDQMacqEBALB3WPbQQw/psEyFU/3799fVZd9++60kJSWJp6enDtButG3bNn3fsmVLm4xJjUONR1WN7dmzp9RwatOmTabtfv362WQsAAAAQOHqsf1x+03VYyeunqj0xWleo/n16rGw/tI1pCvVYwAAVHVYNnLkSL3C5VdffSW//vqr7NixQ4xGo37sueeeKza9MTc3t8yqM2tQK2++8sorenv+/PklhmV5eXl6NUulZs2aMnjwYIvP36RJE9NrLIt6jcqgQYPk559/LscrAAAAQHVxKfWS7j2mwrFfY36tdPWYn4efrh4rmF7ZIKCB1cYKAEB1Z5ewTFH9uObMmSNffvmlXLp0Sa8MqaYbTpw4scR9Y2Nj9fbQoUNtMh61oMCAAQN0dZmaHqrG0qdPnyL7vPnmm3p6pDJlyhRdBVfYggULTOP/5z//KS+++KJNxgoAAIDqWT2mGvOrgOzk1ZOVPmdEzQhTOKaqxzzdi352BQAADhaWqT5lkydP1jdz1LTMkqZmWts777yjp1amp6fLsGHDZMaMGbp6TP1ZBXYfffSRaSrotGnTbD4eAAAAVO/qsYKplap6LDX7+iJSlake612/t55a2b9Bf6kfUN9qYwUAwJXZLSxzRF26dJGlS5fKuHHj5Nq1azosu5EKytasWSOBgYFVMkYAAAA4p+zcbNkXt0+HY6qCLDIx0irVY6oxv6oe6xLSheoxAACqe1iWmZkpiYmJUrduXV2JZg/Dhw+XgwcP6iozFYqdP39evLy8JCIiQkaPHq0r4fz8/OwyFgAAADh/9ZieWnn+evVYWk5apc7n7+l/vXosf3plPf96VhsrAAAomcFoSRf6SkpJSZFffvlFbw8cOFACAgKKPH7lyhV54oknZPXq1ZKTk6Mff+yxx+Tll1/WwRUqRwWABYsoREdHS1hYGJcUAADAStVje+P2mqZXWqN6rEWtFjoYUxVknet2pnoMAAA7Zx52qSxbvny5boTfqFEjOX36dLEVJ2+77TbZu3evafXI5ORk+e9//ytRUVGybNkyewwRAAAAsEhMSoypMf+OmB1WqR7rU7+PDsj6NexH9RgAAFXMLmHZDz/8oO9HjRpVbHql6hm2Z88eMRgM0rVrVxk0aJBs2rRJh2cqZPv+++/l1ltvtccwAQAAgGKycrOuV4+dv149dirpVKWvUstaLU1TKzuHdBZPN1auBADApcKyw4cP6zCsT58+xR5btGiRvu/WrZts27ZNPDw8JDs7WwYMGCC7du2ShQsXEpYBAADAri6mXDQ15lfVY+k56ZU6X4BngPRpkF891qCfhPqHWm2sAADACcOyy5cv6/vGjRsX+boKxVQVmQrSJk2apIMyxdPTU5588knZuXOn7Nixwx5DBAAAgItXj+2J3WPqPXY6qWjrkIpoVauVqXqsU0gnqscAAHASdgnLEhISTCFYYbt375b09HQdlqm+ZYW1bNlS31+6dMkeQwQAAICLuZBywTS1cselylePBXoGSu8GvXVjftV7LMQvxGpjBQAA1Sws8/X11U374+LiinxdVZUpzZs3l9DQ0GLHAAAAANasHtsdu9tUPXYm6Uylz9k6uLWpeqxj3Y5UjwEAUA3YJSxTYdj+/fvl559/lmHDhpm+/vXXX+uqMtXUv7SpmyEh/EYOAAAAFXM++bwpHNt5aadVqscKeo+pW12/urw1AABUM3YJy4YOHSr79u2TOXPm6Mb96jZ//nzdwF+FZcOHDy92zMGDB/V9gwYN7DFEAAAAVAOZuZmy59Ie3ZhfBWRnr52t9DnbBLcpUj3m4WaXj9AAAKCK2OVf+ilTpsjcuXP1VMw777yzyGNt2rQpMSxbs2ZNqStoAgAAAAWik6NN1WO7Lu2qfPWYV6D0bdDXtHIl1WMAALgWu4Rl9evXl2+//VbGjBkjMTExpq83a9ZMvvrqKx2KFXbq1CnZvHmzqSoNAAAAKFw9tvvS773HrFk9NiBsgHSo04HqMQAAXJjdasjV1MszZ87I1q1b9QqXKkDr37+/eHgUH4IK1P7xj3/o7ZL6mQEAAMAxpWSlSGxarKRlp4mfp5+E+oVKgFdApc8bfS3aNLVSVY9l5GZU6nxBXkG/V4817Cd1fOtUeowAAKB6sGvDBS8vLxk8eLDZ/VSIpm47d+6Uv/71rzJ79my7jA8AAADlZzQadYC15PgS2RC1QXKNuabH3A3uMqTREBnTaoz0qNej2IyC0mTkZBRZufLctXOVfmva1m57vXqs4QBpX6c91WMAAKBEDted9Pz587Jo0SJ9O378uP4aYRkAAIBjOhJ/RGZumSmRiZElPq6Cs/Xn1utbRM0ImdV/lg6tShJ1LapI9ZiablnZ6jHVc6x/WH9dRUb1GAAAcJqwLDU1VfcuW7hwoWzatEn/dlJR95b+9hEAAAD2te3iNpm6carFDfVVoDbh+wny9uC3dXilqsdUKFZQPRaVHFXpMbWr3c60cqXqPebu5l7pcwIAANdSZWGZCsJ+/PFHHZCtXLlS0tLSTF9XwsLC5J577pFRo0ZV1RABAABQRkVZeYKyAmr/yT9N1tVlxxKOVbp6rIZ3DR28qamV6r62b23eMwAA4Fxh2W+//SafffaZ/O9//zOtjFkQkDVq1EhGjx4t9957r/Tq1cveQwMAAIAF1Gc3NfWyvEFZgey8bDlw+UCFjjWI4Xr1WNj16rH2tdtTPQYAAJwvLLt8+bJ88cUXuorswIEDRQKymjVrSmJiop5u+dprr8l9991njyEBAACggtTUydJ6lNlCTe+appUrqR4DAABOG5ZlZmbKqlWrdEC2bt06yc3NNQVk3t7ecvvtt8u4ceP0va+vr62GAQAAACtTq17akqoeU6tVFvQeU5Vk9B4DAABOG5Zt2bJFB2RffvmlXLt2rUij/gEDBuiATFWP1ahRw9pPDQAAABtLyUqRDVEbrH7eWt61pG/D36vHgn2Crf4cAAAAVRKWDRw4UAdjBVVkbdu2lbFjx+qb6kkGAAAA5xWbFiu5xlyrnW9MqzFyV/O7dMN/qscAAEC1noYZGBgo77zzjkyYMMFWTwEAAAA7S8u+voK5taigrEPdDlY9JwAAQGW4iQ2oqrKUlBR55JFHpGPHjrpx//nz523xVAAAALAjL3cvq57P39PfqucDAABwuLBs8+bN8uijj+qeZCo0O3z4sEyfPl2aNGkigwcPlk8//dTUywwAAADOY0/sHnnul+esdj4Pg4eE+IVY7XwAAAAOGZb169dPPvroI7l06ZIsXbpUr3bp7u4ueXl58ssvv8hjjz0m9erVk9GjR8s333wjOTk51h4CAAAArOhK+hWZuWWmTPh+gpxOOm218w5pNEQCvAKsdj4AAACHnYapeHl56UBs9erVcuHCBXnzzTf1lExVbZaRkSErVqyQkSNHSmhoqK2GAAAAgErIzcuVxccWy11f3yXfnPrG6tdyTOsxVj8nAACAw4ZlhdWtW1f+9Kc/yb59++TgwYPy5z//WVeXqeDs6tWrevVMRX19ypQpeionAAAAqs7BywflgTUPyMs7Xpbk7GSrnz+iZoR0D+1u9fMCAABUlsGoEqsqoKZlrlu3ThYuXCirVq2S9PT06wPKD85CQkJ05dmoUaPk5ptvroohVhtqcYXw8HC9HR0dLWFhYVU9JAAA4KASMxLl7b1vy4qTK8QoZX9MNIjB7D4l8fXwlQW3LpC2tdtWYqQAAABik8yjysKywpKTk3V/s0WLFsmWLVt0xZkenMGgb/Q1qxzCMgAAYE6eMU++Pvm1DsoSMxPL3NfPw08mdZ4kzWo0k2mbpkl6zvVfeloalL09+G3p26AvbwoAAHDIzMMu0zDNCQwM1Ctobtq0SU6dOiUvvviiRERE6NDMAbI8AACAau1o/FF58LsH5cXtL5oNym5tcqt8M+IbGd9uvAwIG6ArxNSUSkuo/dT+BGUAAMCROURlWWm2bt2qq83mzp1b1UNxalSWAQCAklzLuibv7XtPlh5fqivLytIkqInM6DVD+jToU+wx9XFyd+xuvRjAhqgNkmvMNT3mYfDQq16qZv6qR1lByw0AAABHzTw8xIH169dP3wAAAGA9Ktz69vS38ubuNyUhI6HMfX3cfeTxjo/rSjIvd68S91EBWI96PfQtJStF4tLiJDU7Vfw9/SXEL0QCvAJ4+wAAgNNw6LAMAAAA1nXy6kl56deXZG/cXrP7DgkfIs/1fE4aBjS0+PwqGCMcAwAAzoywDAAAwAWoSq85++fIF0e/KDJNsiRhAWEyvdd0GRg20G7jAwAAcBSEZQAAANV8yuUPZ3+Q13e9LnHpcWXu6+XmJY90eEQebv+w+Hj42G2MAAAAjoSwDAAAoJo6k3RGXt7xsvwa86vZffs37C/Te06XRkGN7DI2AAAAR0VYBgAAUM2k56TLRwc/kgW/LZCcvJwy963nX0/+1uNvesVKVqoEAAAgLAMAAKhWUy43RG+QV3e+KjGpMWXu6+HmIePbjtcrXfp5+tltjAAAAI6OyjIAAIBqIDo5Wl7Z8YpsvrDZ7L696vWSGb1nSLMazewyNgAAAGdCWAYAAODEMnMz5dNDn8rHhz6WrLysMvet61tX/trjr3Jrk1uZcgkAAFAKwjIAAAAntfn8Znll5yu6qqws7gZ3+WObP8qkTpMkwCvAbuMDAABwRoRlAAAATiYmJUZe3fWq/BT1k9l9u4Z0lRm9Zkir4FZ2GRsAAICzIywDAABwEtm52fLZkc/0SpdqxcuyBPsEy5+7/Vnuan4XUy4BAADKgbAMAADACeyI2SGzdsySM0lnytzPIAa5r9V98kyXZ6SGdw27jQ8AAKC6ICwDAABwYHFpcfLGrjfku7Pfmd23Q50OMrP3TGlXu51dxgYAAFAdEZYBAAA4oOy8bPnf0f/JnP1zJC0nrcx9VQXZlK5TZFSLUeJmcLPbGAEAAKojwjIAAAAHszd2r7y04yU5efWk2X3vaXGPTO06VWr51LLL2AAAAKo7wjIAAAAHEZ8eL2/teUu+OfWN2X1bB7eWmb1mSueQznYZGwAAgKsgLAMAAKhiuXm58uWJL+Xdfe9KclZymfsGeAbI5C6T5f5W94uHGx/lAAAArI1PWAAAAFXo0OVDesrlkfgjZve9s9mdMq37NKnjW8cuYwMAAHBFhGUAAABVIDEjUd7Z944sP7FcjGIsc9+ImhEyo9cM6VGvh93GBwAA4KoIywAAAOwoz5gnX5/8Wt7e+7YkZiaWua+vh69M6jRJxrYdK55unnYbIwAAgCsjLAMAALCTo/FH9ZTLg5cPmt33lia3yF+6/0Xq+dezy9gAAABwHWEZAACAjV3Luibv7XtPlh5fqivLytIkqIlM7zVd+jboy/sCAABQBQjLAAAAbMRoNMrq06vlzd1vSnxGfJn7+rj7yOMdH5fx7caLl7sX7wkAAEAVISwDAACwgZNXT8qsHbNkT+wes/sODh8sz/d8XhoGNOS9AAAAqGKEZQAAAFaUmp0qH+z/QD4/+rnkGnPL3FeFY2qVy4FhA3kPAAAAHARhGQAAgJWmXP5w7gd5fefrEpceV+a+amXLRzo8Io+0f0R8PHy4/gAAAA6EsAwAAKCSziSdkVd2vCLbY7ab3bdfw34yo+cMaRTUiOsOAADggAjLAAAAKig9J13mHZwn83+bLzl5OWXuW8+/njzf43m5udHNYjAYuOYAAAAOirAMAACgAlMuN0ZvlFd3vioXUy+W/WHL4CEPtXtInuj4hPh5+nGtAQAAHBxhmYhERUXJu+++K2vWrNHb3t7eEhERIffdd59MmjRJ/Pwq/sF29+7dsmnTJtm1a5ccOXJELl++LAkJCeLl5SUNGjSQ3r17y4QJE2Tw4MHWfF8BAICNRCdHy392/kd+Of+L2X171uspM3vNlGY1m/F+AAAAOAmDUf1q1IWpgGzs2LGSlJRU4uOtWrWStWvXSrNmFfuQ279/f9m6davZ/UaPHi0LFy4UHx/rN/k9f/68hIeH6+3o6GgJCwuz+nMAAFDdZeZmyqeHP5VPDn2it8tS17eu/KX7X+S2prcx5RIAAMCGbJF5uHRl2YEDB3T1WFpamgQEBMj06dN1hVd6erosWbJE5s2bJ8ePH5c77rhDV4apfcpLVakNGjRI+vbtK23atJF69epJ7dq1dYWZev65c+fKmTNn5MsvvxQ3Nzf9vAAAwLFsubBFXt7xsq4qK4u7wV0eaP2APN35aQnwKv/nBgAAAFQ9l64sU8HYzz//LB4eHvLLL79Inz59ijz++uuvy3PPPae3/+///k9eeOGFcj9HTk6OPn9pVDB38803y/bt11fPOnjwoHTo0EGsicoyAAAqJiYlRl7b9Zr8GPWj2X27hHTRUy5bBbficgMAANiJLTIPN3FRqlJMBWXKI488UiwoU6ZNm6arwZS3335bsrOzy/08ZQVliq+vr0yZMsX0ZxXaAQCAqpWdmy0fH/pY7l51t9mgLNgnWP7d79+y4NYFBGUAAADVgMuGZStXrjRtT5w4scR91LTIhx56SG9fvXrVFK5Zm7+/v2k7IyPDJs8BAAAssyNmh4z6dpS8s/cdSc9JL3U/gxjk/lb3yzcjvpERESPEzeCyH6sAAACqFZftWbZ582ZTUNWtW7dS91P9xgps2bJFhg4davWxLF682LTdunVrq58fAACYF5cWJ2/sekO+O/ud2X3b124vf+/9d2lXpx2XFgAAoJpx2bDs6NGj+j4iIqLMqZKFw6uCYyorLy9PN/j/7bffZPbs2aYqN7Xy5i233GKV5wAAAJbJycuRxccWy/v735fU7NQy9w3yCpIpXafIqBajxN3NnUsMAABQDblkWKamOl65ckVvm2v8VqtWLV19lpqaqhvFVUaTJk3k3LlzJT7WuHFjWb58udkeZ6U1sytLTExMuc8JAIAr2Be3T1769SU5cfWE2X1HRoyUqd2m6h5lAAAAqL5cMixLTk42bQcEmF/WvSAsS0lJsfpYVDimVtlUTf6DgoIqdI6CVR8AAIBl4tPj5a09b8k3p74xu2+rWq30lMvOIZ25vAAAAC7AJcOywk30vby8zO7v7e2t79PTS2/ya4l169ZJVlaWnoYZHx8vW7dulQ8++EBeeuklOXnypMyZM8ei8A4AAFRMbl6ufHniS3l337uSnPX7L89KEuAZIJO7TNZN/D3cXPIjEwAAgEtyyU9+Pj4+pm0VXpmTmZmp7319fSv1vC1btizy58GDB8vTTz+t+5QtWrRIDhw4oBcRCAwMLNd5zU0PVdMwe/bsWaExAwBQXRy6fEhe2vGSHIk/YnbfO5vdKdO6T5M6vnXsMjYAAAA4DpcMywqHUZZMrVRTMBVbVH2pnmifffaZtG3bVg4ePCivvPKKvPzyy+U6h7m+awAAuLLEjER5Z987svzEcjGKscx9m9doLjN7z5Qe9XrYbXwAAABwLG7iopVlderUsag5/tWrV01hma16g7Vp00ZatGiht7/66iubPAcAAK4mz5gnK06ukOErh8tXJ74qMyjz9fCVad2myZd3fUlQBgAA4OJcsrKsIKDavHmzREZGSk5OTqmrUB47dqzIMbZSt25d3bestNUyAQCA5Y4lHNOrXB64fMDsvkMbD5Xnejwn9fzrcYkBAADgumFZ//79dVimqsb27NkjvXr1KnG/TZs2mbb79etns/FcuHBB39PgHwCAilNN+9/b954sOb5EV5aVpXFQY5nRc4b0bdiXSw4AAADXnoapjBgxwrQ9f/78EvdRq1YuXLhQb9esWVM35LeFXbt2mSrKOnToYJPnAACgOjMajfLtqW9l+NfD5X/H/ldmUObt7i3PdHlGVty1gqAMAAAAxbhsWKZWhxwwYIDe/uSTT2T79u3F9nnzzTfl6NGjenvKlCni6elZ5PEFCxaIwWDQtxdffLHY8Tt37pS9e/earSgbP3686c8PPvhghV8TAACuKPJqpDz8w8MyY8sMic+IL3Pfm8JvklUjVsnjHR8XL3cvu40RAAAAzsNlp2Eq77zzjp5amZ6eLsOGDZMZM2bo6jH15yVLlshHH32k92vZsqVMmzat3Oc/cuSITJw4Ufr27SvDhw+Xzp07695kBSHZxo0bdVVbUlKS/tof/vAHvT8AADAvLTtNPjjwgXx+5HPJMeaUuW/DgIYyved0GRQ+iEsLAACAMrl0WNalSxdZunSpjBs3Tq5du6bDshupoGzNmjUSGBhY4efZtm2bvpVlwoQJ8v7774ubm8sW+wEAYPGUy3Xn1slru16TuLS4Mvf1dPOUh9s/LI92eFR8PHy4wgAAADDLpcMyRVV8HTx4UFeZqVDs/Pnz4uXlJRERETJ69GiZPHmy+Pn5Vejc999/vzRo0EA2bNigwzJVTRYXFydZWVkSFBQkLVq00JVtauplx44drf7aAACobs4mnZWXd7ws22OKt0+4Ub8G/WR6r+m6kT8AAABgKYNR/XoW1ZoKAMPDw/V2dHS0hIWFVfWQAAAol/ScdJl3cJ4s+G2BZOdll7lvqF+oPN/zeflDoz/ovqIAAACovmyRebh8ZRkAAHBsG6M2yn92/kcupl4scz8Pg4c82O5BebLjk+LnWbGqcAAAAICwDAAAOKTo5Gh5deersun8JrP79qjXQ2b2minNaza3y9gAAABQfRGWAQAAh5KZmymfHv5UPjn0id4uSx3fOvKX7n+R25vezpRLAAAAWAVhGQAAcBhbLmyRV3a8IlHJUWXu52Zwkz+2/qNM6jxJAr0qvmI1AAAAcCPCMgAAUOUupV7SUy5/jPrR7L6d63aWv/f+u7QKbmWXsQEAAMC1EJYBAIAqk52bLQuPLJQPD36oV7wsSy3vWvKnbn+SuyPu1pVlAAAAgC0QlgEAgCqxM2anzNoxS04nnS5zP4MYZHTL0fJs12elhncNu40PAAAAromwDAAA2NXltMvy+u7X5bsz35ndt13tdnrKZfs67e0yNgAAAICwDAAA2EVOXo4sPrZY3t//vqRmp5a5b5BXkEzpOkVGtRgl7m7uvEMAAACwG8IyAABgc/vi9slLv74kJ66eMLvviIgRujdZsE8w7wwAAADsjrAMAADYTHx6vPx3z39l1alVZvdtWaulnnLZJaQL7wgAAACqDGEZAACwuty8XPnqxFfyzr53JDkrucx9/T39ZXLnyTKm9RjxcOOjCQAAAKoWn0gBAIBVHb5yWE+5/C3+N7P73t70dvlL979IXb+6vAsAAABwCIRlAADAKpIyk+Sdve/oijKjGMvct1mNZjKz10zpWb8nVx8AAAAOhbAMAABUSp4xT1ZFrtK9ya5mXi1zX18PX3mq01Myrs048XT35MoDAADA4RCWAQCACjuWcExPuTxw+YDZfYc2HirP9XhO6vnX44oDAADAYRGWAQCAclNN+9/b954sOb5EV5aVpXFQY5nec7r0a9iPKw0AAACHR1gGAAAsZjQaZc2ZNfLGrjckPiO+zH293b3lsQ6PycT2E8XL3YurDAAAAKdAWAYAACxyKvGUzNoxS3Zd2mV235vCbpLnez4vYYFhXF0AAAA4FcIyAABQprTsNJl7YK4sOrJIcow5Ze7bMKCh/K3n3+Sm8Ju4qgAAAHBKhGUAAKDUKZfrz62X13a9JrFpsWVeJU83Tz3d8tEOj+oVLwEAAABnRVgGAACKOZt0Vl7Z+Ypsu7jN7NXp26CvbuDfpEYTriQAAACcHmEZAAAwSc9Jl3kH58mC3xZIdl52mVcmxC9Enu/xvAxtPFQMBgNXEQAAANUCYRkAANA2Rm2UV3e9KhdSLpT94cHgIQ+2fVCe7PSk+Hn6cfUAAABQrRCWAQDg4s4nn5f/7PyPbDq/yey+3UO7y8xeMyWiVoRdxgYAAADYG2EZAAAuKjM3U+Yfni8fH/pYb5eltk9t+UuPv8gdTe9gyiUAAACqNcIyAABc0NYLW+XlHS9LVHJUmfu5GdzkgdYPyNOdn5ZAr0C7jQ8AAACoKoRlAAA4uZSsFIlNi5W07DTdQyzUL1QCvAJK3PdS6iV5bddrsv7cerPn7VS3k/y999+ldXBrG4waAAAAcEyEZQAAOCGj0Si7Lu2SJceXyIaoDZJrzDU95m5wlyGNhsiYVmOkR70eetpkdm62LDq6SOYemKtXvCxLLe9a8qduf5K7I+7WlWUAAACAKyEsAwDAyRyJPyIzt8yUyMTIEh9XwZmqHFO3iJoRMrbNWFl0ZJGcTjpd5nkNYpB7W94rU7pOkRreNWw0egAAAMCxEZYBAOBEtl3cJlM3TjVbHVZABWr/t/3/zO7XtnZb+Ufvf0j7Ou2tMEoAAADAeRGWAQDgRBVl5QnKLKGa9k/pMkVXlLm7uVvtvAAAAICzIiwDAMBJepSpqZfWDMrubn637k1W27e21c4JAAAAODvCMgAAnIBq5l9aj7LyalmrpV7lsktIF6ucDwAAAKhOCMsAAHACatVLa2hVq5UsuXOJeLjxEQAAAAAoCevBAwDg4FKyUmRD1AarnEtVp2XkZFjlXAAAAEB1RFgGAICDi02LlVxjrlXOpc4TlxZnlXMBAAAA1RFhGQAADu5M4hmrni81O9Wq5wMAAACqExqWAADggHLycmTT+U2y7Pgy2XZxm1XP7e/pb9XzAQAAANUJYRkAAA5ETZFcfnK5LD+xXE+/tDYPg4eE+IVY/bwAAABAdUFYBgBAFTMajbLj0g5dRbYxaqPkGHNs9lxDGg2RAK8Am50fAAAAcHaEZQAAVJGkzCRZFblKvjzxpZy9dtYuzzmm9Ri7PA8AAADgrAjLAACws8NXDsuSY0vk+7PfS2Zupt2eN6JmhHQP7W635wMAAACcEWEZAAB2kJadpsOxpceXypH4I+U6tmFAQ7m35b3SrnY7mbJxiqTnpJf7+X09fGVW/1liMBjKfSwAAADgSgjLAACwodOJp2XZiWXyTeQ3kpydbPFxBjHIwLCBcl+r+6Rfg37i7uauv/724Ldl6sap5QrMVFCmjmtbu22FXgMAAADgSgjLAACwsuzcbPkp+ifdsH/XpV3lOjbYJ1hGtRilK8kaBDQo9njfBn1lwa0LZOaWmRKZGGnR1EtVUUZQBgAAAFiGsAwAACuJSYnRzfpXnFwh8Rnx5TpW9RK7v9X9cnOjm8XT3bPMfVXwteKuFbI7drcsPrZYNkRtkFxjrulxD4OHXvVSNfNX52XqJQAAAGA5wjIAACohz5gnWy9s1VVkv1z4Rf/ZUgGeAXJX87v0VMvmNZuX63lVANajXg99S8lKkbi0OEnNThV/T38J8QuRAK+ACrwaAAAAAIRlAABUQEJGgnx98mtdSXYh5UK5jm0T3EZXkd3W9Dbx8/Sr9PVXwRjhGAAAAGAdhGUAAFjIaDTKvrh9ekXL9efWS3ZetsXXztvdW25tcqsOydrXac/USAAAAMBBEZYBAGCGmua4+vRqHZJZ0lS/sCZBTWR0y9Fyd8TdUsO7BtcaAAAAcHCEZQAAlOJ4wnEdkK05vUbSctIsvk7uBnfdYF/1IutVrxdVZAAAAIATISwDAKCQzNxMWXd2nQ7JDlw+UK5roxrr39vyXhnVYpTeBgAAAOB8CMsAABCR6GvRsuzEMlkZuVISMxPLdU36Nuirq8gGhQ0SDzf+aQUAAACcGZ/oAQAuKycvRzad3yTLji+TbRe3letY1X9sZMRI3Y+sUVAjm40RAAAAgH0RlgEAXE5cWpwsP7lclp9YLrFpseU6tmPdjnpFy2GNh4mPh4/NxggAAACgahCWAQBcgtFolB2Xdugqso1RGyXHmGPxsb4evnJHszt0SNY6uLVNxwkAAACgahGWAQCqtaTMJFkVuUq+PPGlnL12tlzHRtSM0L3IhjcbLgFeATYbIwAAAADHQVgGAKiWDl85LEuOLZHvz36vV7i0lGrQP7TxUF1F1jWkqxgMBpuOEwAAAIBjISwDAFQbadlpOhxbenypHIk/Uq5jGwY0lHtb3qub9tf2rW2zMQIAAABwbIRlIhIVFSXvvvuurFmzRm97e3tLRESE3HfffTJp0iTx8/Or8AW+du2arF27Vn766SfZs2ePnD59WtLS0qRGjRrSrl07ufPOO+XRRx+VmjVrWvN9BQCXcjrxtCw7sUy+ifxGkrOTLT7OIAYZGDZQT7Xs16CfuLu523ScAAAAAByfwag6HrswFZCNHTtWkpKSSny8VatWOuxq1qxZuc/93XffyciRIyUzs+zpP6GhobJ48WIZPHiw2ML58+clPDxcb0dHR0tYWJhNngcA7Ck7N1t+iv5JN+zfdWlXuY4N9gmWUS1G6UqyBgENbDZGAAAAALZli8zDpSvLDhw4oKvHVKVXQECATJ8+XQdW6enpsmTJEpk3b54cP35c7rjjDtm1a5fepzzi4+N1UObm5iZDhw6VW2+9VTp16qSryNSb+cUXX8jSpUslNjZWV5ht3bpVOnfubLPXCwDVQUxKjG7Wv+LkConPiC/Xsd1Du+teZDc3ulk83T1tNkYAAAAAzsulw7KpU6fqoMzDw0PWrVsnffr0MT02ZMgQadGihTz33HNy7Ngxeeutt+SFF14o1/k9PT3liSeekBkzZkijRo2KPNalSxcZPny49OvXT5599lk9jmnTpunpmgCAovKMebL1wlZdRfbLhV/0ny0V4BkgdzW/S0+1bF6zOZcWAAAAQJlcdhqmqhTr2bOn3laB1ty5c4vtk5eXJ+3bt5ejR49KrVq1dAWYCsCsrUePHrJ7925dgRYXFye1a1u3sTTTMAE4q4SMBPn65Ne6kuxCyoVyHdsmuI2uIrut6W3i51nx3pMAAAAAHBfTMK1o5cqVpu2JEyeWuI8Krx566CE9PfPq1avy888/6+mU1nbTTTfpsEyFc2fOnLF6WAYAzkT9Dmdf3D69ouX6c+slOy/b4mO93b3l1ia36pCsfZ32YjAYbDpWAAAAANWPy07D3Lx5s7739/eXbt26lbrfoEGDTNtbtmyxSVhWeAEAFdABgCtKyUqR1adX65AsMjGyXMc2CWoio1uOlrsj7pYa3jVsNkYAAAAA1Z/LhmVqaqUSERGhe5aVpnXr1sWOsbZNmzbpezUONR4AcCXHE47rgGzN6TWSlpNm8XHuBncZ0miI7kXWq14vqsgAAAAAWIVLhmUZGRly5coVvW1uSVHVq0xVn6WmpuolSK1tzZo1cvDgQb19yy23SFBQUIXm55YlJiamwuMDAFvIzM2UdWfX6ZDswOUD5To2xC9E7m15r4xqMUpvAwAAAIA1uWRYlpycbNoOCAgwu39BWJaSkmLVcSQkJMjTTz+tt93d3eXf//53hc4THh5u1XEBgK1EX4uWZSeWycrIlZKYmViuY/s26KuryAaFDRIPN5f85wsAAACAHbhsZVkBLy8vs/t7e3vr+/T0dKuNITc3V8aOHSvnzp3Tf/773/8uXbp0sdr5AcBR5OTlyKbzm2TZ8WWy7eK2ch2r+o+NjBip+5E1CmpkszECAAAAgEuHZT4+PqbtrKwsixvw+/r6Wm0MkyZNku+//15v33HHHfKPf/yjwucyNz1UTcPs2bNnhc8PABURlxYny08ul+UnlktsWmy5ju1Ut5Ne0XJYk2F6hUsAAAAAsBeXDMsCAwNN25ZMrVRTMC2dsmmJ6dOny0cffaS3+/fvL19++aWehllR5vquAYC9GI1G2XFph64i2xi1UXKMORYf6+vhK3c2u1OHZK2CW9l0nAAAAABQGpetLKtTp45u8m+uOf7Vq1dNYZk1eoO9+uqr8p///Edvd+3aVVavXm3VijUAqApJmUmyKnKVfHniSzl77Wy5jo2oGaEDMhWUBXhZ55cSAAAAAFBRLhmWKW3atJHNmzdLZGSk5OTkiIdHyZfi2LFjRY6pjDlz5sjf/vY307l++OEHqVGjRqXOCQBV6fCVw7Lk2BL5/uz3eoVLS3m6ecrQxkN1SNYlpIsYDAabjhMAAAAALOWyYZma/qjCMlU1tmfPHunVq1eJ+23atMm03a9fvwo/36JFi2Ty5Ml6u1mzZvLjjz/q6jYAcDZp2Wk6HFt6fKkciT9SrmMbBjTUzfpHRIyQ2r61bTZGAAAAAKgolw3LRowYIa+88orenj9/folhWV5enixcuFBv16xZUwYPHlyh51qxYoVMnDhR9/JR/cV++uknadCgQSVfAQDY1+nE07LsxDL5JvIbSc5Otvg4N4ObDGw4UO5rdZ/0a9hP/xkAAAAAHJXL/o9FrQ45YMAAvf3JJ5/I9u3bi+3z5ptvytGjR/X2lClTxNPTs8jjCxYs0FOH1O3FF18s8XnWrVsnDzzwgOTm5kpISIiuKGvSpIlNXhMAWFt2brauInv4h4fl7lV3yxdHv7A4KAv2CZbHOjwm393zncy+ebYMCBtAUAYAAADA4blsZZnyzjvv6KmV6enpMmzYMJkxY4auHlN/XrJkiWnFypYtW8q0adPKff5ff/1VRo4cKVlZWTpo++9//yvZ2dly+PDhUo9RlWeqig0AqlJMSoxu1r/i5AqJz4gv17HdQ7vrXmQ3N7pZPN2L/pIBAAAAABydS4dlXbp0kaVLl8q4cePk2rVrOiy7kQrK1qxZI4GBgeU+//fffy9paWl6W4VkY8eONXuMmhI6YcKEcj8XAFRWnjFPtl7YKsuOL5NfLvyi/2ypAM8Auav5XXqqZfOazXkzAAAAADgtlw7LlOHDh8vBgwd1lZkKxc6fPy9eXl4SEREho0eP1k35/fz8qnqYAGAzCRkJ8vXJr3Ul2YWUC+U6tk1wG11FdlvT28TPk78rAQAAADg/g1F1nUe1pgLA8PBwvR0dHa2negJwbeqv/n1x+/SKluvPrZfsvGyLj/V295Zbm9yqQ7L2ddrrvo0AAAAAUF0yD5evLAMAV5KSlSKrT6/WIVlkYmS5jm0S1ERGtxwtd0fcLTW8a9hsjAAAAABQlQjLAMAFHE84rgOyNafXSFrO9V6KlnA3uMuQRkN0L7Je9XpRRQYAAACg2iMsA4BqKjM3U9adXadDsgOXD5Tr2BC/ELm35b0yqsUovQ0AAAAAroKwDACqmehr0bLsxDJZGblSEjMTy3Vs3wZ9dRXZoLBB4uHGPxEAAAAAXA//EwKAaiAnL0c2nd8ky44vk20Xt5XrWNV/bGTESN2PrFFQI5uNEQAAAACcAWEZADixuLQ4WX5yuSw/sVxi02LLdWynup30ipbDmgzTK1wCAAAAAAjLAMDpGI1G2XFph64i2xi1UXKMORYf6+vhK3c2u1OHZK2CW9l0nAAAAADgjKgsAwAnkZSZJKsiV8mXJ76Us9fOluvYiJoROiBTQVmAV4DNxggAAAAAzo6wDAAc3OErh2XJsSXy/dnv9QqXlvJ085ShjYfqkKxLSBcxGAw2HScAAAAAVAeEZQDggNKy03Q4tvT4UjkSf6RcxzYMaKib9Y+IGCG1fWvbbIwAAAAAUB0RlgGAAzmdeFqWnVgm30R+I8nZyRYf52Zwk4ENB8p9re6Tfg376T8DAAAAAMqPsAwAqlh2brb8FP2Tbti/69Kuch0b7BMso1qMkntb3isNAhrYbIwAAAAA4CoIywCgisSkxOhm/StOrpD4jPhyHds9tLvuRXZzo5vF093TZmMEAAAAAFdDWAYA5ZSSlSKxabG6r5ifp5+E+oVavMJknjFPtl7YqqvIfrnwi/6zpQI8A+Su5nfpqZbNazbnfQMAAAAAGyAsAwALGI1GPUVyyfElsiFqg+Qac02PuRvcZUijITKm1RjpUa9HiatOJmQkyNcnv9aVZBdSLpTrmrcJbqOryG5repsO5wAAAAAAtkNYBgBmqNUoZ26ZKZGJkSU+roKz9efW61tEzQiZ1X+WtK3dVgds++L26RUt1WPZedkWX2tvd2+5tcmtOiRrX6d9iQEcAAAAAMD6CMsAoAzbLm6TqRunSnpOukXXSQVqE76fICMiRuhKtNICttI0CWoio1uOlrsj7pYa3jV4bwAAAADAzgjLAKCMirLyBGUF1P6Ljy22eP+CaZyqF1mver2oIgMAAACAKkRYBgAlUFMo1dTL8gZl5RHiFyL3trxXRrUYpbcBAAAAAFWPsAwASlCRKZSW6tugr64iGxQ2SDzc+GsYAAAAABwJ/0sDgBKoVS+tSfUfGxkxUvcjaxTUiGsOAAAAAA6KsAwAbpCSlSIbojZY7bq80PsFuSviLr3CJQAAAADAsblV9QAAwNHEpsVKrjHXaufrFtqNoAwAAAAAnARhGQDcIC07zarXJDU7lWsMAAAAAE6CsAwAbgjK1pxeY9Vr4u/pzzUGAAAAACdBzzIAEJHM3ExZdnyZfHzoY0nISLDeX7IGDwnxC+EaAwAAAICTICwD4NKy87JlZeRK+fDAh7pXmbUNaTREArwCrH5eAAAAAIBtEJYBcEm5ebny3dnvZM7+ORKdHG2z5xnTeozNzg0AAAAAsD7CMgAuxWg0yoaoDfLe/vckMjHSps8VUTNCuod2t+lzAAAAAACsi7AMgMuEZNsubpPZ+2bLb/G/WXRM29pt5VTiKd3PrLx8PXxlVv9ZYjAYKjBaAAAAAEBVISwDUO3tjd0r7+57V/bE7rFo/w51OsgzXZ6R3vV7y/aY7TJ141RJz0kvV1D29uC3ddgGAAAAAHAuhGUAqi1VQaYqybZe2GrR/i1qtZBnOj8jN4XfZKoI69ugryy4dYHM3DLTommbauqlqigjKAMAAAAA50RYBqDaUVMn39v3nvwY9aNF+zcOaiyTOk2SW5veKm4Gt2KPq+BrxV0rZHfsbll8bLHueZZrzDU97mHw0Kteqmb+qkcZUy8BAAAAwHkRlgGoNtSqlh/s/0BWn14tRjGa3b+efz15qtNTclfzu8TDrey/DlUA1qNeD31LyUqRuLQ4Sc1OFX9PfwnxC5EArwArvhIAAAAAQFUhLAPg9GJTY+XDgx/K1ye/lhxjjtn9g32C5fGOj8volqPFy92r3M+ngjHCMQAAAACongjLADithIwE+eTQJ7Lk2BLJyssyu3+QV5BMbD9R/tj6j+Ln6WeXMQIAAAAAnAthGQCncy3rmnz222fy+ZHPJS0nzez+fh5+Mq7tOBnfbrwOzAAAAAAAKA1hGQCnkZadJv879j+Zf3i+DszM8XLz0k33H+nwiJ56CQAAAACAOYRlABxeVm6WfHniS5l3cJ7EZ8Sb3V+tTnlPi3t0X7JQ/1C7jBEAAAAAUD0QlgFwWDl5ObIqcpXMPThXLqVeMru/QQxyZ7M75anOT0l4YLhdxggAAAAAqF4IywA4nDxjnnx/5nt5f//7EpUcZdExQxsPlac7Py3Naza3+fgAAAAAANUXYRkAh2E0GmVj9EZ5b/97cvLqSYuO6d+wvzzT5RlpW7utzccHAAAAAKj+CMsAOERItj1mu7y37z05dOWQRcd0C+0mz3Z5VrqGdrX5+AAAAAAAroOwDECV2h+3X97d967surTLov3b1W6nQ7I+DfqIwWCw+fgAAAAAAK6FsAxAlTgaf1Rm75stmy9stmj/iJoRMrnLZBkSPoSQDAAAAABgM4RlAOzqdOJp3ZNs/bn1Fu2vVrVUjftvbXKruLu523x8AAAAAADXRlgGwC7OJ5+XDw58IKtPr9arXZoT6hcqT3Z6Uu6OuFs83TztMkYAAAAAAAjLANhUXFqcfHTwI1l+crnk5OWY3T/YJ1ge6/CYjG41WrzdvXl3AAAAAAB2RVgGwCauZlyVTw9/KouPLZbM3Eyz+wd6BcrEdhNlbJux4ufpx7sCAAAAAKgShGUArCo5K1kWHlkoi44sktTsVLP7+3r4yrg242RC+wkS5BXEuwEAAAAAqFKEZQCsIj0nXVeRqWqypMwks/t7uXnJ/a3vl0faPyK1fWvzLgAAAAAAHAJhGYBKycrNkq9OfCXzDs2TK+lXzP+lY/CQES1GyBMdn5B6/vW4+gAAAAAAh0JYBqBCVLP+b099q1e4jEmNMbu/QQxyR7M7ZFKnSRIeFM5VBwAAAAA4JMIyAOWSZ8yTdWfXyfv735ez185adMzNjW6Wpzs/LS1qteBqAwAAAAAcGmEZAIsYjUbZdH6TvLfvPTl+9bhFx/Rr0E+e6fKMtKvTjqsMAAAAAHAKhGUAzNoRs0Pe3feuHLx80KKr1TWkqw7JutfrztUFAAAAADgVwjIApTpw+YDM3jtbdlzaYdFValu7rQ7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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 432, - "width": 613 + "height": 431, + "width": 1233 } }, "output_type": "display_data" @@ -1034,10 +1024,11 @@ " )\n", " prior_comparison[name] = df\n", "\n", - "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "fig, ax = plt.subplots(figsize=(15, 4.5))\n", "for name, df in prior_comparison.items():\n", " ax.plot(df[\"N\"], df[\"assurance\"], marker=\"o\", linewidth=2, label=name)\n", "ax.set_xscale(\"log\")\n", + "ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{int(x):,}\"))\n", "ax.set_xlabel(\"Sample size per arm\")\n", "ax.set_ylabel(\"Assurance\")\n", "ax.set_title(\"Assurance under three prior commitments\")\n", @@ -1053,11 +1044,11 @@ "\n", "## Planning as a posterior over future posteriors\n", "\n", - "The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty, and a fair one, because the lights are the only ones we have.\n", + "The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty.\n", "\n", "Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. It makes explicit the assumption power leaves buried: a believed distribution of effects. Naming that assumption, and giving it a posterior, is the move this sequence repeats.\n", "\n", - "In each case here, the Gaussian and the Bernoulli, the inference machinery was the same. What changed was the likelihood — and with it, the ceiling that the likelihood's geometry sets on how much assurance any sample size can buy. The same is true across this notebook and its successors. Planning, interpretation, synthesis are three questions the same machinery answers in turn; the posterior is the constant, the question changes. We have just seen the first of the three.\n", + "In each case here, the Gaussian and the Bernoulli, the inference machinery was the same. What changed was the likelihood — and with it, the ceiling that the likelihood's geometry sets on how much assurance any sample size can buy. The same is true across this notebook and its successors. Planning, interpretation, synthesis: three problems, one machinery. The problem changes; its proper characterisation is always a posterior. We have just seen the first of the three.\n", "\n", "## Authors\n", "\n", @@ -1089,7 +1080,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Mon, 01 Jun 2026\n", + "Last updated: Tue, 02 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.13.13\n", diff --git a/examples/causal_inference/assurance_planning_simulation.myst.md b/examples/causal_inference/assurance_planning_simulation.myst.md index c40afae96..c3e67000c 100644 --- a/examples/causal_inference/assurance_planning_simulation.myst.md +++ b/examples/causal_inference/assurance_planning_simulation.myst.md @@ -20,13 +20,13 @@ kernelspec: :author: Nathaniel Forde ::: -Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records — an off-kilter orientation in pursuit of theory, anomalous findings and the next question. +Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records, orientated at a new angle in pursuit of theory, anomalous findings and the next question. ## The question planners are actually asking -When we pursue an experiment, we implicitly assume the experiment will be useful. Our questions are "operationalised" as power: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold. +When we experiment, we implicitly assume the experiment will be useful. Our questions are "operationalised" as power heuristics: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold. -An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the conclusion is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery answers all three questions; the question being asked of it is what changes. +An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery serves all three; what changes is the problem put to it. :::{admonition} Where this lands in regulatory practice :class: note @@ -41,6 +41,7 @@ from dataclasses import dataclass import arviz as az import matplotlib.pyplot as plt +import matplotlib.ticker as mticker import numpy as np import pandas as pd import pymc as pm @@ -82,13 +83,13 @@ $$ \text{Power}(N) = P\!\left(\text{reject } H_0 \mid \theta = \theta_0,\; N\right). $$ -The conditioning bar is the load-bearing part. The planner who has confidently named $\theta_0$ has implicitly used up the uncertainty that motivated the experiment in the first place. Assurance refuses the substitution. Instead it integrates over the planner's prior beliefs about the effect: +The conditioning bar is the load-bearing part. The planner who has confidently named $\theta_0$ has implicitly used up the uncertainty that motivated the experiment in the first place. Assurance refuses the substitution. Instead it integrates over the planner's prior beliefs about the effect. The decision rule is whether the posterior probability of a positive effect clears a threshold $c$: $$ -\text{Assurance}(N) = \int P\!\left(\text{decision} \mid \theta,\; N\right)\, \pi(\theta)\, d\theta. +\text{Assurance}(N) = \mathbb{E}_{\substack{\theta \,\sim\, \pi(\theta) \\ \hat{d} \,\sim\, p(\hat{d} \mid \theta,\, N)}}\!\left[\mathbb{1}\!\bigl(P(\theta > 0 \mid \hat{d},\, N) > c\bigr)\right]. $$ -This is the marginal probability of a successful decision, where "successful" is whatever decision rule the team has agreed in advance and $\pi(\theta)$ is the team's prior over the effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in. +The outer expectation averages over effect sizes drawn from the prior; the inner averages over data generated under each effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in. ## The simulation engine @@ -168,7 +169,7 @@ pc = az.plot_dist(idata_gauss_demo, var_names=["delta"]) ax = plt.gcf().axes[0] ax.axvline(0.0, color="C2", linestyle="--", label="ref = 0.0") ax.legend() -ax.set_title(r"Posterior on $\delta$ at $N=200$ (one simulated dataset)") +ax.set_title(r"Posterior on $\delta$ at $N=200$ (one simulated dataset)"); ``` The posterior concentrates above zero; under the decision rule the team would conclude the new flow lifts revenue. That is a single draw of the experiment. Assurance asks how often this conclusion would arrive if we re-ran the experiment many times under our actual prior. @@ -254,7 +255,7 @@ power_at_pessimistic = np.array( prior_prob_positive = 1.0 - stats.norm.cdf(0.0, EFFECT_PRIOR.mu, EFFECT_PRIOR.sigma) -fig, ax = plt.subplots(figsize=(7, 4.5)) +fig, ax = plt.subplots(figsize=(15, 4.5)) ax.plot(N_GRID, assurance_df["assurance"], marker="o", linewidth=2, label="Assurance (Bayesian)") ax.plot( N_GRID, @@ -287,6 +288,7 @@ ax.axhline( label=rf"Assurance ceiling $P(\delta>0)$ = {prior_prob_positive:.2f}", ) ax.set_xscale("log") +ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{int(x):,}")) ax.set_xlabel("Sample size per arm") ax.set_ylabel("Probability of declaring uplift") ax.set_title("Assurance vs frequentist power, Gaussian outcome") @@ -388,7 +390,7 @@ power_bern_at_pessimistic = np.array( ] ) -fig, ax = plt.subplots(figsize=(7, 4.5)) +fig, ax = plt.subplots(figsize=(15, 4.5)) ax.plot( N_GRID_BERN, assurance_bern_df["assurance"], @@ -419,7 +421,16 @@ ax.plot( alpha=0.7, label=r"Power at $\mu - \sigma$", ) +ax.axhline( + prior_prob_positive, + color="C0", + linestyle="-.", + alpha=0.6, + label=rf"Assurance ceiling $P(\delta>0)$ = {prior_prob_positive:.2f}", +) + ax.set_xscale("log") +ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{int(x):,}")) ax.set_xlabel("Sample size per arm") ax.set_ylabel("Probability of declaring uplift") ax.set_title("Assurance vs frequentist power, Bernoulli outcome") @@ -454,7 +465,6 @@ with bernoulli_two_arm_model( idata_bern_demo = pm.sample( draws=1000, tune=1000, - chains=2, target_accept=0.95, random_seed=RANDOM_SEED, progressbar=False, @@ -464,7 +474,7 @@ az.plot_dist(idata_bern_demo, var_names=["lift"]) ax = plt.gcf().axes[0] ax.axvline(0.0, color="C2", linestyle="--", label="ref = 0.0") ax.legend() -plt.title(r"Posterior on conversion lift at $N=4000$") +plt.title(r"Posterior on conversion lift at $N=4000$"); ``` The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The decision rule is the same; the ceiling is lower because the planning prior carries a smaller probability that the lift is positive, and that probability — not the sample size — is what sets the asymptote. @@ -495,10 +505,11 @@ for name, prior in [ ) prior_comparison[name] = df -fig, ax = plt.subplots(figsize=(7, 4.5)) +fig, ax = plt.subplots(figsize=(15, 4.5)) for name, df in prior_comparison.items(): ax.plot(df["N"], df["assurance"], marker="o", linewidth=2, label=name) ax.set_xscale("log") +ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{int(x):,}")) ax.set_xlabel("Sample size per arm") ax.set_ylabel("Assurance") ax.set_title("Assurance under three prior commitments") @@ -509,11 +520,11 @@ The three curves disagree most where they should: at small sample sizes where th ## Planning as a posterior over future posteriors -The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty, and a fair one, because the lights are the only ones we have. +The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty. Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. It makes explicit the assumption power leaves buried: a believed distribution of effects. Naming that assumption, and giving it a posterior, is the move this sequence repeats. -In each case here, the Gaussian and the Bernoulli, the inference machinery was the same. What changed was the likelihood — and with it, the ceiling that the likelihood's geometry sets on how much assurance any sample size can buy. The same is true across this notebook and its successors. Planning, interpretation, synthesis are three questions the same machinery answers in turn; the posterior is the constant, the question changes. We have just seen the first of the three. +In each case here, the Gaussian and the Bernoulli, the inference machinery was the same. What changed was the likelihood — and with it, the ceiling that the likelihood's geometry sets on how much assurance any sample size can buy. The same is true across this notebook and its successors. Planning, interpretation, synthesis: three problems, one machinery. The problem changes; its proper characterisation is always a posterior. We have just seen the first of the three. ## Authors diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.ipynb b/examples/causal_inference/multiple_experiments_meta_analysis.ipynb index e44fd51f8..a2b23e8ac 100644 --- a/examples/causal_inference/multiple_experiments_meta_analysis.ipynb +++ b/examples/causal_inference/multiple_experiments_meta_analysis.ipynb @@ -694,7 +694,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -1008,15 +1008,15 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { "height": 1066, - "width": 1532 + "width": 3967 } }, "output_type": "display_data" @@ -1043,7 +1043,7 @@ " }\n", ").round(3)\n", "\n", - "fig, ax = plt.subplots(figsize=(7.5, 5.5))\n", + "fig, ax = plt.subplots(figsize=(20, 5.5))\n", "y_pos = np.arange(K)\n", "ax.errorbar(\n", " forest[\"no_pool_mean\"],\n", @@ -1108,22 +1108,22 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { "height": 1023, - "width": 1771 + "width": 4023 } }, "output_type": "display_data" } ], "source": [ - "fig, ax = plt.subplots(figsize=(8, 5))\n", + "fig, ax = plt.subplots(figsize=(20, 5))\n", "for k in range(K):\n", " ax.plot([0, 1], [no_pool_mean[k], partial_pool_mean[k]], color=\"grey\", alpha=0.5, zorder=1)\n", " ax.scatter(0, no_pool_mean[k], color=\"C0\", zorder=3, s=55)\n", @@ -1369,15 +1369,15 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
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" ] }, "metadata": { "image/png": { "height": 923, - "width": 1523 + "width": 4023 } }, "output_type": "display_data" @@ -1391,7 +1391,7 @@ "s_new = np.median(se_obs)\n", "d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples))\n", "\n", - "fig, ax = plt.subplots(figsize=(7.5, 4.5))\n", + "fig, ax = plt.subplots(figsize=(20, 4.5))\n", "bins = np.linspace(\n", " min(d_hat_new_samples.min(), theta_new_samples.min()) - 0.2,\n", " max(d_hat_new_samples.max(), theta_new_samples.max()) + 0.2,\n", @@ -1502,7 +1502,7 @@ "\n", "A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population is not a summary. It is the population our experiments were drawn from, made audible.\n", "\n", - "Replication is often described as a verification protocol: a post-hoc check. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The triptych closes here, though \"closes\" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three questions, one machinery. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior — something to argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The posterior is the constant; the question changes.\n", + "Replication is often described as a verification protocol: a post-hoc check. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The triptych closes here, though \"closes\" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three problems, one machinery. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior — something to argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The problem changes; its proper characterisation is always a posterior.\n", "\n", "And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\\theta_{\\text{new}} \\sim \\mathcal{N}(\\mu, \\tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it — each notebook's synthesis becoming the next notebook's prior, each conversation overheard becoming the next conversation's opening question.\n", "\n", @@ -1536,7 +1536,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Mon, 01 Jun 2026\n", + "Last updated: Tue, 02 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.13.13\n", diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md index 43f2d40a6..cb225be96 100644 --- a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md +++ b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md @@ -363,7 +363,7 @@ forest = pd.DataFrame( } ).round(3) -fig, ax = plt.subplots(figsize=(7.5, 5.5)) +fig, ax = plt.subplots(figsize=(20, 5.5)) y_pos = np.arange(K) ax.errorbar( forest["no_pool_mean"], @@ -408,7 +408,7 @@ plt.tight_layout(); The partial-pooling estimates are pulled toward the population mean: the canonical *shrinkage* picture {cite:p}`gelman2006multilevel`, {cite:p}`gelman2020regression`. The pull is strongest for the markets whose individual estimates are noisiest (widest no-pooling intervals) or most extreme; it is weakest for markets whose estimates are tight and central. This is the data-driven version of "borrowing strength" that vote-counting cannot do and complete-pooling does only by force. ```{code-cell} ipython3 -fig, ax = plt.subplots(figsize=(8, 5)) +fig, ax = plt.subplots(figsize=(20, 5)) for k in range(K): ax.plot([0, 1], [no_pool_mean[k], partial_pool_mean[k]], color="grey", alpha=0.5, zorder=1) ax.scatter(0, no_pool_mean[k], color="C0", zorder=3, s=55) @@ -537,7 +537,7 @@ theta_new_samples = rng_pp.normal(mu_samples, tau_samples) s_new = np.median(se_obs) d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples)) -fig, ax = plt.subplots(figsize=(7.5, 4.5)) +fig, ax = plt.subplots(figsize=(20, 4.5)) bins = np.linspace( min(d_hat_new_samples.min(), theta_new_samples.min()) - 0.2, max(d_hat_new_samples.max(), theta_new_samples.max()) + 0.2, @@ -582,7 +582,7 @@ The probability the true effect is positive in the next market is higher than th A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population is not a summary. It is the population our experiments were drawn from, made audible. -Replication is often described as a verification protocol: a post-hoc check. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The triptych closes here, though "closes" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three questions, one machinery. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior — something to argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The posterior is the constant; the question changes. +Replication is often described as a verification protocol: a post-hoc check. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The triptych closes here, though "closes" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three problems, one machinery. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior — something to argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The problem changes; its proper characterisation is always a posterior. And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\theta_{\text{new}} \sim \mathcal{N}(\mu, \tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it — each notebook's synthesis becoming the next notebook's prior, each conversation overheard becoming the next conversation's opening question. diff --git a/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb b/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb index 4fb00cbe1..b6bbc4d86 100644 --- a/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb +++ b/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb @@ -14,6 +14,8 @@ ":author: Nathaniel Forde\n", ":::\n", "\n", + "All applied inference is argument. Against every experiment you can set the contention that the working conditions were imperfect. Some aspect of the evaluation was flawed. Maybe treatment assignment introduced a subtle kind of bias, or the subjects didn't comply fully with the design. Against every experiment you can contrast the scientific ideal of perfect randomisation and clear adherence. This contrast is just due diligence and sensitivity analysis varies that contrast against the ideal limit case. \n", + "\n", "## The randomisation gap\n", "\n", "Randomisation is an assumption you maintain by design, not a fact you assert by intention. The checkout experiment ran, the headline effect on revenue-per-visitor looks positive, and somewhere in the diagnostic notes there is a line about compliance being 73%: users on slow connections were silently routed back to the old flow by a CDN edge case, and the routing was not random in the way it touched the population. The intent-to-treat estimate now depends on a counterfactual the experiment did not produce: what those users would have done under the new flow. Without further assumptions, the data cannot say what the treatment effect would have been if randomisation had held.\n", @@ -33,10 +35,10 @@ "id": "3abbded2", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:15:59.450621Z", - "iopub.status.busy": "2026-06-01T17:15:59.450509Z", - "iopub.status.idle": "2026-06-01T17:16:02.393024Z", - "shell.execute_reply": "2026-06-01T17:16:02.392147Z" + "iopub.execute_input": "2026-06-02T17:35:59.159236Z", + "iopub.status.busy": "2026-06-02T17:35:59.159124Z", + "iopub.status.idle": "2026-06-02T17:36:01.747061Z", + "shell.execute_reply": "2026-06-02T17:36:01.746567Z" } }, "outputs": [], @@ -63,10 +65,10 @@ "id": "f669edc2", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:02.394961Z", - "iopub.status.busy": "2026-06-01T17:16:02.394693Z", - "iopub.status.idle": "2026-06-01T17:16:02.734556Z", - "shell.execute_reply": "2026-06-01T17:16:02.733954Z" + "iopub.execute_input": "2026-06-02T17:36:01.748523Z", + "iopub.status.busy": "2026-06-02T17:36:01.748381Z", + "iopub.status.idle": "2026-06-02T17:36:02.093165Z", + "shell.execute_reply": "2026-06-02T17:36:02.092571Z" } }, "outputs": [], @@ -93,10 +95,10 @@ "id": "dc1a9456", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:02.736974Z", - "iopub.status.busy": "2026-06-01T17:16:02.736741Z", - "iopub.status.idle": "2026-06-01T17:16:03.716153Z", - "shell.execute_reply": "2026-06-01T17:16:03.715518Z" + "iopub.execute_input": "2026-06-02T17:36:02.095069Z", + "iopub.status.busy": "2026-06-02T17:36:02.094831Z", + "iopub.status.idle": "2026-06-02T17:36:02.928579Z", + "shell.execute_reply": "2026-06-02T17:36:02.928074Z" }, "tags": [ "hide-input" @@ -105,22 +107,22 @@ "outputs": [ { "data": { - "image/png": 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Tffr0MXstSN++fVPH9a233opvfvObqeO9Pv/4xz9i0KBBMWbMmIzVCPyfZR98EG9fc008ccgh8czRR8f7f/97rM7wYhCdttgitvnhD+Pgxx+Pfe+5JzY/4YRo17OnwwQAAAAALcSDDz4Yc+fOXWusS5cuMWzYsKzVBDQ/S5cuTTv+RQtiZ3qf5I6jjz661tjTTz8dH3zwQVbqAQAAAIBM6LrttrH9+efHsKefjr3/8Y/Y7Pjjo7Bz54xO/uI33og3LrssHt1nn5h44onx8T33xJolSzJaA/A/9PuR0O8HAAAAwIYSPA8AAADQis2fPz8VJD9q1KiYPXv2ercdMWJEvPLKK3HbbbfFFltskbEaybwtt9wyFSr/8ssvx/Dhw9e77axZs6K0tDROPPHE1OcJaFqr5s1LBcxPOOaYePyQQ+KdP/4xln34YUanve0mm8QWJ58c+91/fxz06KPx5XPOiU5f+lJGawAAAAAAMuO+++6rNXbwwQdH27ZtHQKg3qqqqprFPskd22+/fXxpne+fampqYvTo0VmrCQAAAAAyJa+gIHrtvXfsfNllcdjzz8fg666LfoceGnmZXHizpibmPvdcvHLeefHInnvGlHPOiVmPPRbVq1dnrgZopfT7kY5+PwAAAAAaqrDBrwAAAACgRSgrK4vTTz/9CwPnhw4dGpdffnnst99+GauN3LDzzjvHAw88EE8//XScf/75MXHixDq3TYLqx48fH3/+85+jpKQko3VCS1e5YkXMfvTRKC8rizkTJkRNFoJUCjp0iH6HHx7FJSXRa599Ir/Q1wsAAAAA0NLNmDEjnn/++Vrjhx9+eJO+bxImPWXKlNR7v/7661FeXh6ffPJJLF++PPV8p06donPnzrHZZpvFwIEDY4cddkh9n9WnT5+Net+FCxfGiy++GFOnTo3p06fHBx98kAp1WLZsWaxatSr1nt26dUs9ttlmm9h9991Tj+Li4miNkgWrJ02aFG+++WZMmzYtNX+fzlW7du2iffv20bVr1xgwYEAUFRXFVlttlfruadCgQY2ycIHj1XArV66MZ555Jl544YV44403oqKiIjWPyXibNm2iS5cusemmm6aO0V577RX777+/RSaawRw21TUzuR7ec889n/39wzoWQv6v//qv6NixY71qHTZsWOpRX8n/N3/5y19q3efwgx/8IPLy8uq9HwAAAABozgratYsBRx6ZeqxeuDBmPPhgVIwZE/NfeCFjNVSvWpV63+TRpkePGDB8eBSXlkaP3XbzuzpoZPr9+CL6/QAAAACor7yampqaem8NAAAAQLOXBGR8//vfj9tvv3292yWhGX/84x/jpJNOciMokfwa8dZbb40f/vCHsWjRovXOyAknnJD67PTs2dPMwQaqrqyMuRMnRsXo0THz0UejatmyjM9lXkFB9N533ygqLY1+w4ZFYadOGa8BAAAAAHLVwQcfnAoczrQkPDgJPM7Pz2/y90oWm73qqqvWGuvQoUMq3LgpgpTnzJkTt912W/zrX/9KfZ/VUNttt1185StfidLS0ujevXu9XvPuu+/Ggw8+GE888US89dZbqe9DGmrfffdNLfY8ZMiQ2FgnnnhirbD/ZL/JvGyMyZMnp77zW9ff//732HPPPeu9n8rKytRixHfccUedIdRfJAmlTxa8PvLII+Ooo45q0Gtz7Xg11rw2tY8//jj++7//O8aOHftZGHl9JAsHfOMb34hTTz01FaheX8mC1vfff380lmQ+k/019j4bcowyPYe5cM2877774oILLojGdPbZZ8c555xT7+3/85//xHHHHZfz5xgAAAAAZMPy8vJUAH15WVksfe+9rNTQcbPNomjkyNT95l0GDsxKDdBS6PdjQ+j3AwAAAGB9Ctf7LAAAAAAtSllZWSpIYfbs2evdbvjw4fGXv/wlioqKMlYbuS0vLy9OPvnkOPTQQ+O73/1uKtSjLkngyPjx41OBNCUlJRmtE5r7DZ+LXn89dfP/jHHjYtWcOVmpo/vOO0dRSUkUHXVUtOvVKys1AAAAAEAumzt3blZC5xM77rhjRkLnE8nv+tc1ePDgRg+dr6qqiptuuiluuOGGBgU6r+vNN99MPZLvw5LA5C/y1a9+NV555ZXYWM8880zqMWzYsLjiiitSiwO0REnQ+09+8pN45513Nmo/q1atSn22nn766QYFzzteDbdmzZr405/+FH/9619Tf26oxYsXx4033hj33ntv/Pa3v40DDjggWptcnMNsXTOzYYcddkiF9yfz+HnJNUTwPAAAAACtXcfi4tj6zDNjq+99Lxa/8UbqHvQkiD6T96Av/+ijePdPf0o9uu24YxSXlsaAo46K9r17Z6wGaAn0+7Gh9PsBAAAAsD6Z6TwBAAAAIKuWLVsW3/rWt2LUqFHrDZ3v1q1b3HLLLTFu3Dih86SVLEaQfD6Sz0nyeanLrFmzorS0NPW5Sz5/QN2Wf/xxvHPddfHk4YfHhFGj4v1bbsl46HzHzTaLL59zThw0fnzsd999seXJJwudBwAAAIA6vPrqq1mbm5133jkj7zN//vx4/fXXa403dtBv8j7JdwlXXnnlRgUorxvKXB8ffPBBNKYkCPlrX/talJeXR0vzn//8J0466aSNDp3fGI5Xw8yZMydOPPHEVDj5hgSmr7uvM844I2677bZoTXJxDrN5zcyGZKGV3XffvdZ4snAFAAAAAPB/ocPdBg2KQRdeGIc++2zs9fe/R/HRR0dBp04ZnaJFr70WU3/zm3h06NCYdPLJ8fH990elXhJYL/1+NBb9fgAAAACkU5h2FAAAAIAW46OPPkoFgL/yyivr3W748OHxl7/8ReA89box+eSTT45hw4bFd7/73XjooYfq3Pbvf/97vPbaazF69OjYbLPNzC78r9ULFsSMBx+M8tGjY8FLL2VlXtr27BkDRoyI4tLS6L7LLqlzGwAAAACoXwh3tuy0004ZeZ8JEyZEdXV1rfEhQ4Y02nskiyUn3zdMnz59vdt17do1Nt988+jZs2d07tw5FcCwcOHC1OsWLVoUTaF3795RXFwcXbp0Sb1/ZWVlLF26NBUq/+GHH0ZNTU3a17377rtxzjnnxD//+c9o27ZttATJv/vMM89c71wnc/SlL30p+vTpEx06dEjNz5IlS1KP999/P3W8mpLjVfvcShYKWF9YfxLovdVWW6WOWffu3VMh5nPnzo0333wzbch6cj34zW9+kzq+xx57bLR0uTiHuXzNbEp77bVXPP7442uNJccluRYn1x0AAAAA4P/kFRRE7332ST12/NWvYvZjj0VFWVl88vTTUVNZmZmpqq6OORMmpB6vdegQ/Q49NIpKSqL3vvtGfps2Dhf8L/1+NDb9fgAAAACsS/A8AAAAQAv2zDPPxNFHHx1z5sypc5tu3brFH/7wh/jWt74ldJgGSQJXHnjggfjb3/4WP/rRj+oMKnj55Zdjjz32iHvvvTf23Xdfs0yrVbVyZerm/fLk5v2nnsrczfufk9++ffQbNiyKR41y8z4AAAAAbKBXX301a3O38847Z+R9nn/++VpjhYWFsf322zfK/pOA5jPOOKPOAOU2bdrE8ccfn1o4edddd42CgoI6AxkmT56cWiR30qRJUVVVtUH1DBgwIA455JDYf//9Y9CgQbHJJpvUue3ixYvj0Ucfjdtvvz2mTp1a6/k33ngjrrjiirj44oujJbjmmmvSfteYHKNjjjkmjjvuuNScrW9x0yQwO1kk+4knnognn3wyFixYsFE1OV51W7FiRercqiswPbmGJOHl++23X2phhXUlIeVJyPe1116bCvZe169//evUOTlw4MA6axg8eHCtsWRfL6VZiPfwww+Pjh07rudfFNGrV68m2Wcuz2EuXDOT+wySx6fuu+++uOCCC2pt99hjj6W+N24qO+ywQ9rx5N8heB4AAAAA6lbYoUMUHXVU6rFq/vyY+eCDUT56dCx4+eWMTVvVihVRMWZM6tG2Z8//qae0NLrvvLP+JVo1/X40Jf1+AAAAAHwqr6ampuazvwEAAADQYvz1r3+NM888M9asWVPnNknj+V/+8pcoKirKaG20POXl5fHd7343FVJQlyTw4IYbbojvfOc7Ga0NsqmmqirmTpoUFWVlMfPhh6Ny6dLMF5GfH72GDo3i0tLof9hhUdi5c+ZrAAAAAIBWLFmY9cILL6w1fuedd8Zuu+0WuaikpCTefvvttca22mqr1IK0jeGiiy6Ke+65J+1ze++9dyqYedNNN23QPj/55JO46aabUgsD3HXXXV+4ffI+yfwn31tsyHGorq6Of/zjH6mQ+cp1FhrNz8+PRx55pMH/hhNPPLFW6P+QIUPitttui42RBDSfdNJJtcb//ve/x5577lnn65J/1z777BMLFy5cazwJ206+i9xll10aXEvy3WXyfVJyjO644456vy4Xj9eGzmtTShY8uPvuu2uNJ0Hsv/zlL6O0tLTexyk5D//5z3/Wem7bbbdNnb/Jd3/11RSh5U0VhJ6Lc5gL18xsBc8vXbo0dt9991i37SUJ2k/+3QAAAABAwyz78MNUEHx5WVkse//9rExfpy99KYpGjYrikpLotPnmWakBskW/H5mk3w8AAACgdcvPdgEAAAAANK6kgf373/9+nHbaaXWGznfr1i1uueWWGDdunNB5GkUSJpCEzdx8883RtWvXOj+bp556aurzuW6gB7QkSfDFojfeiKmXXhrj99svJp10Unx8770ZD53vNmhQbH/RRXHos8/G3rfeGpsefbTQeQAAAADIgjfffLPWWBJ0nQQP56KVK1fGe++9V2t8m222aZT9J2HddQUojxgxIv77v/+7wQHKiT59+qRCkf/2t7/Va/t//etf8ac//WmDw/+TY5iEjv/ud7+LvLy8tCHnzd3LL79cK3Q+cd55521Q6HwiCdpOFjZoSOh8wvH6Yi+++GLawPRkoYBkoYv6BqZ/epx+9atfpRZDWNdbb70VDz74YLREuTiHuXLNzJbOnTunDbZ/7bXXslIPAAAAADR3Sej7l885Jw569NHY7/77Y4uTT462m2yS8fD7d/74x3j8kENiwjHHxPt//3usmjcvozVApun3Ixv0+wEAAAC0boLnAQAAAFqQefPmxRFHHBHXXnttndsMGjQopkyZEieffHKtEAzYGMnn6dvf/na89NJLsf3229e5XfL5TD6n8+fPN+G0KMsrKuLdG26Ip448Mp4eOTKm33RTrJw9O6M1dCgujq3PPDMOfPjh2H/MmBh4yinRvk+fjNYAAAAAAKxt6tSptaZk8803j44dO+bkVL3zzjtRVVVVa3zLLbdslP1feeWVaccHDx4cv//971NhzRujffv29douXZDxhhg+fHiMGjWq1vi9yYKkzXwh3o8++qjWWGFhYYwcOTLjtTheG3ZuJd/f/fGPf9zghS6SRQZ23HHHWuO33nprtES5OIe5cs3MpoEDB9Yae/fdd1MhPQAAAADAhkl+99l9p51ih4svjkOfey72vPnmKBo1Kgo6dMjolC585ZV4/ZJL4tG9947J3/lOVIwZE5XLl2e0Bmhq+v3IJv1+AAAAAK2X4HkAAACAFhRaMmTIkHj88cfr3KakpCQmTpyYtjEbGkvy+Uo+Z+sLHXnsscdSn9d0YTvQnKxetCg+vPPOePbrX4/H9t8/3vr972PJu+9mtIY23brFl77xjdjnn/+MQ558Mrb98Y+jy1ZbZbQGAAAAACC9mpqaeOutt2qNr28B12z7+OOP04737dt3o/f9yiuvxKuvvlprPAnhv+KKKyI/v3ne2nzGGWfUGluyZEnaY9/cQjDW1aNHj2YRVN3ajtd//vOf1OLQ6xoxYkTss88+G7zfJNT8hz/8Ya3x5Du+l19+OVqSXJzDlnrNbKh0//8kC3vMmjUrK/UAAAAAQEuTX1gYfQ44IHa78so4bPLk2PWqq1J/zysoyFgNNVVV8cmTT8ZLP/pRPLLnnvHyj38cn0yYENXNfJFf0O9HrtDvBwAAAND6tI47jQEAAABauLFjx8Zee+0V06dPr3Obiy66KO6///7o0qVLRmujderatWuMHj06Lrzwwjq3mTZtWuy9996pzy80J1WrVsWMf/87Xvje9+LRvfaK//zsZzH/+eczWkN+27bRf/jw2OPPf47DJk2KnX796+i5++6Rl5eX0ToAAAAAgPV7//33Y/ny5bXGBw0alLNTV1FR0WTB88l3Vekcd9xxsemmm0Zztfnmm6ce60oXGN2cFBYW1hpbvHhxrF69Opqzlni87rvvvrTjZ5999kbve9999007X8lC1C1JLs5hS71mNlSfPn3SjpeXl2e8FgAAAABo6Qo7dYri0tLY8+ab49DnnotBF18c3XfeOaM1VC1fHuWjR8fkk0+O8fvsE6//5jex8D//SS34DM2Jfj9yjX4/AAAAgNZF8DwAAABAM5bcNHnZZZdFaWlpLF26NO02HTp0iLvuuit+85vfRH6+XweROcnn7be//W3ceeed0b59+7TbLFmyJPX5vfzyy90ETE6rqa6OuZMmxasXXBCP7LlnTDnrrJj1yCNRnclwnby86LX33rHzFVfEYc8/H7tfe230GzYsFUIPAAAAAOSmN998M+349ttvH7lqxowZDQr+bYinnnqq1liyoObXv/71aO4222yzWmOvv/56NGe9evWqNbZq1ap44IEHorlracfrySefTHud2WKLLRpl/3vuuWetsSlTpkRLkotz2JKvmQ1R18Indf1/BQAAAAA0jna9esWWJ58c+913Xxw0fnx8+ZxzomOa3683pVVz58b7t9wSE77ylXjisMPineuui2UffZTRGqCh9PuRy/T7AQAAALQeksYAAAAAmvFNaOeee25ceOGFdQZ2FxcXxzPPPBNf/epXM14ffOprX/ta6nOYfB7TST6/F1xwQfz4xz8WPk/OWfz22/Hmf/1XjN9//5j4zW/GR3ffHZVLlmS0hq7bbRfbn39+DJswIfb+xz9is2OPjTZdumS0BgAAAABgw0ydOrXZBc8ni8am06lTp43ab0VFRcycObPW+DbbbNNowc7Z1KNHj1pj8+bNi+Zs1113TTv+q1/9Kh577LFozlrS8froo4/Snlv77bdfo73HDjvsUGvslVdeiZYiF+ewpV8zG6Jjx44N+v8KAAAAAGh8nbfYIrb54Q/j4Mcfj33vuSc2P/HEaNuzZ0anetn06fH21VfH4wcdFM8cd1x8cPvtsXrBgozWAF9Evx/NhX4/AAAAgJavMNsFAAAAANBw1dXVcdZZZ8WNN95Y5zZDhw6N++67L/r27WuKybrBgwfHCy+8EEcffXRMnDgx7TZXX311rFy5Mq677rrIz7dmJtmzYubMqBg7NirKymLxW29lpYb2/ftHcWlpFJWURNdttslKDQAAAADAxnvzzTdrjW266abRtWvXnJ3e5cuXpx1v165do89FYuedd45ckQSPJ99jvPPOO/H222/Hxx9/HMuWLUs9knmpqqpq0P4WL14czVnyWR00aFCtBRSSuTjzzDNjyJAhcfzxx8dBBx0UnTt3znh9jtf6F7gYOHBgkwb1L126NPXIxrFvDXPYHK6ZmdK+ffu04ytWrMh4LQAAAADQ2uXl5UWPXXdNPQZddFHMeeaZKB89OmaNHx/VK1dmrI4FL72Uerz+q19FnwMOSN173/eQQ6Kgjt8nQibo96O50e8HAAAA0LIJngcAAABoZpJAi9NOOy1uueWWOrc55ZRT4vrrr9/oABBoTP369Ysnnngivve979X5+b3hhhti1apV8Ze//CUKCgocADJmzZIlMfPf/07d9D5v8uSImpqMz35hly4xYPjwKCotjU322CPyLMAAAAAAAM3eG2+8UWts++23j1yWLBKbTocOHTZqv0mIezo77rhjZFNNTU08+OCDqQWdk9D5hobLr8+SJUuiufv+978fp59+etrnnn/++dSjTZs2qVCCJIh+9913Tx3Tjh07Nkk9jldtH3zwQdq5Gjt2bJ0LQjfUnDlz0o4vXLiwRQTP5+Ic5uo1Mxvq+v9H8DwAAAAAZFd+mzbR96CDUo/KpUtj5iOPRMWYMTHn2WeTBO6M1FBTWRmzH3ss9Sjs3Dn6H3546n78XnvtFXl6Usgg/X40V/r9AAAAAFouwfMAAAAAzUhlZWV861vfijvuuCPt8/n5+XH11VfHOeecE3l5eRmvD75IshjCTTfdFDvvvHOce+65UZ3mZuKbb745FWpz6623RmGhX2HSdKpXr45PnnoqysvKUjeaJ3/PtPy2baPPQQdFcWlp9DnwwCiwYAgAAAAAtBgzZsxIBQo3t+D59QV+b4xPPvkk7XivXr0imwsDXHLJJfHKK680yf5bQijygQceGN/5zndS3+/UZc2aNTFp0qTUI5F8v7PddtvFnnvuGXvttVfqkYTTbyzHK73Zs2enHZ8wYUI0tUWLFkVxcXE0d7k4h7l4zcy1/3/cEwEAAAAAuSMJfd/06KNTj5WffBIV48ZFRVlZLHr99YzVkITff3zvvalH+759Y8BRR0XxqFHRdbvt/D6Rpv3s6fejmdPvBwAAANAySW0CAAAAaEY3oX3961+Pe+65J+3zSVjDnXfeGcccc0zGa4OGSAIAfvCDH6QCFL72ta+lPtvrShZXWL16deozLXyexlRTXR3zX3opKkaPjhkPPhhrFi3KygRvsueeUVRSEv2PPDLaduuWlRoAAAAAgKaVhGSnk+vB8x06dEg7niwa27Vr1w3e77Jly9KOb8w+N8bTTz8dZ599dqxatSor79+c/PSnP42ePXvGVVddFVVVVV+4ffLdz2uvvZZ6/PWvf43u3bvHEUccESeccEJsvfXWG1SD41W3JUuWRLbUdV43N7k4h7l2zcym5P+fdNq3b5/xWgAAAACAL9a+T58YeMopqceS995LBdCXjxkTK8rLMzZ9K2fPjuk33ZR6dN566yguLU3dv9+xqChjNdA66PejpdDvBwAAANDyCJ4HAAAAaAaSAIeTTjqpztD5tm3bxr333htHHXVUxmuDDZUsknDffffFsccemwqZX1fyeU8+23//+9+joKDARLNRlrz7bpSXlUVFcsN6RUVWZrPL1ltH0ahR/3PD+oABWakBAAAAAMh+8PygQYOaZfD8xga0r1mzJu14p06dItNeeOGFOOuss9J+P7FuqPGAAQOiV69e0aVLl9TcJAvmJk3365oyZUp89NFH0VKdeuqpcdBBB8Uf//jHGD9+fL0C6D+1cOHCuOuuu+Luu++O4cOHx4UXXhibbLJJvV/veK3fF32Om1JNTU20BLk4h7l0zczV4Pm6/r8CAAAAAHJHl622im1//OPY5txzY8GUKal7+mc8+GCsWbgwYzUsfffdeOv3v089eu6xRyqEvv/w4dG2W7eM1UDLpN+Plki/HwAAAEDLIXgeAAAAIMdVV1fHaaedFnfeeWedzdSjR4+Oww47LOO1wcYaOXJkjBkzJkaNGpU2MOCOO+5Ihbr893//d+Tn55twGmTl7NlRMW5c6ub0xVOnZmX22vftG0UjR6YC57tuu23aMCIAAAAAoPUEz/ft27dBgdvZ0LVr17Tjy5Yt26j9JovNprN06dLIpCTM+ec//3mdQdN77713jBgxInbffffYfPPN6/173fPPP79FB88nBg4cGNdcc03Mnj07xo4dG08//XS88sor9V6UIPnec9y4cTF58uS4/vrrY6eddvrC1zheX8x3aC1zDnPlmpkLli9f3qD/rwAAAACA3JN839Jz991Tjx0uvjg+efrp1H3+s8ePj+oMLg46/4UXUo/Xf/Wr6HPAAan7/PsedFAUtGuXsRpoGfT70ZLp9wMAAABoGQTPAwAAAOSwmpqaOPvss+OWW25J+3ynTp3igQceiAMOOCDjtUFjOfzww+Ohhx6Ko446Km1ozc033xzt2rWLP/3pT0K7+UJrliyJWY88kroJfe7EicndvBmftcLOnaP/EUdEcWlpbLLnnpFXUJDxGgAAAACA3Aye33777SPX9e/fP+14Eja+7bbbbvB+O3bsmHZ8yZIlkUn//Oc/Y/r06bXGe/ToEVdeeWXss88+G7TfjQ3mb06SBRROPfXU1CMJ8E/C51988cWYMmVKvPzyy184F3PmzInvfve78a9//Ss23XTT9W7reG34ufXUU09Fv3796rEHcnEOc+WamQuS/3/SGTBgQMZrAQAAAAA2Xn7bttFv2LDUI7n/f+bDD0fF6NExd9KkpJEqI1OchN3PevTR1KOwS5cYcOSRUZTc/z9kSOTl4GKl5Bb9frQG+v0AAAAAmj/B8wAAAAA5fBPaueeeGzfccEOdofMPP/zwBodfQC458MADU5/n5Ka0dGEkyXnQvn37VOBLXl5eVmokd1WvWRNzJkyI8tGjY9b48VG9alXGa8hr0yb6HHBAKmy+78EHR0H79hmvAQAAAADIHfPnz08blDto0KDIdcXFxQ0K/m1IWHk6c+fOjUxKFsNdV2FhYdx4442xyy67bPB+Fy5cGLlq+fLlTbbvtm3bxpAhQ1KPRFVVVUydOjUef/zx1Hc/6UL+EwsWLIjf/va3qXlfn9Z4vBqqd+/eacfLy8sFzzfjOcyVa2YuqOv/n6KioozXAgAAAAA0rjZdusRmxx6beqyYNStmjB0b5WVlsfjNNzM21ZVLlsRHd9+derTv1y+KSkqieNSo6LrNNhmrgeZDvx+tiX4/AAAAgObNMqsAAAAAOeryyy+PP/zhD2mf69ChQzzwwANC52lRkkUUxo0blwqYT+fqq6+OK664IuN1kbs3685/6aV47Re/iEf23jueP+20mPHAAxkPne8xeHDs+Otfx2ETJ8aQP/85BgwfLnQeAAAAAEgFb6ez/fbbN9vg+VmzZm3UfjfddNO046+99lpkShI2/vLLL9caHzFixEaFmCc++eSTaIwQ93WtWbNmo/e7aNGiyJSCgoLYaaed4oc//GEqNP5vf/tbbFNHMMsTTzwR7733XrM9Xrl+zn7wwQcZr6W5ysU5zIVrZi4HzycLUGRrUQAAAAAAoGl06NcvBp52Whwwblwc8OCDsdUZZ0T7/v0zOt0rZ82KaX/5Szw1fHg8NWJEvPeXv8SKmTMzWgO5Tb8frY1+PwAAAIDmS/A8AAAAQA4aM2ZMXHTRRXUGTowePToOOOCAjNcFTe3AAw+MsrKytMEqiQsvvDDGjh3rQLRiS6dPj7euvjoeP/jgePa44+KDf/wj1ixYkNEaOg8cGNuce24c8uSTse/dd8fm3/hGtO3RI6M1AAAAAAC5bdq0aWnHt91228h1SUh4Eui7runTp2/Ufrfbbru046+88kpkykcffRRVVVW1xg899NCN2u/8+fMbJaC6U6dOtcaWLl260fstLy+PbNl7773jn//8Z+y8885pn58wYUKzPV65oq4FLSZOnJjxWpqrXJzDXLhm5vL/qVtvvXW0adMmK/UAAAAAAE2v6zbbxHY/+UkMe/rpGHrnnbHZV78abbp2zejUL37rrXjziiti/H77xXPf/GZ89K9/xZolSzJaA7lFvx+tlX4/AAAAgOZJ8DwAAABAjpk6dWp885vfjJqamlrPJSEf9957bxx22GFZqQ0yIfl833PPPWlDbZLz4hvf+EbqPKH1WDV3bky/5ZZ4etSoeOLQQ+Pd666L5R99lNEa2vXuHVueckrsV1YWBz78cHz5rLOi46abZrQGAAAAAKD5SBdq3aFDhxgwYEDkunbt2qUCfdf11ltvbdR+k397UVFRrfF33nkn3n///ciEefPmpR0vLi7eqP1Onjw5GkPnzp1rjc2ZM2ej9/vSSy9FNiWf/QsuuCDtc+v7XOX68cqlxSLSfXaSUP/Kysqs1NTc5OIc5sI18/PqCnlPtzhEY0oW36ioqKg1vuOOOzbp+wIAAAAAuSEvPz82GTIkdr700jh00qTY/frro9/hh0d+27aZK6KmJuZNmhSvnn9+PDJkSLx49tkx69FHo3r16szVQNbp96O10+8HAAAA0PwIngcAAADIIUl4QklJSap5el35+flx1113xVFHHZWV2iCTRo4cmfq8J5/7dSXnR2lpacyfP99BacEqly2L8tGjY9K3vx2PDh0aU3/zm1j02msZraGgU6co/spXYq9bb41Dn302Bl10UXTfYYfIy8vLaB0AAAAAQPPzUZrFM5MA4eby+8V0gb7Jv2nlypUbtd+DDjoo7aKzd9xxR2RCuu/gPg3b3xh33nlnNIbevXvXGlu4cGHMnj17g/e5ePHirAfPJ3bZZZfo2LFjrfH1fd+T68crVySB4AceeGCt8UWLFkVZWVlWampucnUOs33N/LxOnTqlHV+xYkWTvu/bb7+d+jeva6eddmrS9wUAAAAAck9Bu3bR//DDY4/rr0+F0O906aWxyZ57ZrSGJGx+5kMPxQtnnBGP7LVX/OdnP4t5L74YNdXVGa2DzNLvB/9Dvx8AAABA8yJ4HgAAACBHrFmzJo4//viYPn162uevuuqqOOaYYzJeF2RL8nm/8sor0z43bdq01PlSWVmZ8bpoOtWVlTH7ySfjpXPPjUf23DNe/vGPY87TT0dNVVXGpj2vsDD6HHRQ7PaHP8RhkyfHrr//ffTed9/IKyjIWA0AAAAAQPO3ZMmSWmPdu3eP5mLIkCG1xqqqquKNN97YqP2OGjUq7fg999wTH3/8cTS1rl27ph3fmGD3//znPzF58uRoDNtvv33a8YkTJ27wPpOFfps6GLo+kkUX0gXPJ4HfzfV45ZK6vkf+05/+FKtXr854Pc1RLs5htq+Zn9e5c+dGPx/r4/XXX087vsceezTp+wIAAAAAua1tt27xpa9+NYbecUccMmFCbPfTn0aXrbfOaA1rFi2KD++8M5776lfjsQMPjDd///tY8u67Ga2BpqffD9am3w8AAACg+RA8DwAAAJAjfvzjH8fjjz+e9rlTTjklvv/972e8Jsi2H/zgB/Htb3877XOPPfZY6ryheaupqYkFr74ar//qV/Ho0KHx/He+ExVlZVGV4RCcHrvuGjv88pdx6HPPxZ5//WsUjRwZhR06ZLQGAAAAAKDlSELa6zOWq/bbb7/Iz699m/HGBnbvuOOOsdtuu9UaX758efz0pz+N6urqaEq9e/dOO/70009v0P6SQPfzzjsvGssOO+yQdnzMmDEbtL8ZM2bEX//618gFixcvjvnz59ca79OnT7M9Xrlk6NChsd1229Uar6ioiJ///OdZqam5ycU5zPY18/OKi4vTjr/zzjtN+r7p/t/50pe+FJtvvnmTvi8AAAAA0Hx0HDAgtjr99DjgoYdi/3HjYuBpp0X7fv0yWsOKiop474Yb4skjjoinSkpi2k03xcomXriTzNDvB7Xp9wMAAABoHgTPAwAAAOSAJPDh2muvrbPB/frrr4+8vLyM1wXZlnzub7jhhth7773TPn/NNdfETTfdlPG62HjLPvww3rn22nhi2LB45uij4/1bb43V8+ZldGo7bb55bPPDH8bBjz0W+95zT2xx4onRbpNNMloDAAAAANAytW3bttbY+++/H5WVldEcdO/ePXbeeedGD55P1LWo7EsvvRQ/+clPYs2aNRu1/5UrV9b53Je//OXo1KlTrfF//vOf8cknnzTofVavXp0Kfp4+fXo0lqKiothyyy1rjT/77LMxYcKEBu1r2bJlce6558aiRYs2uJ5bbrklpk6dGo3hrrvuShuSPWjQoGZ7vHJNXaH6999/f1x99dWpxYAbw5IlS+LWW2+NligX5zCb18zPGzBgQOr/hnU98MADTRaAn+z3xRdfrDW+//77N8n7AQAAAADNv/+k23bbxfbnnx/Dnn469r7tttj0uOOisHPnjNaxeOrUeOPSS+PRffeNiSedFB/fe2+sWbIkozXQOPT7QXr6/QAAAACaB8HzAAAAAFn2zDPPxJlnnpn2ueLi4rjvvvuiXbt2Ga8LckXy+U/Og+R8SOd73/teKnCF3Ldq3rx4/7bb4pljj43HDz443v7DH2LZBx9ktIa2PXvGFt/6Vux3//1x0Pjx8eVzzkkF0AMAAAAANKYtttii1tjChQvj5z//eSxdurRZTPYhhxxSa+zll1+ud0hxXXbffff46le/mva5cePGxWmnnRYff/xxg/c7a9asuPTSS+Pkk0+uc5vCwsLYZ5990oa0n3HGGbFgwYJ6vdecOXNS2z/yyCPR2I499tg6A7Hffffdes/FiSeemDpeG+Pxxx+Po48+Ok466aTUv3XVqlUbtJ9HH300tZjwutq0aROHHXZYsz5euSRZyLmuc+vGG29MnVuzZ8/e4P0nn7/LL788DjjggPj9738fLVEuzmE2r5npalnXm2++GRdddFHMnTs3Gluy8EW6xTPS/f8EAAAAAPB5eQUF0Wvo0Njl8svjsMmTY/C110bfYcMir02bzE1UdXXMffbZeOWnP41H9twzpnz/+zH78cejeiMXFCUz9PvB+un3AwAAAMh9eTU1NTXZLgIAAACgtfroo49SzdlJ2MG6OnTokLpJbbfddstKbZBrpkyZEvvuu2/aQJs+ffrECy+8EJtttllWaqNulStWxOzx46O8rCzmTJgQNZWVGZ+ugg4dot9hh0VxaWn02mefyC8szHgNAAAAAEDrcscdd8Qll1yS9rn27dvHDjvsEL179079ObHjjjvGN7/5zcglSSjxQQcdFNXV1WuNX3fddXHooYdu1L6XL1+e+ve+8cYbaZ9PAsmToOXhw4fHLrvsEgUFBWm3S8KWJ02aFA888EA8//zzUVVVFdtuu22UlZXV+d4vvfRSfP3rX0/7XHJMfvCDH8TIkSM/Ozbrfrc3duzYuOmmm1Lh55/q3LlzbL/99qkaPq+oqCgV3t4Q8+fPT81vugUKkvc566yz4rjjjosuXbrUev79999PLeZ72223xYoVKz4bHzp0aDz33HO1tv/73/8ee+65Z521JOH1n/83dezYMQ4++OA48MADU5/hzTffPPLy8tK+trKyMvX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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { "height": 723, - "width": 1223 + "width": 3023 } }, "output_type": "display_data" } ], "source": [ - "fig, ax = plt.subplots(figsize=(6, 3.5))\n", + "fig, ax = plt.subplots(figsize=(15, 3.5))\n", "ax.set_xlim(0, 10)\n", "ax.set_ylim(0, 5)\n", "ax.axis(\"off\")\n", @@ -175,10 +177,10 @@ "id": "9d37c3cd", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:03.718053Z", - "iopub.status.busy": "2026-06-01T17:16:03.717835Z", - "iopub.status.idle": "2026-06-01T17:16:03.721488Z", - "shell.execute_reply": "2026-06-01T17:16:03.720995Z" + "iopub.execute_input": "2026-06-02T17:36:02.930162Z", + "iopub.status.busy": "2026-06-02T17:36:02.929961Z", + "iopub.status.idle": "2026-06-02T17:36:02.933311Z", + "shell.execute_reply": "2026-06-02T17:36:02.932936Z" } }, "outputs": [ @@ -217,19 +219,102 @@ "id": "ba7bfe6c", "metadata": {}, "source": [ - "The observed difference recovers the *biased* effect, as it must. $\\tau$ is what we want; $\\beta$ is what we cannot observe. The model below asks which decomposition the data find most plausible given a prior on $\\beta$." + "The observed difference recovers the *biased* effect, as it must. $\\tau$ is what we want; $\\beta$ is what we cannot observe. Before asking how a prior reshapes that sum, confirm the machinery is faithful: hand the model the belief an analyst would hold if they knew the confounder exactly — a prior on $\\beta$ centred at the true bias — and the treatment effect should return unbiased across repeated experiments." ] }, { "cell_type": "code", "execution_count": 5, + "id": "39981d0f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-02T17:36:02.935072Z", + "iopub.status.busy": "2026-06-02T17:36:02.934958Z", + "iopub.status.idle": "2026-06-02T17:36:03.173574Z", + "shell.execute_reply": "2026-06-02T17:36:03.173186Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 823, + "width": 4023 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "def gaussian_sensitivity_posterior(\n", + " d_hat, sigma_d, tau_prior_sd, bias_prior_sd, tau_prior_mean=0.0, bias_prior_mean=0.0\n", + "):\n", + " \"\"\"Closed-form posterior over (tau, beta) given the observed effect.\"\"\"\n", + " prior_precision = np.diag([1.0 / tau_prior_sd**2, 1.0 / bias_prior_sd**2])\n", + " F = np.array([[1.0, 1.0]])\n", + " data_precision = F.T @ F / sigma_d**2\n", + " post_precision = prior_precision + data_precision\n", + " post_cov = np.linalg.inv(post_precision)\n", + " prior_mean = np.array([tau_prior_mean, bias_prior_mean])\n", + " rhs = prior_precision @ prior_mean + F.flatten() * d_hat / sigma_d**2\n", + " post_mean = post_cov @ rhs\n", + " return post_mean, post_cov\n", + "\n", + "\n", + "# Across many synthetic quasi-experiments, where does the posterior mean of tau land\n", + "# when the analyst's prior on the bias is centred at the true confounding strength?\n", + "recovery_rng = np.random.default_rng(RANDOM_SEED)\n", + "n_recovery = 500\n", + "tau_posterior_means = np.empty(n_recovery)\n", + "for i in range(n_recovery):\n", + " y_A_i, y_B_i = simulate_quasi_experimental_gaussian(\n", + " N, TRUE_TAU, TRUE_BIAS, SIGMA_OBS, recovery_rng\n", + " )\n", + " d_hat_i = y_B_i.mean() - y_A_i.mean()\n", + " post_mean_i, _ = gaussian_sensitivity_posterior(\n", + " d_hat_i, sigma_d, tau_prior_sd=1.0, bias_prior_sd=0.1, bias_prior_mean=TRUE_BIAS\n", + " )\n", + " tau_posterior_means[i] = post_mean_i[0]\n", + "\n", + "fig, ax = plt.subplots(figsize=(20, 4))\n", + "ax.hist(tau_posterior_means, bins=30, color=\"C0\", alpha=0.7, edgecolor=\"white\")\n", + "ax.axvline(TRUE_TAU, color=\"C2\", linestyle=\"--\", linewidth=2, label=f\"true \\u03c4 = {TRUE_TAU:.2f}\")\n", + "ax.axvline(\n", + " tau_posterior_means.mean(),\n", + " color=\"C3\",\n", + " linewidth=2,\n", + " label=f\"mean estimate = {tau_posterior_means.mean():.3f}\",\n", + ")\n", + "ax.set_xlabel(r\"Posterior mean of $\\tau$ across simulated experiments\")\n", + "ax.set_ylabel(\"Frequency\")\n", + "ax.set_title(r\"Recovery of $\\tau$ when the bias prior is correctly centred\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "054df778", + "metadata": {}, + "source": [ + "The sampling distribution of the posterior mean sits on the true $\\tau = 0.30$: with the confounder's strength correctly specified, the estimator is unbiased across repeated experiments. The recovery is real, but read what produced it. The data fixed the sum $\\tau + \\beta$ through the observed difference; the correctly-centred prior on $\\beta$ is what split that sum into its parts. A single difference in means cannot separate a treatment effect from a confound that imitates it — the two are not identified by the data, only by the data together with a commitment about the confounder. The prior is not a nuisance to be tuned away. It is the content of the decomposition, and that is why the honest analysis does not settle on one prior but varies it. Recentre the prior on $\\beta$ at zero — where an analyst lands by default, having no oracle for the confounder's size — and the estimate moves. The moderate prior below makes the shift concrete." + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "7d543aca", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:03.722613Z", - "iopub.status.busy": "2026-06-01T17:16:03.722538Z", - "iopub.status.idle": "2026-06-01T17:16:06.091447Z", - "shell.execute_reply": "2026-06-01T17:16:06.090878Z" + "iopub.execute_input": "2026-06-02T17:36:03.174922Z", + "iopub.status.busy": "2026-06-02T17:36:03.174827Z", + "iopub.status.idle": "2026-06-02T17:36:05.724139Z", + "shell.execute_reply": "2026-06-02T17:36:05.723669Z" } }, "outputs": [ @@ -242,24 +327,14 @@ }, { "data": { - "text/plain": [ - "Text(0.5, 1.05, 'Posterior of $\\\\tau$ and $\\\\beta$ under a moderate bias prior')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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wmEkvAABgYrG4CwAAuKgeOHEumfmt5tnxC/OnmzwAADAuzKqrvrC8K32HY8THt7TF/SfOZtAKAAAAAABgdPn01o7YeqormVs3Y3K8aM3CTDoBAABj0+8tnRu/MG9aMnfgfF+87759USqVMukFAABMHBZ3AQAAmZpVWxV/NIybTQAAAMaSa+Y0xAtXp18gKZQi3njnrjjZ259JLwAAAAAAgNHgzqNn4ou7DiVzk6vy8fZrl0dlhdddAACAB5fL5eItV7fE7Nqq5Jj+seNkfLftuHECAAAXlScZAABApl6/YWlMra40dQAAYNx53uqFce2chmTuRO9AvOWu3VFwiyMAAAAAADABnOodiFs27hlW9uYrl0XjpNoR7wQAAIx902qqLiz+Hc7L8rfevz/2n+3OoBUAADBRWNwFAABk5gmLZsUvNs4wcQAAYFzK53LxtmtaYuYwbnHcePxsfHZbRya9AAAAAAAALpViqRRv37gnTvYOJLNPXjwnfnXhzEx6AQAA48OVsxvi+asXJnN9hWK88c5d0VsoZNILAAAY/yzuAgAAMjGjpir++PLFpg0AAIxrM2ur413Xroh8Lp398x0H4z8On86iFgAAAAAAwCXxtd2H4ydHzyRzi6fUOV8GAAA8LEOLu66cNTWZ23u2Jz78QJspAwAAF4XFXQAAQCZev2FpNFRXmTYAADDubZg9NW5cu2hY2Vvu3h2HzveOeCcAAAAAAICsbTvVFR/f0p7MVVfk4l2PWh61lflMegEAAONLPpeLW65tiYbqymT2W/uPxj8fOJlJLwAAYHyzuAsAABhxv940M355wQyTBgAAJoxnrWiMX5o/PZk7O1CIm+/cFf2FYia9AAAAAAAAsnCmbyDecOfOGCyVktlXrl8cLQ2TMukFAACMT3PqauItV7cMK/uue/bGgS6XLQIAAI+MxV0AAMCIml5TFa++fIkpAwAAE0oul7twEGzBpJpkdvvp8/HhTa2Z9AIAAAAAABhphVIp3nLX7jjS3Z/MPrZxRjxl6VwfCgAA8Ig9Zv70eMby+cnc+cFCvP6OndFbKJg6AADwsFncBQAAjKjXblgS02qqTBkAAJhwplRXxnuuWxnVFblk9hv7jsb32o9n0gsAAAAAAGAk3b6tI+481pnMzamrjpuvWnrhQhSA/5+9+wyz86ruxr3OdI1GGvXepSmSJctFkmXZYNMSAv+QRkhCCAmhxWAMLrj33qmhQwohjSQEEvJCCMFNVnFRsdqM2qj3NqPp5fyvmbx5UwCfZ2Sdoyn3/Ql8rW2vaz1f5jzP3r8NAHA2fGT+tJg7cmjGuq2nmuLxtTsNHQAAOGOCuwAAgKx585TR8cbJo00YAAAYtKpGDI0bLpiZqPahl3fEjvqmrPcEAAAAAACQLc8dOBHf2LIv0WGWe5dURHmRSyEBAICzpzAvL+5fUhmlBfkZa/+p7kh8b+ch4wcAAM5IwZktAwAABqWCIdE+8sJYdehktHelf2ZJY/o/NlKNKCpIfDgdAABgIHvHjHGx7lhDfH/XkVeta+nsiptX1MSfvPH8GFqYeeMYAAAAAABAX7LvdEvc/cLWRLXvnzclLhwzPOs9AQAAg8+UspK49aJZcfvqzL9PHlu7M6pGlvVc0HjOFQyJ1OiLM9YAAAB9QyqdTv/s0/YAAAA/w60ra+PH+45lnM0Dl1TEm6eMMUMAAIDuUK6Oznj/Uxti26mmjPN485TRcf+SikilUmYHAAAAAAD0Cy2dnfHBn2yI2gTfQi4dPyKevKw68nwLAQAAsujhl3fEd3Yeylg3eWhx/Nkbz49hRQWeBwAAkFhe8lIAAGCw+/HeY4lCu94weVS8afLonPQEAADQH5QU5MdDl1TG0IL8jLX/tvdYfHv7wZz0BQAAAAAA8Fp13yf/6JqdiUK7JpYWxz1L5gjtAgAAsu7ahTOiasTQjHX7Glvjnhe3RVc67akAAACJCe4CAAASOdHaHo+t3ZGxrryoID55wcxIuQ0RAADgf5g2bEjcsWh2oql8Zv2ueOVYgwkCAAAAAAB93j/uPBzf33UkY11RXioeXloZ5UWFOekLAAAY3Irz83p+gwwvzHzZ4rMHTsRf1O7PSV8AAMDAILgLAABI5Im1O+NEa0fGuusvmBmjS4pMFQAA4Gd4w+TR8e6KiRln05FOx22rauNka7s5AgAAAAAAfdam46fjiXU7E9V2XwhZPbIs6z0BAAD8p0lDS+KuxRWJBvLFDbvjpSOnDA8AAEhEcBcAAJDRU/uOxY/2HstYd8WkkfELU0abKAAAwKv46PxpsXD0sIwzOtTcFneu3hqd6bR5AgAAAAAAfU73BSS3rKqJ9q7M3zJ+Zca4eMfM8TnpCwAA4L+7fOLIeF/15IxD6YqI21dtjSPNbQYIAABkJLgLAAB4VSda2+PhNTsyTml4YX7cdOGsSKVSJgoAAPAqCvLy4oFLKmNkcWHGOa06fCr+ZPNe8wQAAAAAAPqU9q6uuGVlbRxsynygvXrE0Lj+gpk56QsAAOBn+eC8qbF4bHnG4RxvbY/bVtVGR1d3jBcAAMDPJ7gLAAB4VY+t2RknWjsyTum6C2bG6JIi0wQAAEhg7JCiuH9JRaIPNV/bvDdWHjpprgAAAAAAQJ+QTqd79pW9fLQ+Y+3wooJ4aGllFOc7vgIAAJw7+alU3LukomffVibrjjXEZ9bvyklfAABA/+XLBwAA8HP9296j8eN9xzJO6HUTR8Zbp44xSQAAgF5YNK48Pnze1Ix16Yi4c/XWONTUar4AAAAAAMA59+3tB+O7dYcz1qUi4t7Fc2LS0JKc9AUAAPBqRpUUxoOXVPaEeGXyt9sPxj8n+N0DAAAMXoK7AACAn+lYS1s8umZnxukMK8yPmy6cFakEHy4AAAD4n95bNTkumzAi41hOtXXEratqo72rywgBAAAAAIBzZuWhk/GpdXWJaj8wd0pcOmFk1nsCAABI6vzRw+JjC6Ynqn1kzY7YdPy04QIAAD+T4C4AAOCnpNPpePjlHT0HwzO5duGMGDukyBQBAADOQF4qFXctnhMTS4sz1m44fjo+u36XOQMAAAAAAOfErobmuG1VbSS5ZuTS8SPiD+dOyUFXAAAAvfPbcybEmyaPzljX1pWOm1bWxLGWNiMGAAB+iuAuAADgp/xgz9F45sCJjJN53cSR8bZpY00QAADgNSgvKoyHllZGYV4qY+3fbj8Y/7b3qHkDAAAAAAA5Vd/WETc8vyVOt3dmrJ1aVhL3LqnoucAEAACgr0mlUnHrxbNiellJxtrDzW09AcYdXUkijAEAgMFEcBcAAPA/HG5ujSfW7sw4leFFBXHLRbN6PlgAAADw2swdWRbXLZyRqPaBl7ZHXX2zkQMAAAAAADnR0ZXuOai++3RLxtqywvx4Yll1z/4yAACAvqqssCAeubQqSgvyM9auOdoQn16/Kyd9AQAA/YfgLgAA4P9Jp9Px0Ms7oiHBrYg3XDAzRpcUmR4AAMBZ8mszx8dbp43JWNfU0RW3rKqJ5o7Mv90AAAAAAABeq8+sr4vVh08lOqDywCWVMX3YEEMHAAD6vJnDS+PuRXMS1X57+8H457rDWe8JAADoP1Lp7pP5AAAAEfG9usPxwEvbf+4sZuafiG+O+c6rzqrgwrsjNXSyeQIAAJyB7jCuP/zJK7GjvjljbXfIV/fGsVQqZdYAAAAAAEBWfGfHoXh4zY5EtdctnBG/NWeiJwEAAPQrX964O76xZV/GuqK8VHz5ivkxb1RZVvpIN+6LjjV3v2qNMzsAANB3dF9oAgAAEAebWuPT6+pMAgAA4BwaUpAfDy+titKCzJ9wfrD7aHxn56Gc9AUAAAAAAAw+Lx4+FY+t3Zmo9ldmjIt3zZ6Q9Z4AAADOtg/OmxqXTxiZsa6tKx03rqyJYy1tHgIAACC4CwAAiEin0/HAS9ujsaPTOAAAAM6x6cOGxG0Xz05U++S6uth0/HTWewIAAAAAAAaX7aea4qaVNdGZTmesvWjM8PjkhTMjlUrlpDcAAICzKS+VinuWzIlpZSUZa480t8Wtq2qjvavLQwAAgEEu83XtAADAgPednYdi9eFTGeuWTRiRk34AAAAGuzdPGZPoVvr2rnTcvLImTra256QvAAAAAABg4Os+iH7t8s1xuj3zRZCTSovjoaWVUZjneAoAANB/lRUWxKOXVkVpQX7G2rVHG+LxtTsjnSDoGAAAGLh8GQEAgEFuf2NLfHb9rox1o0sK4w+qJ+ekJwAAACKuOX96zB9VlnEUh5rb4o7VWxPdeA8AAAAAAPBqGts747rlm3u+P2RSWpAXT1xWHSOKCw0VAADo92YOL427F89JVPuPOw/H320/mPWeAACAvktwFwAADGJd6XTc9+L2aO7sylh7y4WzYlhhQU76AgAAIHpupn/wksoYUZT5t9jqw6fia5v2GBsAAAAAAHDGOrq64paVNVF7qiljbSoi7ltSGbOGl5o4AAAwYFwxaVS8f+6URLWfWl8Xqw6dzHpPAABA3yS4CwAABrHu2z1ePlqfse7t08fG6yaNyklPAAAA/JfxpcVx75KKnsMvmXxjy7547sAJ4wMAAAAAAHotnU7Hw2t2xKrDpxLVf3T+tLh84kiTBgAABpwPzJ0Sr0vwe6czHXHbqtrY3dCck74AAIC+RXAXAAAMUt0fBj6/YXfGurFDiuLa82fkpCcAAAB+2iXjR8QH501NNJq7X9ga+063GCMAAAAAANArX9+yN/6p7kii2l+dOS7eUznJhAEAgAEpL5WKuxfPiellJRlrG9o744bnt0RDW0dOegMAAPoOwV0AADAIdabTcd9L26O1sytj7W0XzYphRQU56QsAAICf7X3Vk+OyCSMSbQS7ZVVttHR2GiUAAAAAAJDIP9cdjq9u2puodtmEEfHJC2ZFKpUyXQAAYMAqKyyIx5ZVR1lhfsbaXadb4vbVtdHRlc5JbwAAQN8guAsAAAahv956INYfa8hY9yszxsWlE0bmpCcAAAAy3eJYEZNKizOOqeZkYzy+dqdxAgAAAAAAGa06dDIefHlHoklVjxgaD1xSGQV5QrsAAICBb/qwIT2/gZIcxl956FR87pVdOegKAADoKwR3AQDAILOjvim+tHF3xrqJpcXx8fNn5KQnAAAAMhteVBAPX1oVRQkOw/xT3ZH47s5DxgoAAAAAAPxctScb4+aVtdGZTifaT/bkZdVRWpBvogAAwKCxdPyIxGdr/nrbgfiePVsAADBoCO4CAIBBpL2rK+5+YVu0dWXeaHX7xbNjaKFNVgAAAH1J1Yih8ckLZyWqfXztzth84nTWewIAAAAAAPqfPaeb4+PPbY6mjs6MtcML8+NTl1XH6JKinPQGAADQl/zWnAnxKzPGJap9ZM3OWHO0Pus9AQAA557gLgAAGES+vnlv1JxszFj3zlnjY9G48pz0BAAAQO+8Y8a4RBvBukObb1lZG6fa2o0YAAAAAAD4f440t8XHnt0cx1szf0MozEvFo8uqY+bwUhMEAAAGpVQqFZ+8cGZcMGZYxtqOdDpuXlET+xtbctIbAABw7gjuAgCAQeKVYw3xZ1v2ZaybPLQ4rl4wPSc9AQAAcGauv2BmVI0YmrHuQFNr3LV6W3Sl00YNAAAAAABEfVtHfPy5TT3fEJK4a9GcuHDMcJMDAAAGtcK8vHh4aVVMLC3OWHuyrSOuXb4lGto6ctIbAABwbgjuAgCAQaClozPueXFbdGWoS0XEHYvmxJCC/Bx1BgAAwJkozu/eCFYZwwsz/35bcehkfGPzXoMGAAAAAIBBrrmjM65/fktsr29OVP+xBdPjLVPHZL0vAACA/mBkcWE8vqwqSgsyH8+va2iOW1fVRkdXppM8AABAfyW4CwAABoHPb9gde063ZKz73cpJbkcEAADoJyYNLYl7llT0hDBn8rXNe2PFwRM56AoAAAAAAOiL2ru64paVtbH+WEOi+nfOGh+/WzEx630BAAD0J3PKh8Y9i5Pt2Vp9+FQ8vrYu0ul0DjoDAAByTXAXAAAMcKsOnYxvbz+YsW728CHxoXlTc9ITAAAAZ8eyCSPj/XOnZKzr3vp15wvbYn9j5lBnAAAAAABgYOlKp+PeF7fFikMnE9VfOWlUXHfBzEilkhxFBwAAGFxeP2lUXDV/WqLa7+w8FH+19UDWewIAAHJPcBcAAAxg9W0dcd9L2zPWFaRScffiiijO9xMBAACgv/nDuVNi6fjyRL8Rb1lZG62dXTnpCwAAAAAAOPfS6XQ8ua4u/nXPsUT1i8eWx31LKiJfaBcAAMDP9d7KSfHWqWMSTeizr+yKp/cfN00AABhgnMoHAIAB7Il1O+NIc1vGug/OmxKVI4bmpCcAAADOru6DM/curoiJpcUZa7ecbIwn1u70CAAAAAAAYJD4+ua98e3tBxPVzhs5NB65tCqKXAAJAADwqlKpVNx68ew4f/SwjJNKR8Sdq7fGlhOnTRUAAAYQwV0AADBA/XjvsfjB7qMZ6+aPKov3VE7OSU8AAABkR3lxYTy0tDIK81IZa79bdzj+qe6wRwEAAAAAAAPc32w7EF/dvDdR7YxhQ+JTl82NoYX5We8LAABgICjOz4tHL62KSQkuXGzp7Iobnt8Sh5pac9IbAACQfYK7AABgADrW0haPrNmRsa4kPy/uWjQnChIc7AYAAKBvmzuyLK5fODNR7WNrdkTNycas9wQAAAAAAJwb/7jzUDy5ri5R7fghRfHZy+fGiOLCrPcFAAAwkIwsLownL6uOsgQhyEda2nvCu5o6OnPSGwAAkF2CuwAAYIBJp9Px4Es74lRbR8bajy2YHtOGDclJXwAAAGTfr84cF2+fPjZjXWtXOm5eURP1CX47AgAAAAAA/cv/2X0kHn4588WP3UYUFcRnL58X40uLs94XAADAQDRzeGk8vLQq8lOpjLW1p5riztVbozOdzklvAABA9gjuAgCAAeZ7dYfjuYMnMtYtGVcevzFrfE56AgAAIDdSqVTceOHMqCgvzVi7v6k17n5ha3TZBAYAAAAAAAPGv+87Fve9uC2SHAEvLciLT18+N2YMd/kjAADAa7F4XHnPvq0knj1wIj63fpeBAwBAPye4CwAABpB9p1vi0+vrMtYNK8yP2y+e3XOgGwAAgIGlJD+/5wbHssL8jLXLD56MP6vZl5O+AAAAAACA7Fp+4ETcsWprdCZI7SrMS8Vjl1bH3JFlHgsAAMBZ8Kszx8d7Kiclqv2rbQfi77cfNHcAAOjHBHcBAMAA0ZlOx70vbYumjq6MtZ+8YGaMLy3OSV8AAADk3pSykrhncUWi2i9v3BOrDp3Mek8AAAAAAED2vHD4VNy8siY60ulEB0nuW1IRi8aVeyQAAABn0UfnT4srJo1MVPvEup2x8qB9WwAA0F8J7gIAgAHir7YeiLVHGzLWvWny6PiFqWNy0hMAAADnzuUTR8b7qidnrOs+vnPH6q1xsKk1J30BAAAAAABn17qj9XHD81uirStzaFe32xfNjjdMHu0xAAAAnGV5qVTPhYvVI4ZmrO1MR9y6qja2n2ryHAAAoB8S3AUAAANA90v6L23cnbFudElh3HjhzEilUjnpCwAAgHPrg/OmxpJx5RnrTrV1xC0ra6KtsysnfQEAAAAAAGfH5hOn4xPLt0RLwnf8N104M94+fZzxAwAAZMmQgvx4fFl1jBtSlLG2saMzrn9+SxxrafM8AACgnxHcBQAA/Vz3oeq7X9ga7QluS7z1otkxorgwJ30BAABw7uWnUnHvkooYn2AT2KYTjfHp9XU56QsAAAAAAHjttp5qjGue2xxNHZ2J6j++YHr8+qwJRg8AAJBlY4cUxRPLqqO0IPNR/gNNrT3hXUkDmQEAgL5BcBcAAPRzX9m0J2pPNWWs+5UZ4+LyiSNz0hMAAAB9x8jiwnhwaWUUpFIZa/9+x6H4l11HctIXAAAAAABw5rafaoqrn9kU9W0dieo/PG9qvLtykpEDAADkSOWIoXHfkspEh/k3n2iMz72yKwddAQAAZ4vgLgAA6MdeOnIq/qJ2f8a6SaXF8fHzZ+SkJwAAAPqe+aOGxXULk/0ufHjNjth6qjHrPQEAAAAAAGdmR31TfPTZjXEyYWjXe6smxfuqJxs3AABAjl0+cWRcm3Df1stH6rPeDwAAcPYUnMV/FwAAkEPdNyXe88K2SGeoS0XEnYvnxNDC/Nf+H80rjCidmLkGAACAPufXZ42P9ccb4ge7j75qXWtnV9y8oib+9I3nx7Ain5IAAAAAAKAv2VnfFB95ZlOcaE0W2vWu2RPiI+dNi1SqeycZAAAAufauORNj9+mW+Pb2g69a1xb5sbNjRIwqLozy4p+zb8uZHQAA6DNS6XQ60zl/AACgD7pjdW38655jGeveUzkpPrZgek56AgAAoG9r6eiM9/9kQ2yrb8pY+7qJI+PRS6siz0EeAAAAAADoE3Y1NMdVz2yMYy3tiep/Zca4uOWiWUK7AAAAzrGOrnTc8PyWWHHoZMba7tjlB5dWxhsnj85JbwAAwJnJO8N1AADAOfTD3UcShXbNGV4aH5o3NSc9AQAA0PeVFOTHQ0srY2hBfsbaZw+ciG/W7s9JXwAAAAAAwKvb3dAcH+lFaNdbp46Jm4R2AQAA9AkFeal44JLKqCgvzVibjoi7V2+NV4415KQ3AADgzAjuAgCAfuZAY2s8unZnxrqivFTcs2ROFOf7sx8AAID/Mm3YkLhr0ZxEI/nSht2xKsEtjwAAAAAAQPbsOf0foV1HE4Z2vWHyqLhj0ZzIT6U8FgAAgD5iaGF+PLGsOsYOKcpY29qVjhue39LzexAAAOibnOAHAIB+pDOdjnte3Bqn2zsz1n5k/vSYUz40J30BAADQv1wxeVS8t3JSxrquiLhj9daeEGkAAAAAACD39p5uiY88symOJAztunLSqLh/SUUU5AntAgAA6GvGlxbHpy6rjtKC/Iy1J9s64hPPbYmTrcl+DwIAALkluAsAAPqRb9XujzVHGzLWLRlXHr81Z0JOegIAAKB/+vB50+LiscMz1p1q64ibVtZES2fmEGkAAAAAAODs2d/YHdq1MQ43tyWqf93EkXH/Jd2hXY6KAAAA9FUV5UPjoaWVkZ8gb3lvY0t8coW9WwAA0Bf5GgMAAP3ElhOn48sb92SsG15UEHcumhN5KTcmAgAA8PMV5KXiviUVMbakMOOYak42xqNrdkY6nTZSAAAAAADIgX2nW+KqZzbGoYShXZdNGBEPXlIZhUK7AAAA+ryl40fEzRfNTlS7/lhD3PvC9uiydwsAAPoUwV0AANAPtHR0xl0vbIuOBC/Zb75wVowdUpSTvgAAAOjfRpcUxUNLqyI/Qfjz93cdie/sPJSTvgAAAAAAYDDbc7o5/uiZjXGwKVlo16XjR8TDS6uiKN8REQAAgP7iHTPGxfuqJyeq/fG+Y/H5V3ZlvScAACA5X2UAAKAf+PyG3VHX0Jyx7u3Tx8abpozOSU8AAAAMDAtGD4trF85IVPvE2rp45VhD1nsCAAAAAIDBandDc1z1zKY43JwstGvp+PJ45FKhXQAAAP3Rh+dNjbdOG5Oo9ltbD8TfbT+Y9Z4AAIBkBHcBAEAf9/zBE/HtBC/WJw8tjusXzsxJTwAAAAws75w1Pt42bWzGuo50Om5ZWRPHWpIdFgIAAAAAAJLb1RPatTGOJAztWjLuP0K7ivMdDQEAAOiPUqlU3H7x7Lho7PBE9U+s3RnP7j+e9b4AAIDMfJ0BAIA+7ERre9z34vZEf9jfvbgihhbm56QvAAAABt4GsJsumhmV5aUZa4+0tMdtq2qjo6srJ70BAAAAAMBgsLO+qSe062hLe6L6RWOHx2OXVkVJvj1jAAAA/VlhXl48srQqZgwbkrG2e8fW7au3xuYTp3PSGwAA8PMJ7gIAgD4qnU7Hgy9tj+OtmTdi/UH15Dh/9LCc9AUAAMDA1H2w55FLq2J4glDoNUcb4vOv7M5JXwAAAAAAMNDtqG+KjzyzKY4lDO26aOzweGJZdZQUCO0CAAAYCIYXFcSnLquOUcWFGWtbOrviuuVbYn9jS056AwAAfjbBXQAA0Ed9r+5wPHPgRMa680aWxfvnTslJTwAAAAxsk4aWxH1LKiOVoPavth2IH+45moOuAAAAAABg4Np+qju0a2OiCx67XTx2eDwptAsAAGBA7t168rLqKMnPfPy/+zfktcu3RH1bR056AwAAfprgLgAA6IN2NzTHk+vqMtZ1v4y/e/GcKMjzpz0AAABnx9IJI+KPzpuaqPaBl7bH1lONRg8AAAAAAGeg+x17d2jXidZkB62XjCvvCe0aUpBv3gAAAAPQ3JFl8cAllYkCAOoamuOmFTXR1tmVg84AAID/LZVOp9M/9U8BAIBzpqOrKz701MbYeOJ0xtpbLpoVvzpzfORKuvlwdGz+/KvWFMy9OlJDxuWsJwAAAM6+rnS6Z1PXMwdOZKydMrQ4/vSN58ewogKPAgAAAAAAEqo92RhXP7spTrUlC+26ZFx5PLqsKkryhXYBAAAMRP/9zE59W0cca2n/qZqbT7459nUO/x//7K1Tx8Tdi+dEKpXKWa8AAECEExQAANDHfHnTnkShXa+fODJ+ZUaOA7K62iOaDmSuAQAAoF/LS6XirsVz4n3//krsPt3yqrV7G1vjrhe2xuPLqnvWAQAAAAAAr27LidPxsec29xzETuLS8SPikUurojg/z2gBAAAGqv92Zqc7mmv4z0gBKIrOn/pnP9hzNCYOLY4/Om9aLroEAAD+L19tAACgD3nh8Kn4Zs3+jHWjigvj1otnuw0DAACArCkrLOg5BDQkwSGg5QdPxjc27/U0AAAAAAAgg80nTsfVz25KHNq1bILQLgAAAF7dn2zZF9/deciYAAAghwR3AQBAH3GitT3ufmFrpBPU3rFodowsLsxBVwAAAAxms4aXxh2L5iSq/ermvfHcgRNZ7wkAAAAAAPqrTcf/I7Srob0zUf3rJo6MR5ZWRXGCSzYAAAAY3B5ZsyNWHjx5rtsAAIBBw9cbAADoA9LpdNz/4vY42tKesfads8bHsgkjc9IXAAAAvGnK6HhP5aREg+gOpN5zutnQAAAAAADgf9lwvKEntOt0wtCu108cGQ8trYwioV0AAAAk0JmOuGVVTdSebDQvAADIAcFdAADQB3x7+8F47uCJjHUzhw2Jjy2YnpOeAAAA4D9ddd60WDR2eMaBNLR3xs0raqO5I9mhIwAAAAAAGAxeOdYQH3t2czQmfH9+5aRR8eDSyijMc+QDAACA5Jo6uuK657fEoaZWYwMAgCzzFQcAAM6x7pssPvfKrox1RXmpuP+SiigpyM9JXwAAAPCfCnp+k1bG+CFFGYeyrb4pHnx5e6TTaQMEAAAAAGDQW3e0Pq55blM0JQzteuPkUfHAJRVCuwAAAPgphXmpjFM50tzWE951ur3DBAEAIIsEdwEAwDnU0tEZd6zeGm1dmQ8zX3P+jJhTPjQnfQEAAMD/NrK4MB5eWpVo89e/7jkWf7PtoCECAAAAADCorT1aH59YvjmaOroS1b95yui4b0lFFOQ56gEAAMBP++iC6ZF591bEtlNNcevK2ujoSvZ7FAAA6D1fcwAA4Bz61Pq6qGtozlj3uokj452zxuekJwAAAPh55o0qixsvmJloQJ99pS5ePnLKMAEAAAAAGJTWHKmPTzyXPLTrF6aOjnsWC+0CAADg57tkXHlcs2B6ohGtOnwqHl2zM9LptJECAEAWCO4CAIBz5Md7j8U/7jycsW5sSWHcfvHsSKWS3IkBAAAA2fWOmePjV2eOy1jXmY64ddXWONTU6pEAAAAAADCovHTkVHxi+eZo7kwW2vXWqWPirkXdoV32iAEAAPDqfqdiYvzm7AmJxvTdusPxZzX7jBQAALJAcBcAAJwDB5ta46GXt2es696GdffiihhRXJiTvgAAACCJ6xfOjPNGlmWsO9HaHjetrImWzk6DBQAAAABgUHjh8Km4dvmWaEkY2vW2aWPjzsVzhHYBAACQSCqVimsXzojXTRyZqP6LG/fED3cfMV0AADjLBHcBAECOdXSl487VW6OhPfOh5d+vmhyLxpXnpC8AAABIqig/Lx5aWhkjiwsy1m4+0RiPvLwz0um0AQMAAAAAMKCtOnQyrl++OVoThna9ffrYuH3R7MhPdV/xCAAAAMl0/468b0lFzBs5NFH9fS9tjzVH6o0XAADOIsFdAACQY3+6ZW+sO9aQse68UWXxwXlTctITAAAA9Nb40uK4f0ll5Cc4S/Qvu4/E32w7aMgAAAAAAAxYKw+ejBue3xKtXckusnjHjHFx+8VCuwAAADgzQwry4/Fl1TGxtDhjbXtXOm5csSXq6puNGwAAzhLBXQAAkENrjtbH1zfvzVg3tCA/7l9SEQV5/mQHAACg71o0rjw+On96otrPvlIXLxw+lfWeAAAAAAAg11YcPBGfXLEl2hKGdv3qzHFxy0WzIi+V4HYMAAAA+DlGlxTFpy6rjmGF+RlnVN/eGdcu3xzHW9rNEwAAzgIpAAAAkCP1bR1x1+qt0ZWg9qaLZsWkoSU56AoAAABem3dXTIw3Txmdsa4zHXHbqtrY39hi5AAAAAAADBjLD3SHdtUkDu36jVnj46YLhXYBAABwdswcXhqPXloVBQnCofc3tcYNz2+Jlo5O4wcAgNdIcBcAAORAOp2OB1/eHoea2zLWvn362PjFqWM8FwAAAPqFVCoVt188OyrKSzPWnmrr6Dm81GzjFwAAAAAAA8BzB07ETStroj1haNc7Z42PT14wM/ISHKYGAACApC4aWx53LJqdqHbjidNx1wvbojOd7LcsAADwswnuAgCAHPhu3eH4yb7jGeumlpXEDRfM9EwAAADoV4YU5Pfc2lheVJCxdtupprj/pe09IdcAAAAAANBfPbv/eNy0Inlo17tmT+jZG9Z9IQYAAACcbW+dNjY+PG9qotqn9h+Pz67f5SEAAMBrILgLAACybEd9Uzy5ri5jXUEqFfcvqYjSgnzPBAAAgH5n0tCSePCSyshPcN7o3/Yei2/W7s9FWwAAAAAAcNY9vf943LyyNjoSXlLxO3MmxnULZwjtAgAAIKveVz05fnnG2ES1f73tQPzNtgOeCAAAnCHBXQAAkEXNHZ1xy8raaO3sylj70fnTonpkmecBAABAv7VoXHlcs2BGotovbNgdzx88kfWeAAAAAADgbHpq37GePWFJQ7veXTExPn7+dKFdAAAAZF0qlYqbL5wVS8aVJ6r/1Lq6nnBqAACg9wR3AQBAFj22dmfUNTRnrFs6vjx+u2KiZwEAAEC/91tzJsTbp2e+tbH7ONMdq7fG7gS/mwEAAAAAoC/4yb5jceuqrdGZMLTrPZWT4poFQrsAAADInYK8vHhoaWXMGV6aeA/XpuOnc9IbAAAMJIK7AAAgS/5l15H4/q4jGetGFhfGnYvmRF4q5VkAAAAwIG5tvOnCWTFv5NCMtafbO+OTK2ridHtHTnoDAAAAAIAz9eO9x+K2VbWJQ7veWzUprp4/ree9OQAAAORSWWFBPHlZdYwpKcxY29rZFdc9vyX2N7bkpDcAABgoBHcBAEAW7KxvikfW7EhUe/eiOTG6pMhzAAAAYMAozs+Lh5dWxajizBu/6hqa454XtkVXwoNOAAAAAABwLkK77ljdHdqVrP4PqibHR84T2gUAAMC5M760OJ68bG6UFmSOEzjR2h7XLt8S9W0uYAQAgKQEdwEAwFnW0tHZc7NiS2dXxtr3VE6KpRNGeAYAAAAMyI1fDy+tjIJUKmPtMwdOxDc2781JXwAAAAAA0Bs/2nO0V6Fd76ueHH903tRIJXg/DgAAANlUNWJoPHBJZeQn+InafQHjTStroi3BeSgAAEBwFwAAnHVPrquL7fXNGesWjCqLq86b6gkAAAAwYC0cMzyuv2Bmotqvbt4bT+87nvWeAAAAAAAgqR/uPhJ3rt6aOLTr/XOnxIfnCe0CAACg71g2YWTccMGsRLUvH6mPB17eHul0wh/CAAAwiOWd6wYAAGCgbdT6bt3hjHXDC/PjviWVUZDnT3IAAAAGtl+fNT5+beb4RLV3v7g1tp9qynpPAAAAAACQyQ92H4m7X9gWXQlH9cG5U+JDQrsAAADoo3u43ls5KVHtD3Yfja9s2pP1ngAAoL+TEgAAAGfJ7obmeHjNjkS1ty+aExOHFps9AAAAg8L1F8yI80cPy1jX1NEV1z+/JU60tuekLwAAAAAA+Fn+ZdeRuKc3oV3zpsQH5k01TAAAAPqsq+ZPizdPGZ2o9htb9sX36g5nvScAAOjPBHcBAMBZ0NrZFbeuqu05YJzJb8+ZGFdMGmXuAAAADBqFeXnx8NLKGDukKGPtgabWuHllTbR3JT0OBQAAAAAAZ8/3dx2Oe19MHtr1R+dNjQ/MFdoFAABA35aXSsWdi+YkuoCx28Mv74hVh05mvS8AAOivBHcBAMBZ8Ol1dbH1VFPGunkjh8bVC6aZOQAAAIPO6JKieHRpVRTlpTLWrj3aEI+s2RHpdDonvQEAAAAAQLd/rjsc9724PZK+nb7qvGnxvuophgcAAEC/UJyfF49fWhVTy0oy1nam03HLytrYdqoxJ70BAEB/I7gLAABeox/uORr/sPNQxrqywvx44JLKKMzzZzgAAACD07xRZXHzRbMS1f5T3ZH4q60Hst4TAAAAAAB0+17d4bj/peShXR+dPy3+oHqy4QEAANCvlBcXxqcuq44RRQUZaxs7OuPa5VviSHNbTnoDAID+RGIAAAC8Bjvrm+Khl7Ynqr394tkxaWjmGykAAABgIHv79HHxW3MmJKr93Cu7YvmBE1nvCQAAAACAwe27Ow/FA70I7bp6/rR4b5XQLgAAAPqnqWVD4vFl1VGcl8pYe7i5La5/fks0dXTmpDcAAOgvBHcBAMAZ6n7hfPPK2mju7MpY+5uzJ8QbJo82awAAAIiIaxbMiKXjyzPOovsX9+2rt8aO+iZzAwAAAAAgK76z41A8+PKOxPXXLJgevye0CwAAgH5uwehhcfeSisgc3RVRc7Ix7ly9NTrTSSOvAQBg4Eul0/5CBgCA3ur+M/ruF7bFD/YczVhbNWJofO3K+VGULzcXAAAA/lNDW0e8/yevxK7TLRmHMqm0OP7kjQtiRHGhAQIAAAAAcNb8w46D8cianYnrP3H+9PidikmeAAAAAAPGt2r3x2df2ZWo9rfnTIxrF87Iek8AANAfSA4AAIAz8J2dhxKFdpUW5McDl1QI7QIAAID/ZVhRQTy+rDqGF+ZnnM3+pta4eWVNtHd1mSMAAAAAAGfFd3Yc6lVo17XnzxDaBQAAwIDz7oqJ8c5Z4xPV/vW2A/F32w9mvScAAOgPBHcBAEAvbT5xOp5cV5eo9vaLZ8fUsiFmDAAAAD/DtGFD4qGlVZGfSmWcz5qjDfHYmp2RTqfNEgAAAACA1+S7Ow/Fw2t2JK6/fuGM+O2KiaYOAADAgJNKpeLahTPj8gkjE9U/uW5nrDh4Iut9AQBAXye4CwAAeuFUW3vcsrI22rsyHxL+7TkT401TRpsvAAAAvIpF48rjhgtmJJrRd+sOx19vc2MjAAAAAABn7p/qDsdDLycP7brhgpnxrjlCuwAAABi4CvJScf8lFVE9YmjG2s50xK2rtsa2U4056Q0AAPoqwV0AAJBQVzod976wPQ40tWasnT+qLK5eMM1sAQAAIIFfnzUhfnP2hESz+uz6unjejY0AAAAAAJyB7+86HA+8tD0yX9v4H268YGbi99cAAADQnw0pyI/Hl1XHuCFFGWubOjrjuuVb4lhLW056AwCAvkhwFwAAJPTN2v3xXIKDweVFBfHgJZVRmOfPbQAAAEjqE+fPiCXjyjPWdUXE7au2xs76JsMFAAAAACCxf9l1JO57MXlo100XzorfENoFAADAIDJ2SFE8saw6Sgsyn4k61NwWNzy/JVo6OnPSGwAA9DWSBAAAIIGXjpyKL23YnbEuFRH3Lq6I8aXF5goAAAC9UJCXigcuqYxpZSUZaxs7Ons2fZ1qbTdjAAAAAAAy+uHu7tCubYlDu265aFb8+qzxJgsAAMCgUzliaNy3pDJRCMGmE41x1wvboiud9Bc3AAAMHIK7AAAgg6PNbXHH6q3RlWBS7587JZZOGGGmAAAAcAaGFxX03Ng4rDA/Y+3exta4eVVttHcl+cUOAAAAAMBg9a97jsbd3YeIE9bffOGs+NWZQrsAAAAYvC6fODKuXTgjUe1T+4/HFzbsznpPAADQ1wjuAgCAV9F9+PeWVbVxrKU945yWjCuPP5w7xTwBAADgNZg2bEg8eEll5Kcy1758pD4eWbMj0m5sBAAAAADgZ/jx3mNx9wvJLm3sduMFM+PXZgntAgAAgHfNmRjvmj0h0SC+Wbs//nHnIUMDAGBQEdwFAACv4jPrd8X6Yw0ZZzR2SFHcu6Qi8lMJThUDAAAAr2rJ+BFx3cKZiab0T3VH4s9q9psoAAAAAAD/w7/vOxZ3rK6NznSywdxwwcz4jYQHkgEAAGAw+MTCGXHZhBGJah9dsyNWHzqZ9Z4AAKCvENwFAAA/x7/sOhLf3n4w43y6w7oevKQyRhYXmiUAAACcJe+cPSF+Y9b4RLVf3Lg7frTnqNkDAAAAANDjqX3H4vZVWxOHdl23cEb8ptAuAAAA+KkzU/ctqYyK8tKMk+n+DX7LqtrYUd9kigAADAqCuwAA4GeoOdkYD7+8PdFsPrZgepw/epg5AgAAwFnWfVBq8djyRLX3vrgt1h9r8AwAAAAAAAa5p/cfj1t7QruSpXZ94vzp8VtzJma9LwAAAOiPhhbmxxPLqmNMSWHG2tPtnXHd8i1xvKU9J70BAMC5JLgLAAD+l1Nt7XHTippo7cq8ceuNk0fFb8+ZYIYAAACQBQV5efHg0sqYWlaSsbatKx2ffH5L7D3d4lkAAAAAAAxSz3aHdq2sTRzadc2C6fE7FZOy3hcAAAD0Z+NLi+PxZdVRkp85muBAU2t8csWWaOnszElvAABwrqTS6YRfpAAAYBDo3rB13fLNsfLQqYy1M4YNiW+8YUHPzRGDRbr1eHTu+NtXrcmf9a5IFY/KWU8AAAAMfLsbmuP9T22I+raOjLXTy0riq2+YH+VFmW94BAAAAABg4Fh+4ETctLIm2hNc2Njt6vnT4veqJme9LwAAABgoZ3ae3n88blpRE0l+eb95yui4b0lF5KVSOegMAAByr+Ac/DcBAKDP+uqmPYlCu0oL8uORpVWDKrSrR0dzpI+99Oo10345ojhXDQEAADAYTBs2JB5dWhVXP7spOjLcSbPrdEvcvKI2Pvu6uVGYl/mGRwAAAAAA+r8VB3sX2nXVeUK7AAAA6GP6wZmdKyaNimsWTI/PvLIrY+2/7T0WU8tK4o/Om5aT3gAAINecVgAAgP9268OfbNmXaB53LZodM4YPMTsAAADIkQvHDo/bF81OVPvy0fp48KUdkc4Q8gUAAAAAQP+38tDJuHFF8tCuD8+bGn9QPTnrfQEAAMBA9DsVE+PXZo5PVNt9Tuuf6w5nvScAADgXBHcBAEBE7G5ojnte2JZoFr9fNTmunDza3AAAACDHfmna2PjA3CmJav9l95H4RsKAbgAAAAAA+qfV3aFdz2+JtoShXR+cOyX+MOF7ZgAAAOCnpVKpuOGCGXHJuPJE43no5R3x0pFTRgkAwIAjuAsAgEGvsb2z58bFxo7OjLNYMq48Pnze1EE/MwAAADhXuoO73jptTKLar2zaE/+y60jWewIAAAAAIPdePHwqblhRE60JQ7v+sHpyfGCevV8AAADwWhXk5cWDSytj1vAhGWs70um4eUVN7GpoNngAAAYUwV0AAAxqXel03PPi1tiZ4OXvxNLiuH9JReSnUjnpDQAAAPjZNzbedtHsuGDMsETjuf+l7bHq0EmjBAAAAAAYQF46cique35LtHZ2Jar/g6rJ8SGhXQAAAHDWlBUWxBPLqmNkcWHG2vr2zrhu+eY42druCQAAMGAI7gIAYFD7+ua98fT+ExnrivNS8fDSyihP8DIZAAAAyK6i/Lx4dGlVTC0ryVjb2X1j48raqD3Z6LEAAAAAAAwAa47Ux3XLk4d2vbdqUvzReVN7LoYAAAAAzp5JQ0vi8WVVPeeuMtnb2Bo3rqiJtoS/5wEAoK8T3AUAwKD17/uOxdc2701Ue9NFs6J6ZFnWewIAAACS6Q7X/tRl1VFeVJCxtqmjMz6xfHMcaGw1XgAAAACAfmzt0fq4dvnmaEl4yPc9lZPiI+dNE9oFAAAAWTJ/1LC4a3FFotp1xxrigZe3Rzqd9jwAAOj3BHcBADAobT3VGPe+sC1R7W/MGh9vnz4u6z0BAAAAvTO1bEg8emlVFCa4sfFYS3tPeNeptnZjBgAAAADoh9Yfa+gJ7WpOGNr17oqJcfV8oV0AAACQbW+aMjo+Mn9aotof7D4aX9+8N+s9AQBAtgnuAgBg0DnZ2h6ffL4m0QauBaPK4tqFM3LSFwAAANB7F4wZHncsmpOotq6hueedQGvCQ10AAAAAAPQNrxxriI8/tzmaOpK93/2tORPimgXTI5XKfPEDAAAA8Nq9t3JS/PKMsYlqv7p5b/xw9xFjBwCgXxPcBQDAoNLR1RW3rKqNA02tGWvHlhTGw0urojDPn80AAADQl/3i1DFxdcIbG9cda4i7Xtganel01vsCAAAAAOC123j8P0O7OhPVv2v2hLj2/BlCuwAAACCHusOzb7pwVlw8dnii+vte2h7rjtZnvS8AAMgWCQQAAAwqn16/K14+kvmlblFeKh69tDrGDCnKSV8AAADAa/Oeyknxm7MnJKr9yb7j8el1dZEW3gUAAAAA0KdtPnE6rnluczQmDO36jVnj47qFQrsAAADgXCjMy4uHl1bFjGFDMta2d6XjxhU1se90S056AwCAs01wFwAAg8Z3dx6Kb28/mKj2lotmx7xRZVnvCQAAADh7NzZeu3BGXDlpVKL6v91+ML619YDxAwAAAAD0UVtOnI6PPbspTrcnC+36tZnj44YLZva8LwYAAADOjeFFBfHEsuoYUVSQsfZkW0dc9/yWaGjryElvAABwNgnuAgBgUFh3tD4eXbMzUe27KybG26aPzXpPAAAAwNmVn0rFPUvmxPmjhyWq/9wru+IHu494DAAAAAAAfUzNycae0K6GhKFdvzJjXNx44czIE9oFAAAA59yUspJ49NKqKMzLHK5d19Act6ysjY6urpz0BgAAZ4vgLgAABrxDTa1xc/cL3HQ6Y+3S8eVx9YLpOekLAAAAOPtK8vPj8UurYnpZSaL6e1/cHs8fPOFRAAAAAAD0EbUnG+PqZzdFfcLQrnfMGBc3XzRLaBcAAAD0IQvHDI87Fs1JVPvCkVPx6JqdkU5w9gsAAPoKwV0AAAxoje2dcd3zW+J4a3vG2qllJXHfksrId+siAAAA9GvlxYXx6cvnxqjiwoy1nel0T+D3+mMNOekNAAAAAICfb+up/xva1daRaExvnz42bhHaBQAAAH3SL04dEx+cNyVR7XfrDse3th7Iek8AAHC2CO4CAGDA6j54e+fqrbHtVFPG2tKC/Hjs0qoYXlSQk94AAACA7Jo0tCQ+dVl1lBZk/hzW2tkV1y3fHNsTvEMAAAAAACA7tnWHdj2zKU4lDO36pWlj4raLZ0eeixoBAACgz3p/9ZR467QxiWo//8queGrfsaz3BAAAZ4PgLgAABqzPrd8Vzx08kbEuFRH3LamImcNLc9IXAAAAkBvVI8vioUuqIj/Boa2G9s74+HObYn9jS056AwAAAADgv3RfrPDRZzbFyYShXW+dOibuWDQn0ftfAAAA4NxJpVJx20WzY+HoYRlr0xFx5wvbYvOJ0znpDQAAXgvBXQAADEj/sONQ/NW2A4lqr5o/LS6fODLrPQEAAAC5t3TCiLj1olmJao+0tMc1z22O4y3tWe8LAAAAAID/sKO+KT767MbEoV1vmTJaaBcAAAD0I0X5efHopVUxeWhxxtrWzq644fktcaipNSe9AQDAmRLcBQDAgLP60Ml4fO2OxJu43ls5Kes9AQAAAOfO/zdjXFw9f1qi2j2nW+ITyzfH6fZkB8QAAAAAADhzO7tDu57ZFCdak72TffOU0XH34oooyEsZOwAAAPQjI4oL48llc2NYYX7G2qMt7XH981uisb0zJ70BAMCZENwFAMCAUlffHLesqo3OdOba80aWxe2LZkcqZRMXAAAADHS/VzU5frdiYqLampONceOKmp7bGwEAAAAAyN5er488symOt7Ynqn/T5NFxj9AuAAAA6LdmDB8SDy+tivwEZ7m2nmqKO1Z3nxFLcEgMAADOAcFdAAAMGCdbu29T2BynE9ymMH5IUTy2rCpK8jPf0gAAAAAMDB9bMD3ePn1sotqXjtTHnau32vgFAAAAAJAFuxqa4yPPbkwc2vWGyaPi3iVzoiDPJY0AAADQny0aVx43XzgzUe3ygyfjM+vrst4TAACcCcFdAAAMCG2dXXHTyprY29iasba0IC+euKw6RpcU5aQ3AAAAoG9IpVJx60Wz43UTRyaqf2r/8Xjgpe3R5dZGAAAAAICzZnd3aNczG+NYS7LQrisnjYr7l1REQZ7jDwAAADAQvGPm+Hhv5aREtX+z7WB8e/vBrPcEAAC95csVAAD9XjqdjofX7Ii1Rxsy1nbft3jfksqoKB+ak94AAACAvqUgLxX3X1IRF4wZlqj++7uOxJPr6nrePwAAAAAA8NrsOd0cH3l2UxxNGNr1+okje97pCu0CAACAgeWq+dN6wrqTeHLtzlhx8ETWewIAgN4Q3AUAQL/3jS37eg7RJvHx86fH5RNHZr0nAAAAoO8qyc+Pxy+tjory0kT13Tc2fmHj7qz3BQAAAAAwkO093RIfeWZTHGluS1T/uokj48GllVGY59gDAAAADDR5qVTcs3hOzB05NGNtV0TcumprbDvVmJPeAAAgCV+wAADo1/657nB8ZdOeRLW/NnN8/PaciVnvCQAAAOj7hhUVxGcunxuThxYnqv/zmv3xp1v2Zr0vAAAAAICBaF9PaNfGOJwwtOvyCSPjwUuEdgEAAMBAVlLwHxcwjh9SlLG2qaMzrn9+SxxrSfZuAQAAsk1wFwAA/dbKQyfjwZd3JKpdPLY8brhgRqRSqaz3BQAAAPQPo0uK4rOXz4tRxYWJ6r+4cU/89dYDWe8LAAAAAGAg2d/YEh95dmMcShjatWzCiHhoaWUU5TvuAAAAAAPdmCFF8cRl1VFakPk9wMGmtvjk8zXR0tmZk94AAODV+JIFAEC/VHuyMW5ZWRud6XTG2ullJfHg0sooyPPnLwAAAPA/TSkric9cPjfKCvMTjeZT6+viezsPGSMAAAAAQAIHGlvjI89s6jlYm8Sl40fEw0urhHYBAADAIFJRPjTuW1KZKPhg44nTce8L26MrwZkyAADIJskFAAD0O4eaWuO65ZujqSPz7QjlRQXx5GVzY3hRQU56AwAAAPqfyhFD49OXzY0h+ck+nT348o741z1Hs94XAAAAAEB/drCpNa56ZmMcaGpNVH/JuPJ45NKqKE74rhYAAAAYOC6fODKuXTgjUe2P9x2LL2/ck/WeAADg1fiiBQBAv9LQ1hGfWL45jrS0Z6wtzEvFI0urYkpZSU56AwAAAPqvBaOHxePLqqMoL5Wxtvuuxrtf2BbP7D+ek94AAAAAAPqb/Y0t8UdPJw/tWjKuPB5dJrQLAAAABrN3zZkYvzl7QqLaP63ZF/9cdzjrPQEAwM8juAsAgH6jrbMrblxZEzvqmxPV37loTlw4dnjW+wIAAAAGhkXjyuOhpVWRn8oc3tWZTsetq2pj5aGTOekNAAAAAKC/2Hu696Fdjy2ripL8/Kz3BgAAAPRtnzh/Rlw6fkSi2ode3hEvHTmV9Z4AAOBnSaXT6e5LwQEAoE/rSqfj7he2xQ/3HE1Uf/X8afF7VZOz3tdgk+5qj2jOcBvFkHGRyivMVUsAAABw1v1477G4fVVtdCWoLc5LxePLqmNJws1iAAAAAAAD2e6G5vjIs5viSHNbovpFY4fHE8uqo6RAaBcAAACDmzM7/+V0e0d8+KmNsa2+KePchhfmx9ffsCCmDRuS1ecDAAD/m+AuAAD6hT/esCv+vGZ/otrfnD0hrl84I1KpVNb7AgAAAAamf647HPe9tD1RbXd415OXzY1F48qz3hcAAAAAQF+1qzu065mNcbSlPVH9RWOHx5PLqmOI0C4AAADgfznY1Brv+/dX4nhr5vcMU4aWxDfeMD/KiwvNEQCAnMnL3X8KAADOzLe3HUgc2nXFpJFxrdAuAAAA4DX6/2aMixsumJmotrUrHdc9vyVeOnLK3AEAAACAQWlHfVP80dPJQ7suHDNMaBcAAADwc00oLY4nllVHcX7mOIS9jS1x48qaaOvsMlEAAHJGcBcAAH3aD3cficfX1SWqnT+qLO5dXBH5qVTW+wIAAAAGvt+cPSE+On9aotrWzq64bvmWWHOkPut9AQAAAAD0JdtPNcVHntkYx1uThXZd0B3addncGFKQn/XeAAAAgP5r3qiyuHvxnES1a482xEMv74h0Op31vgAAoJvgLgAA+qznD56Ie17cnqh2ytCSeHxZdZTYzAUAAACcRe+tmhzvq56cqLalsyuuXb451h4V3gUAAAAADA61Jxt7QrtOtHYkqr9o7PD49GVzo9Q+LwAAACCBN04enfjyxX/ZfST+tGafuQIAkBOCuwAA6JNeOdYQN6+sjc4EtxyMKCqIT19eHSOLC3PSGwAAADC4fHje1Hh3xcREtc3/N7xr/bGGrPcFAAAAAHAu1ZxsjI8+uylOtiUL7Voyrjw+taw6hgjtAgAAAHrh9yonxS/PGJuo9ksb98SP9hw1XwAAsk5wFwAAfc72U01x3fLN0drZlbG2OD8vnrisOqaWDclJbwAAAMDgk0ql4poF0+O35kxIVN/U0RUff25zTzA5AAAAAMBAtPnE6fjoMxujPmFo19Lx5fHYsqooEdoFAAAAnMH+rZsunBUXjx2eqP7eF7fZuwUAQNYJ7gIAoE/Z39gSH39uU9S3dyb6Y/a+JRUxf9SwnPQGAAAADO7NX9eePyPeNTtpeFen8C4AAAAAYEDacLwhrn52UzQk2OPVbdmEEfHopdVRkp+f9d4AAACAgakwLy8eXloV08tKMta2daXjkyu29JxTAwCAbBHcBQBAn3GspS0+9uzmONLSnqj+lotmxRWTRmW9LwAAAID/DO+6buGM+I1Z4xMNpLGjM655blOsPVpvgAAAAADAgLD+WEPPHq/TCUO7XjdxZDyytCqK8x1dAAAAAF6b4UUF8cRl1VFeVJCx9kRrR1y3fEucbu8wdgAAssLXLwAA+oTul6DXLt8SexPeZPDR+dPiHTOTHZIFAAAAOJvhXTdcMDN+LeF7iaaOrvj4c5vjxcOnPAQAAAAAoF9bc7Q+Pv7cpmjqSBbadeWkUfHQ0sooEtoFAAAAnCVTy4bEI5dWRWFeKmPtzobmuGVlbXR0dZk/AABnneAuAADOudbOrvjk8zVRc7IxUf3vVkyM36uclPW+AAAAAH6WvFQqbrxwZvzKjHGJBtTS2RXXLd8cKw+eNFAAAAAAoF/qfr/ZfUlB92UFSbxx8qh44JKKKMxzZAEAAAA4uy4cMzxuu3h2otrVh0/F42vrIp1OewwAAJxVvoIBAHBOtXd1xS0ra+Llo/WJ6t8+fWx8bMH0SKUy34oAAAAAkM3wrpsvmhW/PGNsovrWrnTcsGJLPHfghIcCAAAAAPQrT+873vN+s/uCxiTeMmV03LekMgqEdgEAAABZ8kvTxsb7505JVPudnYfiL2r3exYAAJxVgrsAADhnOrrSccfqrbH84MlE9a+bODJuvWi20C4AAACgz4R3db+r6A4aT6K9Kx03raiJp/Ydy3pvAAAAAABnww92H4lbVtX0vN9M4q1Tx8TdiyuiIM/FjAAAAEB2fXDulPiFqaMT1X5+w+741z1HPRIAAM6aVDqdTvYFDQAAzqLOdDrueWFb/DDhC88LxwyLT18+N0ry8z2HcyjdVh9dB5561Zq8iVdGqmh4znoCAACAvvCe44GXtsf3dx1JVJ+firhncUW8ZeqYrPcGAAAAAHCmvrPjUDyyZkckPXDQfcnBbRfPjvyU0C4AAADoLWd2zkxrZ1d89JmN8crx0xlrC/NS8dnL58ZFY8vP8L8GAAD/peC//W8AAMiJrnQ6Hn55R+LQrsry0nh8WbXQrr6gvSG69vzTq5bkjbk4QnAXAAAAg0j3IbTbL54dBalUfLfucMb6znTEnau3RntXOt42fWxOegQAAAAA6I2/2ro/Pr1+V+L6d8wYF7dcNCvyhHYBAADAmXFm54wU5+fFY8uq4w///ZXY39T66iPuSseNK2riK1fOj1nDS8/sPwgAAP9X3n/+DwAAyIV0Oh1PrN0Z30twiLXblKEl8enL50ZZocxZAAAAoO/qPox280Wz4p2zxieq74qIe1/cFt/ZcSjrvQEAAAAA9GZ/19c37+1VaNevzRwvtAsAAAA4Z0YWF8YTl1VHWWF+xtqG9s64dvnmONLclpPeAAAYuAR3AQCQ001dn3tlV/xdwgOpY0sK43OvmxujS4qy3hsAAADA2QjvuuGCmfHuiomJ6tMR8fCaHfHnNfsMHwAAAADoE/u7Pr9hd3xl057Ea7rfh9504cye96MAAAAA58qs4aXx0CWVkZ/gHcXBpra4bvnmaGzvzElvAAAMTIK7AADIme4NXd/aeiBR7cjigvj8686LSUNLst4XAAAAwNmSSqXimgXT4/erJide88cbdscfb9jVcygOAAAAAOBc6Eyn45E1O+MvavcnXvPBuVN63od2vxcFAAAAONeWjB8Rt108K1Ft7ammuHVVTXR0dWW9LwAABibBXQAA5MSfbtkX39iyL1Ht8KKC+Nzr5sWM4UOy3hcAAADA2dZ9SO2q86bGB+dNSbzmz2v29xyK6z4cBwAAAACQS22dXXH7qtr4zs5Didd0B3Z9YN5UoV0AAABAn/L26ePiQ/OmJqpdeehUPPTyDhcuAgBwRgR3AQCQdd+q3R9f3Lg7Ue3Qgvz47OVzo6J8aNb7AgAAAMhmeNcH5k6Nj8yflnhN96G4O1dvjXa3OAIAAAAAOdLY3hnXLd8S/77veOI1N104M363clJW+wIAAAA4U39YPTl+Zca4RLX/vOtIfH3zXsMGAKDXBHcBAJBVf16zLz77yq5EtUPy8+LTl8+NuSPLPBUAAABgQPj9qsnxifOnJ67/t73H4pPP10RLR2dW+wIAAAAAONnaHlc/uzFeOHIq8eGDuxbNiV+fNcHwAAAAgD596eKNF86MS8ePSFT/1c1745/qDme9LwAABhbBXQAAZM2fbtkbf7xhd6La4rxUPHFZdZw/epgnAgAAAAwov1MxKW68YGakEtavOHQyPr58c5xu78hyZwAAAADAYHWwqTU+9NSG2HSiMVF9QSoVDyytjLdNH5v13gAAAABeq4K8vHhwaWVUjRiaqP6hl7fHyoMnDR4AgMQEdwEAkBVf27wnvrhxT6LawrxUPHppdVw8ttzTAAAAAAak35g9Ie5ZPCfyU8niu9YebYirnt4Yx1vas94bAAAAADC47Kxvig8+tSF2nW5JfCnjY8uq4o2TR2e9NwAAAICzpbQgPz51WXVMLC3OWNuZjrhlVU3UnEwWcg4AAIK7AAA4q9LpdHx105746qa9ieq7D6s+eEllLJ0wwpMAAAAABrRfnDY2Hr20queQWxK1p7oPz70SexMengMAAAAAyGTj8Yb48NMb43BzW+IDrp++fG4smzDScAEAAIB+Z3RJUU941/DC/Iy1TR1dcd3yzXGgsTUnvQEA0L8J7gIA4KyGdn1505742ua9if8YvXfJnHj9pFGeAgAAADAoXD5xZM8ht+7DbknsbWyNDzz1Smw+cTrrvQEAAAAAA9vKQyfjo89silNtHYnqRxYXxpeuOC8uGlue9d4AAAAAsmXm8NJ4dFl1FCW4cPFoS3t8YvnmqE/4/gQAgMFLcBcAAGcttOsLG3fHn2zZl/gP0bsXz4k3TxnjCQAAAACDSvchty+8fl6MKCpIVH+itSOuenpjrDh4Iuu9AQAAAAAD0z/XHY7rlm+J5s6uRPUTS4vjK1ecF1Ujhma9NwAAAIBsu3DM8Lhr8ZxEtXUNzXH981uipaMz630BANB/Ce4CAOCshHZ9fsPu+POa/Ynq81MR9y6piF+cNtb0AQAAgEFp7siy+PIV82PckKJE9d2H6a5/via+v+tw1nsDAAAAAAbW3q6vbd4T9720PTrT6URrZg8fEl+9cn5MGzYk6/0BAAAA5Mqbp4yJaxZMT1S7/lhD3LZqa3R0JQtBBwBg8BHcBQDAa9KVTseT6+riL2qTh3bdt6Qy3jJ1jMkDAAAAg9qM4UPiK1fMj6llJYnquw/V3fvi9vjTLXt7DtsBAAAAALya7oOlD768I766aW/iQZ0/elh86Yr5MTbhpQMAAAAA/cm7KybGu2ZPSFT73METPe9W7NUCAOBnEdwFAMAZ6+hKx/0vbY+/3X4wUX1+KhUPXFIZb5oy2tQBAAAAImLi0OL48hXnRUV5aeJ5fHHjnnhs7c6eIC8AAAAAgJ+lsb0zbni+Jr5XdzjxgC4dPyI+d/ncGF5UYKgAAADAgJRKpeITC2fElZNGJar//q4j8fkNu7PeFwAA/Y/gLgAAzkhbZ1fctqq25+VjEgWpVDy0tDLeMFloFwAAAMB/N7qkKL50xXlx8djhiQfz9zsOxa0ra6Ols9MwAQAAAID/4WhzW1z1zMZYcehk4sm8deqYeHxZVZQU5JsmAAAAMKDlp1Jxz5I5sWBUWaL6v6jdH9+s2Zf1vgAA6F8EdwEA0GvNHZ1x/fNb4qn9xxPVF+al4uGllXFFwpsIAAAAAAabssKC+PRlc+PNU5KHnne/m7n6mU1xorU9q70BAAAAAP3Hzvqm+MBTG6LmZGPiNe+aPSHuWjwnCvIcLwAAAAAGh5L8/Hh8WXVMLytJVP/5Dbvj+7sOZ70vAAD6D1/WAADolfq2jvjYs5ti9eFTieqL8lLxyNKqeJ3QLgAAAIBXf4+Snxf3LamI35kzMfGkXjl+Ov7w31/pOYwHAAAAAAxua47Uxwef2hAHmloTr/nYgulx3cIZkZdKZbU3AAAAgL5mRHFhfObyeTF2SFGi+gde2h7PHTiR9b4AAOgfBHcBAJDYsZa2uOqZjT0HQpOGdj12aXVcNnGkKQMAAAAk0H047hMLZ8Q1C6Ynntf+ptb4wFMb4sWEQesAAAAAwMDzT3WH4+pnN0VDe2ei+sK8VNy/pCLeUzkpUkK7AAAAgEFq4tDi+Ozlc2N4YX7G2s50xK0ra2Lt0fqc9AYAQN8muAsAgEQONLbGh5/eGNtONSWqH5KfF09eNjeWThhhwgAAAAC99LuVk+LexXOiIOGBudPtnXHNc5vje3WHzRoAAAAABpHOdDo+u74u7n9pe3Sk04nWDCvMj89dPi/eMnVM1vsDAAAA6OtmDS/tOQdXnJ85eqG1Kx03PL8ltp5qzElvAAD0XYK7AADIqK6+OT789IbYc7ol+cau182LxePKTRcAAADgDP3itLHx6cvnRmlB5tsc//OA3gMvbY8/3rAruhIe0AMAAAAA+q+mjs64aUVNfGvrgcRrJpQWxVeunB8Xjh2e1d4AAAAA+pMFo4fFQ5dURn6CexYb2jvjY89ujt0NzbloDQCAPkpwFwAAr2r9sYb44NMb4lBzW6JJjSoujC9ecV7Py0oAAAAAXpvuYPQvX3FejC4pTLzmz2v2x+2rtkZLZ6fxAwAAAMAAdbCpNT741IZ49sCJxGuqRgyNr1+5IGYNL81qbwAAAAD90WUTR8btF89JVHuitT2ufnZTHGhszXpfAAD0TYK7AAD4uZ7edzyufmZj1Ld1JJrSxNLi+MqV50VF+VBTBQAAADhLKkcMja9dOT9mDhuSeM2P9x2LjzyzKY61JAtjBwAAAAD6j1eONcT7/v2V2HaqKfGapePL44uvPy/GDCnKam8AAAAA/dnbpo+Njy+Ynqj2UHNbXP3sxjjabI8WAMBgJLgLAICf6e+3H4ybV9ZEa1c60YRmDBsSX77ivJhalvwAKQAAAADJTBpaEl+9cn4sGVeeeGQbj5/uOby35cRpYwYAAACAAeKHe47GR57ZGMdb2xOv+eUZY+OJZdUxtDA/q70BAAAADATvrpwU762clKh2b2NrfOy5TXGyF+9qAAAYGAR3AQDwP6TT6fjiht3x6Nqd0ZVwNlUjhsaXrjgvxpcWmyYAAABAlgwrKohPXVYdvzJjXOI13bc6fujpjfGve456LgAAAADQj3Wm0/GljbvjztVboy3hZYzdPjJ/Wtx20ewoyHN0AAAAAKA371S6w9CT2FHfHB9/bnOcbu8wYACAQcTXNwAA/p+Orq6476Xt8ac1+xJPZeHoYfGF18+LkcWFJgkAAACQZd2H6265aFZ8bMH0SCVc09rZFXes3hp/vGFXz+E+AAAAAKB/qW/riOuXb4k/2ZJ8X1dJfl48srQyfr9qcqRSSd8mAgAAANCt+33KzRfOjisnjUo0kC0nG+O65VuiuaPTAAEABgnBXQAA9Gjq6Izrn98S3991JPFELh0/Ij57+dwoKywwRQAAAIAcbgp7T+WkeGhpZRTnJ//c9+c1++OTz29xsyMAAAAA9CPbTzXF+/59faw4dDLxmrFDiuIrV86PKyePzmpvAAAAAANZQV4q7ltSEUvHlyeqX3esIW5cURNtnV1Z7w0AgHNPcBcAAHGspS2uenpjrDx0KvE03jZtbDy+rCpKCvJNEAAAAOAceMPk0fGl158Xo0sKE69ZfvBkvP8nG2J3Q3NWewMAAAAAXrsf7z0W7//JK7G3sTXxmrkjh8afvGFBVI0Y6hEAAAAAvEZF+XnxyNKquGDMsET1qw+fittW1UZHl/AuAICBTnAXAMAgt/VUY/zhT16JLScbE6/5g6rJceei2VGQ589JAAAAgHNp3qiy+MYbFsTs4UMSr6lraO55H7Ty4Mms9gYAAAAAnJnOdDo+/8quuHVVbTR3Jj/k+eYp/xH2P3ZIkdEDAAAAnCUlBfnxxLLqnsD0JJ45cCLuemFbdHSlPQMAgAFM0gIAwCD23IET8aGnNsTBprbEfzzeeMHMuGr+tEilUlnvDwAAAIDMJpQWx1eunB+XTxiZeFwN7Z1x7fLN8c2afZFO2yAGAAAAAH3Fqdb2+MRzm+Obtft7te4Dc6fE/Usqeg6SAgAAAHB2lRUWxGcum5v4gsV/23ss7nphq/AuAIABLJW2Ex8AYNDp/hPwb7YdjM+sr4uk9zEW56XiviWVccXkUVnujr4s3dEU6ZObXrUmNWJepApKc9YTAAAA8B860+n48sY98Wc1+3o1kismjYw7F83p2VwGAAAAAJw7NScb46YVNXGgqTXxmqK8VNy+aE784tQxWe0NAAAAyC5ndvqHYy1t8aGnNsbexpZE9W+eMjruWVwRBXmprPcGAEBuCe4CABhkOrq64om1dfEPOw8lXjO8MD8eX1YdC8cMz2pvAAAAALx2P9pzNO57aXu0diaNbI+YMrQkHr60MirKh3oEAAAAAHAOLmL8zs7D8al1O6OtK5143YTSonh4aVXMHVmW1f4AAAAA+C8Hm1rjw09viINNbYnG8pYpo+Nu4V0AAAOO4C4AgEGkoa0jbl1VG6sPn0q8ZmJpcXzqsuqYObw0q70BAAAAcPZsOXE6blxRE4eak20O61acnxc3Xzgr3jZ9rEcBAAAAADnS2N4ZD6/ZHv+651iv1i0aOzzuv6QyRhYXZq03AAAAAH623Q3N8eGnN8bx1vZEIxLeBQAw8AjuAgAYJPadbonrnt8SdQ3NiddUlJfGpy6bG2OHFGW1NwAAAADOvmMtbXHzytpYf6yhV+t+feb4uHbhjCjKz/NYAAAAACCLak829lzEuOd0S6/WvbtiYnx0/vQoyEtlrTcAAAAAXt32U03xR89sjPq2jkSj+oWpo+OuRRXe6QAADBCCuwAABoE1R+rjppU1cSrhS8Bul44fEfdfUhFlhQVZ7Q0AAACA7Gnv6orH1uyM79Yd7tW6eSOHxkNLq2JCaXHWegMAAACAwSqdTve8s3ty7c5o7UonXlecnxe3Xzw7fmHqmKz2BwAAAEAyNScb4+pnNyUO7/rFqWPizkVzhHcBAAwAgrsAAAb4Bq+/23EoPrWuLjrTyTd4/dacCXHNghleAAIAAAAMkHdEf7/jUDzZy3dE5UUFcffiObFswsis9gcAAAAAg0lTR2c88vKO+MGeo71aN3locTxyaVVUlA/NWm8AAAAAnGF41zMbo769M3F4112L50R+KmXcAAD9mOAuAIABqq2zKx5duyP+qe5I4jX5qYjrF86M35g9Iau9AQAAAJB7a4/Wx22rauNoS3uv1v1uxcS4av60KMzLy1pvAAAAADAYbD3VGLetrI1dp1t6tW7p+PK4d0lFlBcVZq03AAAAAHIX3vXmKaN7LlW0JwsAoP8S3AUAMAAdbm6Nm1fWxsbjpxOvGVqQHw8urYyl40dktTcAAAAAzp1jLW1x++qt8fKR+l6tmzdyaNy3pDKmlJVkrTcAAAAAGKi60un4220H44837Iq2rnTidamI+IPqyfHBeVMjP9X9/wAAAADoq7acOB0fe3ZT4vCuZRNGxENLK6MkPz/rvQEAcPYJ7gIAGGDWH2uIm1fWxLGW9sRrJpUWxxOXVces4aVZ7Q0AAACAc6+jKx1f3rg7/rx2f6/WlRbkx80XzYpfnDoma70BAAAAwEBztLkt7ntpW6w8dKpX60YWF8TdiytcxAgAAAAwgMO7Lho7PB6/tDqGFgrvAgDobwR3AQAMIN/ZcSgeX7szOtLJb2VcOHpYPHJpVYwsLsxqbwAAAAD0LU/vOx73vLgtGjuSbRL7T788Y2xcv3BmDCmwWQwAAAAAXs0z+4/HAy9tj5NtHb0a1IVjhsV9Sypj7JAiAwYAAADoh+FdVz+7KRoShnedN7IsPnV5dZQXOd8HANCfCO4CABgA2ru64om1dfGdnYd6te6Xpo2JWy+aHUX5eVnrDQAAAIC+a3dDc9yysja21Tf1at2MYUPiviUVUTliaNZ6AwAAAID+qqWjMz6zflf8Qy/3c3X7g6rJ8cF5U6MgL5WV3gAAAADIvs0nTsfHehHeNWd4aXz2dXNjdIkgdwCA/kJwFwBAP3ewqTVuXVUbG4+fTrymO6br6gXT490VEyOVssELAAAAYLAfInx4zY74P7uP9mpdUV6q5x3Tb86eEHneMQEAAABAj5qTjXHn6q1R19Dcq4mUFxXEPYvnxKUTRpokAAAAwCAM75paVhKff928mFBanPXeAAB47QR3AQD0YysOnog7X9gW9W0didcML8yP+y+pjEvGj8hqbwAAAAD0H+l0Or5bdzieXFcXrZ1dvVq7eGx53LFodoy3YQwAAACAQawznY6/rN0fX9q4JzrS6V6tPX/0sLh/SYV3bAAAAAADMOT9mmc3xcmE5//GDynqCe+aNmxI1nsDAOC1EdwFANBPN3l9bdOe+JMt+6I3W7zmDC+NRy+tisllJVnsjoEs3dEU6ZObXrUmNWJepApKc9YTAAAAcPbsqG+K21bVxo765l6tKyvMj+sXzoxfmjYmUqmURwIAAADAoLK7oTnueXFbbDh+ulfrut+k/V7VpPjwvKlRkJeXtf4AAACAvs+ZnYFrZ31TfOy5zXGkuS1R/ajiwvjM5XOjcsTQrPcGAMCZE9wFANDPHG9pjztXb40Xjpzq1bo3Th4VdyyaE6UF+VnrjYEv3bgvOtbc/ao1BRfeHamhk3PWEwAAAHB2tXR2xmfW7Yp/2Hmo12vfMHlU3HzhrBhRXOixAAAAADDgdaXT8TfbDsYXN+yK1q7eXMEYMW5IUdy9eE5cPLY8a/0BAAAA/YczOwPb/saW+Ogzm2J/U2ui+u4zgI9eWhWLx3l3BADQV7mWBwCgH1l3tD7e++N1vQrt6r6V8SPzp8WDl1QK7QIAAAAgo5L8/Ljpolnx0NLKKCvsXQj8T/Ydj9/50bp47sAJkwYAAABgQNt7uiWuemZjfHp9Xa9Du940eXR8680LhXYBAAAADBKThpbEl688L2YMG5KovqmjMz7+3Ob4P7uPZL03AADOjOAuAIB+IJ1Ox7dq9/ds9DrS0p54XffByicvq47fr5ocqVR3hBcAAAAAJPPGyaPjm286PxaMKuvVyI63tsf1z2+JB1/aHo3tncYNAAAAwIDSlU7H320/GO/5t3Wx9mhDr9aWFuTFnYtmxwOXVMTwooKs9QgAAABA3zNuSHF8+YrzomrE0ET1nel03P3CtvjTLft6zhcCANC3CO4CAOjjTrW2xydX1MRnX9kVnb14vzZr+JD4kzcsiGUTRmazPQAAAAAG+E2PX7rivPiD7mD4Xq79bt3h+J0frY3nD57IUncAAAAAkFsHGlvjmmc3x2Nrd0ZzZ1ev1p43qiy++aaF8fbp41zCCAAAADBIjSgujC+8fl6cP3pY4jVf3Li7531Ud5AXAAB9h+AuAIA+7OUjp+J3f7w+nj3Qu8ONvzRtTHzjDQti2rAhWesNAAAAgMGhIC8vrpo/LT7/unkxfkhRr9Yeam6La5dvibte2BonW9uz1iMAAAAAZFP3oci/2XagJ6j+hSOner1h//1zp8RXrjgvppSVZK1HAAAAAPqHssKC+Ozlc+OSceWJ1/z9jkNx04qaaOnozGpvAAAkJ7gLAKAP6uhKx1c37YmPPrMpjjS3JV5XlJeKWy6aFXctmhNDCvKz2iMAAAAAg8uiceXxl29ZGG+bNrbXa3+w+2j89o/Wxr/uORppNz8CAAAA0I/sqG+KDz21IZ5cVxfNnV29WjtlaEl86Yrz4kPzpvYE5AMAAABAt+6zf48vq44rJ41KPJBnD5yIjzy7KU64QBEAoE/w9Q8AoI851NQaH312Y3xt897ozTavSaXF8dUr58evzhwfqVQqix0CAAAAMJhve7xr8Zx4eGlljCgq6NXaE60dccfqrXHD8zU978AAAAAAoC9r6+zquXzx9/5tfWw4frrX6981e0J8683nx8Ixw7PSHwAAAAD9W1F+Xjy4tDJ+Y9b4xGs2Hj8dH/jJhtjd0JzV3gAAyExwFwBAH/L0/uPxnn9bF2uPNvRq3esnjow/f9P5UT2yLGu9AQAAAMB/esPk0fGXb1kYr5s4stdDee7gifjtH62Lf9hxMLrSaUMFAAAAoM955VhDvPfH63suX+zo5TusiaXF8YXXz4vrL5gZJQX5WesRAAAAgP4vP5WKT14wMz46f1riNXsbW+L9P3klVh86mdXeAAB4dYK7AAD6gNbOrnhszY64cUVN1Ld3Jl6Xn4r42ILp8eilVTGsqCCrPQIAAADAfze6pCgeu7Qqbrt4dpQW9O6zY1NHZzyyZmd8+OmNUXuy0WABAAAA6BO631s9sXZnfPCpDbGzobnX63995viewPuLx5ZnpT8AAAAABp5UKhXvrZocdy+eEwWpVKI13WcQP7F8c/zNtgORdnkiAMA5Id0BAOAc6z6YePcLW2N7fe82eo0pKYz7L6mMC8cMz1pvAAAAAJBp09g7ZoyLRWOHx70vbos1Rxt6NbD1xxri93+8Pt45e0J8+LypUVbo8yUAAAAAudd9uPGZ/Sfi8XU743BzW6/Xjx9S1BNwf8n4EVnpDwAAAICB75emjY0xJUVx04qaaOzozFjfmY54cl1dbDvVFDdeODMK83p3+SIAAK+Nv74AAM6RrnQ6/qJ2f/zhT17pdWjXZRNGxF+8eaHQLgAAAAD6hElDS+ILrz8vPnnBzCgt6N0nyK6I+NvtB+M3f7g2/s/uI26ABAAAACCnDjS2xidX1MSNK2vOKLTrl2eMjb98y0KhXQAAAAC8ZovHlceXrzwvxpYUJl7zvbrD8dFnNsXxlnZPAAAghwR3AQCcAwebWuOjz26Kz72yK9q70onXFaRS8YnzZ8QTy6pjZHHyl28AAAAAkG15qVS8c/aE+Ou3XNATPN9bx1vb4+4XtsVVz2yM7aeastIjAAAAAPynjq6u+GbNvvjtH62NZw+c6PVgJpUWx+cunxu3XzwnygoLDBYAAACAs6KifGh8/Q0LYtbwIYnXrDvWEO/7yfqoPdnoKQAA5IjgLgCAHPvh7iPxu/+2Ll4+Ut+rdVPLSuLrb5gfv1MxMVKpVNb6AwAAAIDXYnxpcU/w/L2L58SIot4fWFxztCF+78fr4jPr6+J0e4eHAQAAAMBZt+5ofbz3x+vj8xt2R0tnV6834L+7YmL85VsWxpLxvQ+wBwAAAIAke7C+csX8uGjs8MTDOtjUFh98akP8+75jBgwAkAOCuwAAcqS+rSNuX1Ubd76wLU63d/Zq7dumjY0/e+P5UT2yLGv9AQAAAMDZ0h08/4vTxsZf/8IF8dapY3q9vjMd8ZdbD8Q7f7g2vrPjUHSm0x4OAAAAAK/Zqdb2ePCl7fGhpzfG9vrmXq+fU14aX3/Dgvj4+TNiSEG+JwIAAABA1gwrKojPXDY3fnnG2MRrukPqb1lZG1/YsDs6uuy5AgDIpt5fcQ0AQK+tPnQy7ntpexxubuvVutKCvLjxwlnxS9OSv1wDAAAAgL5iZHFh3LOkIn5h6ph4ZM2OONTL92MnWtvj4TU74u+2H4xrzp8el4wfkbVeAQAAABi4uoPh/3HHofjSpj09FzD2VmFeKt4/d0r8XuWkKMhzdzYAAAAAuVGUnxe3XTQ7KsqHxqfX1UVXwnV/VrMvXjnWEPctqYgxQ4qy3CUAwOAkuAsAIIuaOjrj86/sir/fcajXa6tHDO15MTZt2JCs9AYAAAAAuXLZxJHxV2MuiK9t3hN/s+1AdPbyMsdt9U1xzXObY9mEEXHNgukxc3hptloFAAAAYIBZc6Q+nli3M7aeajqj9QtHD4tbL5odM4bbxwUAAABA7qVSqfitORNjxrAhcduq2mho70y07uWj9fGeH6+P+5dUxKJx5VnvEwBgsEml0+lebosHACCJl4+civte3B77m1p7NbDu+xh/v3pyzw2NhW5npI9JN+6LjjV3v2pNwYV3R2ro5Jz1BAAAAPQv2081xWNrd8Saow1ntD4/FfFrM8fHB+dNjRHFhWe9PwAAAAAGhkNNrfG5V3bFj/YeO6P1wwvz4+oF0+OXZ4yLvFTqrPcHAAAA8N85s0MSuxua45MraqKuoTnxwLrfbHXvtXpf9WTvuQAAziLBXQAAZ1lLR2d8YePu+JttB3u9dlJpcdy9eE4sHDPcc6FP8hEAAAAAOCvvGNLp+MGeo/HZ9bvieGv7Gf07ygrz4/erJse7Zk+IkoJ8DwYAAACAHq2dXfGt2v3xZzX7oqWz64ym8rZpY+NjC6bHqBLB8QAAAEBuOLNDUqfbO+KO1Vvj+YMnezW0S8aVxz1LKmKkyxIBAM4KwV0AAGfR+mMNce+L22LP6ZZer3379LFx3cIZUVZY4JnQZ/kIAAAAAJztTWRf3rgn/m77wTizI5QRY0oK4/1zp8Q7ZoyLgrw8DwgAAABgEIfFP7P/RHx6fV3sb2o9o3/H9LKSuPHCWbFoXPlZ7w8AAADg1TizQ290ptPxhQ274y9q9/dq3dghRXH/koq4YMxwAwcAeI0EdwEAnKVbGr+8cXf85dYDke7l2uFFBXHLRbPijZNHexb0eT4CAAAAANlQe7IxHlu7sycY/0xNGVocH5o3Nd4ydUzkpVJntT8AAAAA+rYd9U3xqXV1sfrwqTNaX5yXij+onhLvqZwURfnC4QEAAIDcc2aHM/Evu47Ewy9vj9au5Kca81MRH5g7Nd5bNTkK8uyzAgA4U4K7AABeo7VH6+PBl7bHrtMtvV67dHx53H7xnJ6keugPfAQAAAAAsqUrnY4f7Tkaf7xhdxxqbjvjf8+c8tK46rxpcdmEEZES4AUAAAAwoDW0dcTXNu+Nb28/EJ29vXHx/7pkXHnceOGsmFJWcrbbAwAAAEjMmR1ey6WJt66qjT29PN+4YFRZ3L24wnsxAIAzJLgLAOAMnW7viC9s2B1/v+NQr9eW5OfF1fOnxTtnT3B4kH7FRwAAAAAg21o6OuOvth2IP9uyL5o7u87433P+6GHx4XlTY9G48rPaHwD/P3v3Ad5Web5//LbkvfeM7cTO3mGPMMKmZc8CpVCgdEIXLS1tKW0p5ddFSymU/ttSyoayNwHCCiQpkL0T7733tvW/3uMYTCIltiTbkvX9wLkknyO9enUk5bJ1nnM/AAAAAOAbIfAvFNXo7s0lauzuc2uMjMgwXb8wV8syE6nhAgAAAAAAE45zduDpuY63fVSgN8rrR3W/CLtN31k0VWdPTeU7MgAAgFEiuAsAAMAN71U26v/WFqims8etEwZvPiRf2dER7Hv4HQ4CAAAAAACA8VLX2aN7t5Tq+aIaOTwYZ0lyjK6Zk62DU2IpLgMAAAAAAJgENta36vfrCrWtqd2t+4fZbbpiVpYum5mhcLvd6/MDAAAAAABwB+fswFMOh0P/3V2lP20oVp9jdBVXx2Qk6KaD8pUYHsILAQAAMEIEdwEAAIxCY3ev7lhfpFdL60a938JsQfravBxdPCND9qAg9jv8EgcBAAAAAADAeNvR1K4/byjSh7UtHo2zODlGXyHACwAAAAAAwG/Vdvbor5uK9XLJ6Gu3hpw0JUnXLchVemSYV+cGAAAAAADgKc7ZgbdsaWjTTat3qLKje1T3SwgLtsK7js1M5MUAAAAYAYK7AAAARpg2b8K6TGhXU0/fqPfZvMRo3XzwdE2NjWB/w69xEACAp9ra2rR+/Xpt3rxZZWVlqqmpUVdXl/r7+xUeHq64uDhlZWVp+vTpWrJkiaZMmeIXO723t9f6fWEiBAcHy2azeTRGX1+fCgoKVFJSYr0uZmlsbFR7e7s6OzsVFBSkqKgoa4mNjdW0adOs12jGjBlKTOSgHAAAAMae+X37vcpG3bWpREWtnR6NtSgpRl+Zm61DUmKt33UBAAAAAADg23r6B/TYrkr9a1uZOvoG3Bpjemykvrd4qg5OifP6/AAAGG+TtQZrpMxzXrt2rXbt2qXy8nI1Nzdbz99ut1vPPzU11XrO8+bN06JFixQdHT3RU7bqsMzrtXXrVlVUVKiqqspa193dbdV+hYWFWbVZZu5paWnKzc1Vfn6+srOzrecFAAACA+fswJtaevr0yw936d3KxlHf9+ypqfr2wqmKCuF3UQAAgP0huAsAAOAAqju6dfvaAr1f1TTqfRViC9K1c7N16YxMBds4CRD+j4MAANxVXV2tZ555Ru+//74VcjVSpnjszDPP1KGHHurTO//6669XXZ37XZ098dWvflXHHXec2wVspiBs+/btVhGYO0yB2ymnnKKDDz7Y4wAxAAAA4ED6Bhx6uaRW/9haqqqOHo922MKkGF01O0tHpMUT4AUAAAAAAOCjTJi7abZY1t7l1v1jQ+y6dl6Ozp2WRv0WAMDvTfYarAP53//+p+eff94K7BqpkJAQHXXUUTrnnHOsQKzxZmqzli9fro8++sgKVhutiIgIzZ8/X4sXL9YxxxxjNXkcrbffflv33nuvxtpXvvIVLVu2bMwfBwCAyYxzduD195TDoYd3Vuqvm4rVP8o+5akRobpxSZ6WZiTwwgAAALgw+m/rAAAAAsSAw6GnCqqtL6bc6dQ4Oz5KNx8yXflxkWMyPwAA/OVAz7PPPqunn356VMViQ0yR1R133KEFCxboa1/7mhISOOjjCdOtcdWqVdZigru8VVxmFlPYdu2112rOnDleGRcAAABwxoTjnzk1VadmJ1vf3f17e5kau/vc2lkb6lv1nZXbNCs+Sl+alallWUmyBxG+DwAAAAAA4AtKWjt1x4Yit5otGqbl0Ll5afrq3GzFhYV4fX4AAIynQK/BamhosIKnNm7cOOr7mv1lgqtM2Nl5552ns846a1waupSWlurBBx90a87DdXZ2WoFlZlmyZIni4+O9NkcAAABMfuZ338tmZmpxcox+/r9dKm0beTh+TWePvv/+Np2SnaTvLpymxHC+YwMAANgbwV0AAABOFLV06raPd2t9feuo90+YLUhfmZutS2Zk0qURABDQuru7deedd2rt2rUej2UKmH7yk5/ohhtuUF5enlfmN1mEh4eP+LZ//etfVVhYOGYdPW+99VaddtppuuSSS9zq7ggAAACMVKjdpi/MyLBCvB7ZVaGHdlSqo2/0XcqN7U3t+snqnZoSVarLZ2Xqczkp1vgAAAAAAAAYf+29/bpvW5ke2VmpPofDrTHMiYjfXzRNM+OjvD4/AADGW6DXYJnQsT/84Q9qbm72aBwT4PXYY49p586duv766xUaGqqx8vLLL+vRRx91K2QNAAAAGAvzEmP0wIkL9ZeNxXqyoHpU932ttF6rqpv13YVTdXpO8rgE4QIAAPgLKs4BAACG6erv1982l+iy19e7Fdp1UHKsHjxpkS6flUVoFwAgoPX09Oj3v/+9VwrGhjQ1Nem2225TUVGR18b0dxEREVq0aJF8qbunKTz73e9+Z70HAAAAgLEWFWLXNXOy9fRpS3TZjAwrVN9dZe1d+s3HBTr3lY/14I4K6yRRAAAAAAAAjI8Bh0MvFdfqwtfW6oEdFW6FdqVGhOrWw2bob8fOI7QLADApBHoNVkFBgW6//XaPQ7uG+/jjj60gsLEI1erv79ff//53PfDAA4R2AQAAwOdEBNv1wyV5uuPo2UoKDxnVfVt6+vSLD3fp2+9tVUV715jNEQAAwN8ET/QEAAAAfMWq6ib9bm2Bytq7R33fqGC7vrUgV+dMS5WN1HgAAPTvf/9bmzdvdrknMjIytGzZMs2dO1cpKSmy2+1qbW3V7t279f7771vFZiYEam8dHR1WMZopHouNjQ34PX3UUUcpPDzca/shLS1N8+bNU35+vjIzM5WcnGyFg5nXwrw+NTU12rJli1avXq3q6ur9due8++679e1vf5uOOgAAABgX8WEhun7hVF0yI1MP7CjX0wXV6hkY/cmdRl1Xr9Vd8v5tZbogP10XTc9QQtjoitUAAAAAAAAwclsa2vSH9YXa1NDm1m4LtQXpspmZumJWlnUCIgAAk0Ug12CZsC4zRzNXZ4KCgnTQQQdZ9VN5eXnW8zBhXHV1ddY+W7FihaqqqlzWNv3nP//R1Vdf7bX5DgwM6C9/+YvWrFnj8jbm9Zk/f74WLFigadOmKTU11arNMtrb29XW1qbS0lIrVG3btm1+Ea4GAAAA/3NUeoIePmmR1eDwrYqGUd13dU2zLlm+Xl+bl60L8zMU7EGTRQAAgMkgyOHsG1gAAIAAUtfZoz9tKNLysnq37n9MRoJ+sHia0iLDvD43wNc4elo0UPnWfm9jyzheQaG+WcgBYHyYoq+77rrLZfHRhRdeqM9//vPWdVd27txpjVFbW+t0+5IlS/SDH/xAgcAUyG3atMnptltvvdUqPBupn/zkJyosLNwnrOuYY47RkUceaRXzjbTQ7IMPPtD9999vFYy5cuWVV+qUU04Z8fwAAAAAb6nt7LECvJ4pqFa3mwFeQ8LsNp09NdU6+TOd7wABAAAAAAC8pqGrV/dsLtHzRTVy9xuc4zIT9O0FU5UV7b2GRwAA+IJAr8G6/fbbtWHDBqfbTEjZt771Lc2YMcPl/fv7+/Xiiy/qiSeesK47Y5oSHn744V4LWXvttdecbjOv0UknnaSzzjpLCQkJIx6zoaFBH330kRVCZkK8TCPF+Pj4Uc/t7bff1r333rvP+qVLl+qaa66RtwQHB8tms3ltPAAAAhHn7GBc328Oh14srtUf1hepo8/578z7MyMuUt9fPE1LkjmHDAAABC6CuwAAQMDqdzj0dEG17t5UonY3vlxKCAvW9xdN00lTkqyuTQAAYLAb4w033KCmpqZ9docpyrnuuutGXOxkxvjVr36lyspKp9u/+93v6tBDD53Uu90UzX3nO99x2vkyNzdXv/nNb0Y13vDgLtO58XOf+5wWLlzo9u8ypjjsd7/7nYqLi51uj4yM1J/+9CdFR0e7NT4AAADgjdD+B3ZU6OmCKo8DvOxBQTo1O1lfnJmp/LhIXhwAAAAAAAA39Q0M6IndVfp/W8rcqtsypsZE6HuLpurwtNGHVwAA4OsCvQZr1apVuvPOO51uM40Jb775ZsXFxY1orNWrV+svf/mL1ahwb4mJifr973+v8HDPAkBXrlypv/71r063JSUlWfVf+fn5Hj3Gjh07rHqxsLAwrwV3HXvssfra177m0bwAAADg/yrau/SLD3dpXV2rW/c/LTtZ31qQq5SIUK/PDQAAwNcRYw8AAALS9qZ2XbNio363rtCt4q/Tc5L12MmLdXJ2MqFdAAAM89JLLzktGDPOP//8UXUoNN0Bv/e977ksNnrkkUecFlRNJqZborPQLmPZsmWjHs8EdJnALlOM9+Mf/1iLFi3y6HcZU7x24403Wl0sXRURLl++3O3xAQAAAE8lR4Tqu4um6unTD9Il0zMUZgvyqBHASyW1uvT19freyq1aW9vi8vd1AAAAAAAAOLe6ukmXvb5Bf9pQ7FbdVlSwXd9ZOFUPnbSQ0C4AwKQVyDVYZi6PPvqo023mOZjnMtLQLsPsK7PPXDUtfPnll+WJuro6/etf/3K6LTU1Vb/4xS88Du0yZs6c6VZoFwAAAHAgmVHhuufYebpxyTRFBttHvcNeKa3TRa+t1QPby9XrQ39bAAAAjAeCuwAAQEBp7+3XHeuLdOUbG7SlsX3U90+PDNWfjp6tWw6dobiwkDGZIwAA/qqrq0uvvfaa021TpkzRWWedNeoxs7KydM455zjdVlVVpTVr1miyMkVo77zzjtNtoaGhOvroo0c9pumQaAK7vFEMNry478tf/rLL7e+9957XHgsAAABwV1J4qL6zJ8DrillZ1gmenlhZ1aSvvbNZV7+1SSvK661QLwAAAAAAALhW3talH36wTde/t1VFrZ2j3lUmjv2sqan676lLdMmMDAXbKIMHAExOgV6DtWrVKtXU1Djddu6551rPZbTMPjP7zplXXnlF3d3dcteDDz6ozs59f7cJDw+3GiKaxogAAACAr7MFBem8vHQ9dsoiHZORMOr7d/QN6K5NJbps+XqtqnYeQgwAADAZccQSAAAEBIfDYZ1Ad/HydXp0V6UG3Cj8ujA/XQ+ftFhHpo/+yycAAALBBx98oLa2NqfbLrjgAtnt7p0Yf/rppysmJsbpttdff12T1fr1662ujs4cdthhioqKGvWYrgrQPLV48WLNmDHD6bbKykqrsyQAAADgKwFe35ifo+c+d5C+OT9HiR6G829uaNOPVu3Qxa+t09MF1erup2skAAAAAADAcJ19/frb5hJ9Yfk6vV3R6NbOmZ8YrftOWKCfHJyvxHCaLQIAJrdAr8Favny50/WxsbE67bTT3BrT7LPzzz/f6bbW1latXr3arXG3b9/uMvTssssuU0ZGhlvjAgAAABMlNSJMvztylm47fKZbdVXFbV369ntbrQD/srauMZkjAACALyG4CwAATHqlbZ363vvbrBPoajt7Rn3/mXGR+uey+bph8TRFhbh3sBsAgECwcuVKp+tN18BDDjnE7XFDQ0O1bNkyp9u2bt06aUOhVqxY4XKbq/0xkfb3GhcXF4/rXAAAAIADiQ4J1pdmZenp05foh4unKTMyzKOdVtrWpdvXFuiclz/Wv7eVqaWnjxcBAAAAAAAo0BstvlZap4teW6f7tpWrZ8Ax6jGSwkP080Om6/8dP19zEqLHZJ4AAPiaQK7Bqq2t1Y4dO5xuO/74463n4K5DDz3U2ofOvPvuu26N+dxzzzldn52d7ZP1XQAAAMBIBAUF6cQpSXr0lEU6c2qKWzvNBPibRoh/XF+o5u5edjwAAJi0gid6AgAAAGOlq69f/95ergd3VKjXjcKvyGCbrp2bowvz0xVsCxqTOQIAMFmYzoOmgMuZI488UjabZ9nhRx99tNNCJ1Ps/eGHH7rdTdFXNTc3a+3atU63mU6Mc+bMka/Jy8tzuc0XCvvcMTAwYL2v161bp8LCQlVVVam9vV19fX2KiIiwivnM816yZIkOOuigUXc07erq0scff6xNmzZZ4Wb19fXq7Oy0xomLi7PGnzdvnlV0mZOTo/HS3d1tzckUQhYVFVlFkS0tLdZ6cyA2PDxcCQkJ1ntxxowZWrx4sbKyssZ8XtXV1dacysvLrcX8bF4Psx/N3IKDgz+ZW1pamqZOnWrtv/z8fI//DfJUR0eH9T7asGGDSkpKrM+Eea1DQkKsbrbm9TbvpUWLFmnu3LkKC/MsOAQAAIxcuOmwnp+us6el6Y2yOv1ne4V2tXS4vQsbunt1z+ZS/Xtbuc6ZlqZLZmQozcNQMAAAAAAAAH+zo6ldf1hfqHV1rW7dPzgoyPpe5cuzp9BoEQAQUAK9BsvMwczFmaVLl3o0ttl3Zh+++OKL+2zbsmWL2traFB098qDQiooKqxbEmbPPPnvCa1XgHlOPZOq5tm3bprKyMquOz9QlmdA48/4wNVyzZ8+23o+m3mc0zHt7165d+uijj6xaNPMeMjVFvb29io2NtcYz9U6mVmz+/PlWXdF4KSgosD4Hu3fvtvZBQ0ODVZNl6uRMHZN57qmpqVZ9k6ltMvMbba3caJnaqu3bt1u1VqZWrLKyUk1NTda8zDZTx2ZeF7PfkpKSrNdm5syZ1twiIyM1kcxrberczHvJ7Fszd1Pn1t/fb9WKmdfb1LctWLDAqhdLTk6e0PkCAOBKXGiIfnrwdJ2anaLbP96tsvbuUe2sPodDj+2q0ovFtbpyVpYump6hMDu/JwMAgMmF4C4AADDpmAMdKyoa9OcNRarq6HFrjGVZifruwqmcVAcAwAht3rzZZdGUCTTylOlCaIoTnAVAbdy4ccKLxrzt7bfftoo0XHWP9EX7K0QyhTK+sl/vvffefdYfe+yx+trXvvbJz+a9/NZbb1mFiqYQyRlTrGcWUxhkbmvCoi655JIRFQmaEKznn39er7/+ulXYtTdTjGUKjMxjm2LM//73v1aRzpVXXmmFZY0V81xMceKaNWuczmvv515aWmrd9qGHHrKKss466yyrO6kpivKGnp4erV+/3irKNIVhJtjsQLc3i9m/JgjNzM0wAWimi6n5dyIqKsorc7v++uud/nv05z//WSkpn3aWMvvpmWee0Ztvvmm9pnszn3Oz3oSjmcK81157zSp0O/fcc3XyySdbYWQAAGB8mOD+U3NSdEp2slZWNen+7eXaUO/eiaVGZ/+AHtlVqcd3V+nU7GR9cWam8uMmtkAcAAAAAABgrDV19+rezaV6prBaA26OcXR6vL6zcKpyYiK8PDsAAHxfoNdgmYZozphajClTpng8vmlO5yy4y+xzs+8PP/zwEY/13nvvOX2tTCjPYYcd5vFc4R2XXnqp0/UPP/zwZ342tUaPP/64Fcbm7HU19W9mMTU+Jnjr0UcfteqRLrroogMGvpnxVq5cqaefftoKcHLGBGWZxQR6rVixQvHx8friF7+oo446SmPFhIa98cYb1lJTU+PydsOfu/mcmLo38z43tU3m34zRBN4diHkdVq1aZf17ZBpOmsab+2OCxczzMPvVNKp86aWXrHorU8N2xhlnaNq0aV6Zl6nfe+qpp/ZZf9555+mCCy7Y59+GZ5991gobc6axsdFahurbTK2dCRW8+OKLP1N3BgCALzk0NU4PnbxID+6o0H+2lat7wPnfLK609fbrrk0l+m9Blb42L8eqpbJ5qd4cAABgonHmFwAAmFSKWjqtbo1raprdun9GZJhuWDxNSzMSvD43AAAmMxOq44zp+jZjxgyvPIbp1PbOO+/ss94EG5kCjcnUpdAETDljutSZkCl/40+vjSmKufPOO61ufaO939133211yTMhYKabnzNr1661wsNMuNRomGKkH/3oR7r22mut7qfeZOZiitGcfb5GynQG/NOf/mR9Tr/yla9YHQE98fe//12rV6/2SuibKWp78skn9eqrr+pLX/qSxx1YR8q8F8zzGO1rbcK+HnjgASvsy7zmpiMkAAAYP6Yw2nw3aJZ1dS1WgNf7VU1uj9fvcOilklprOSo9XpfPzNSS5FivhZ0CAAAAAAD4gr4Bh54urNbfN5eopdd5g6IDyY4OtwK7qNsCAASyQK7BMo/tql5n3rx5XnmMmTNnWvvSNNVztu9HE9z1wQcfOF1vAoNo1OY/TKiWqSsyQUuuGm06Y25rmjaaWrAf/OAHysnJ2W9NmQm8Go2mpibdddddVv3Udddd59X3lNUkfcUKK3zM1Cm5o7W11QqyMvvgiiuusIKnPGHCup544gmXwWajYcK8zOfTjHncccfp8ssvV0TE2IcCm9fsr3/966hfa/N6vP/++/rf//6nr371q2Ma1gYAgCfC7XZdMydbn8tJ0Z83FOutioZRj1HV0aNb/rdLj+ys1HULcq1AMAAAAH/nP2dNAgAA7Ed7b7/+srFYl76+3q3QLntQkL40M1OPnLyI4i8AANwM7XHGFKR4q2gkPz/f6fquri6vFGz4ClME5+r5HHzwwYqL880DVPX19S63RUVFyR+YLnc/+9nPRh3aNZwp+PnjH//otNvfK6+8ot///vejDnIaYooG77nnHqtQx5vvNxMO5Ulo195FjD/96U9HXYC0N1M85Y3QruFMoZkphLvvvvsO2I3RUy+//LL+8Ic/uP1aD70ff/GLX6i6utqrcwMAACO3ODlWdxw9Rw+dtNAqOjPfIXrCBIB9/Z0tunrFJr1ZXm+FegEAAAAAAPi7j2qbdcUbG/T7dYVuhXZFBtv0rfk5evgk6rYAAAjkGqyKigprDs5MmzbNK49hQruys7OdbissLBzxOKaWw1U9x5IlS9yeH8aXqR/6y1/+YgVQjSa0a++aOVPfU1ZW5rT2x9M6KhPmZJpQmjAqb9VPmfq1f/zjH26Hdg1naqPMPjRNCk0A1VjUTLrLzOett96y6gHr6uo0lszrbx7Hk9fa1Aaa4K833njDq3MDAMDbMqPC9X9HztKdS+doaox74Zjbm9r1rXe36Lp3t2hTQ6vX5wgAADCeCO4CAAB+zRxQebW0The/tlYP7qhw62S3hUkxeuDEhfrmglxFBNvHZJ4AAEz2ApbS0lKn21x1knPH/sYqLi7WZGG62bmybNky+apdu3a53Jaeni5f19DQoNtvv9269NSGDRusjoTDLV++XP/5z388KlAa+rzde++9XilUMh0Zb7vtNqvbnze1t7frt7/9rTZu3ChfZF4LUyw21uN7+lobpmjsz3/+s9vFgQAAwDumx0Xp54dO11OnLdEl0zMUYffsEOvmxjb9eNUOXfzaOj1dUK3u/rENFQUAAAAAABgLVR3dumnVDn3jnS3a1dLh1hgmLP2JU5bo8llZCvXwOxcAAPxdoNdg7e+xc3NzvfY4rsYqKSkZcSM4V+E8QUFBmjNnjkfzw/gx4VWmSaOnTHPC3/3ud58JnqupqdGvfvUrNTY2ejz+hx9+qOeee87jcUyNmAkZW7t2rcaiyaHZn77IhALeeuutHjVg3B8T4mdq8PbX+HSkTL2ZaUq5e/dur8wNAICxdHhavB48aaGuW5BrBfO7Y01Ns9UA8Yb3t2lHU7vX5wgAADAevNNuAQAAYALsbu6wOjV+XOfeQZTY0GBdNz9HZ0xNlS0oyOvzAwAgUJiCA9Pty5mMjAyvPc7+wp+qqqo0GZjAozVr1jjdlpycrAULFshXme5+zthsNq91vRwrpujurrvu2qd4JisrS4ceeqi13xMTExUTE2MVWJkwJRPOZTryuQr6eumll7R06VKr2NF0BDShXXsX6i1evNhapk+frri4OIWHh6u1tdUqwjT7c+XKlU4LAs3nzRTo3HTTTW4/Z1OAZbod7q/gMC8vT4sWLbIKCpOSkhQdHW3Nu7m52SpoMmOY92tHR4fTOf7pT3+yCtAyMzPlDebfkxkzZlj71Czx8fGKiIhQZGSk1VXSzMO8hqb77KZNm6zgMFfhWa+++qq1348++mh5k3mt77///s+sM/ts3rx51r6cNWuW9VqbfTk0X/Neeuedd1x2diwqKtILL7ygs88+26tzBQAAo5ceGabvLJqqq+ZM0VMFVXp0V5Uau53/LTQSpW1dun1tge7dUqovTE/XeXnp1neWAAAAAAAAvqyrv99qsPif7RVuB5LPjo/SDYunaUFSjNfnBwCAvwr0Gqz9NbHzZtNAV2P19PRYdUCmRsvdBodmbFPH4uy5ffTRR9q2bZvKy8utAKHu7m6FhIQoKirKeszs7GyrrsTUEpm6kvFgAqdMjdKOHTus52SCndra2qz1Zm6mLichIUFpaWmaOnWqVUOUn59v1YNNhuaapvZrOPN8Ta3YkiVLrLqx2NhY67maWimzj95//32XoW21tbV66qmndOmll1qv7R133LFPUJSpoRpei2bGN7c1NUPr16/fby3as88+q6OOOsrtz4KpU/r1r39tvf9cMfM5+OCDNXfuXOv9aGrlTD2beU+Yf59MPZapFSsrK3O5T1NTU71W42Q+S7Nnz7bqxMz7z4xtXiPzmbHb7dZzMrV2JvRv586dVhNLU3/pjAlS++tf/6of//jH8ibzb/Yf//jHfRpnms+M2ZfmtTafb1MvZur0zHvCzNUExrlqimlu9/e//916vYKDOXYMAPBtITabvjgzU6dmJ+uuTcV6pcR5LfSBvFvZaC0nTUnSV+Zka2pshNfnCgAAMFb46x0AAPidlp4+/WNrqf67u0r9zs/DP6Azp6bom/NzlRAW4u3pAQAQcExRgyspKSleexxTvBAWFmYVq4xmDv7EBDWZIjBnjjvuOJ8tejJFZabrpDMzZ850WpDmS0wB2vD3lSlC+sIXvqBjjz12n31unospnDLP64wzztC///1vK3TJWQHNk08+qa9//eu6++671d/f/8k2U9z0pS99yWkHUzO+Kdw55JBDdPrpp1uFPc4CnUwhlFnmz58/6udrQrdMUJmr0C4TjnXxxRdb83TGFAcOhZqZ/fTEE0/ojTfe2Od2pojPhIOZjoWmWModZh+ZcC2zPw5UhGqKxcy+M/M2r40piHv00Uf1wQcfOL29CVMzhXbefH+a98Pw/WqKx774xS9aIWh7M4VkJhDNvJfOOussaz+++OKLLovvTj31VKsYDgAATDwTrnXl7Cm6ZEamXiqu1UM7K6wQLneZ8K97Npfq39vKdc60NH1hRoYVEgYAAAAAAOBLTMOUFRUNunNDsSo79j1mOxIJYcH6xjwaLQIA4Eyg12CZOg9nzFxNLY+37G9fmjmMJLjLVZ2UCTsazoRhmXoQVwE95jUwiwlrMsFQpv7G1NiYmhxTS2LCisa6ZspVs8ahuZlAosLCQitoyDCvhampOvnkk736vhxvezdhPOGEE3TRRRc5fa8N1UotW7bM2g/33HOP05A900jQ1Cw988wzVpjUSGvRTDiaqdf6/Oc/r3/9619WDeHezOOZYLBvfOMbbv0eb2q4XIV2mSCsCy64wHp+oaGhTmuczGtt6qDOP/98vfvuu3rooYes0Ky9Pf7441aDQ9NQ0R1mfwzVipmguP0FVw3VXpnPiamtNPV4Jvzsscces+rW9mY+hyZ8zQSgecvy5cutRqDDX+sLL7zQ2pfOaj3N9ilTpljbTbNHU8NngtH2Zhp/mrma9wwAAP4gJSJUvzh0hi7Oz9BfNhbr47rPBpiO1Otl9XqzrF6n56bomjlTlBlF3TQAAPB9BHcBAAC/0Tfg0DOF1fr7llI19/S53a3xB0umaX4i3RoBdzgGeqXOAxRmRKQqyEYoHhBITDc1V+Lj4736WKZwzFmB2P7m4E/27uI3JCgoSMcff7x8lSl2ccUfikeGFyKaIiPTWW8k3QlNiNK11177SffJvZlOmffee+9n3p/mdbzmmmtGFMJmiopuuukm/fSnP7U6BO7NBIaNNrjLhEr97W9/c1qcZJgCMFMoNtKgLVNMdPXVV1tzve+++/YJAzNFaK+88oo17miYboMnnniiFi1aJHeZ1/K6666zwrnMc957bqZ47LXXXtM555wjbxleiGUK+q666qoRvdam6O2yyy6zis1MSJezcU3hny//OwAAQCAKs9t0bl6azpqWqncqGvTA9gptbty3sHqkOvsH9MiuSj2+u0qnZCdZHSmnx0V5dc4AAAAAAADu2N3coT+uL9SHte6ddGcPCtJF+em6es4UxYRSvg4AgDOBXoPl6rG9/dz3N56z5nqumuY5YxrODYUsPfjgg3r99det0KTRMM0BTY3I6tWrrTqRyy+/3KcavbW0tOiFF16w6oFMbY8JuzIBSv5mqF7M1PWY+h5T5zMSRxxxhFXfY5ox7s287qbp35o1az7znvjRj370yXtjf8x+NE0iTV3Xxx9/vM92U59mto12f5twqfXr1zvdZmq+vvOd7yg1NXVEY5k6RlMPaILGbr/99n0C98z7/Z///Kduu+0267YjZQK4TO2UeR3cfT+Z+qtTTjlFBx10kH7961+rurp6n9uYRpxHHnnkqOY20loxU2944403jui1NhYuXGjVBv7iF79wGqRoakn9ofYSgPdxzg782dzEaN197Fy9W9mov24qUVGr83r1/THV3i8W1+rVkjp9PjdFV8zKUla07/w+DAAAsDeOfAIAAL+wprpJf9pQpN0to//CxogNHezWaE6iM4VgANzUWaO+tbfs9ybBS26RorLYxUAAcdY5bYg3ux0OjeesaMxZ1zF/U1BQoKKiIpdFGqY4xRe9/fbb2r59u9NtiYmJWrp0qfyF6dhnQrJG0w3SFG9deeWVVge8vQtoTFCUKaIbYrphmqCv0TAFPeeee67VpdBZMZYp/hlNcZ55vUw3T2dMp04T2uUOU4hnwsUeeeSRfbY9//zz1vbRzPN73/uevMW8B03RmgkW29ubb76ps88+22vFWMMf0wS0jZbpXmkK70zXRGdBbQR3AQDgm8z3jcuyknR8ZqLW1bXqgR3lWlnV5PZ4/Q6HXi6ps5Yj0+J1+axMHZQc6/XfWQAAAAAAAA6kpadP/29LqZ4sqFL/6DInPnFYapy+u2iq8mIj2eEAAOxHoNdguXr+Y/HcXRnJ829vb/9MWM9wpubI1M/83//9n3bu3OnRPE0A0ooVK6y6rB/+8IcjDlYaL319fXr11Vetxobf/va3lZ+fL3906aWXjji0a8ghhxxi1YE5a/RoQteG16L95Cc/UXJy8qhq0b785S9r06ZN6unp+cw2U5s2FOg2UuY97aopZ1ZWljW/qKjRNxLKyMiwQqfMsncDSdPo0czThJyNlKlb8xazv00NoGneufdnurKyUps3bx51s8yR/Lvys5/9TAkJCaO6nwlOO++885zW3G3bts0KHxtpEBiASYRzduDnrKDPzEQdlZ6g54qq9fctZWrs7h31OH0Oh54tqtELxTU6NTtFV87OUm6M/wXGAgCAyc820RMAAADYn9K2Tt3w/jZd995Wt0K7zKls501L039PWaxz89II7QIAYAzsr2DJ2930XI23v8I1f2EKrVxZtmyZfJHpmPfAAw+43H7hhRdaHQb9xRVXXDGq0K4hpuDmQIVGplunO0FOhgm9Mh0B92aKsQoLC0dVMPfUU0853bZ48WJdfPHF8sQZZ5yhuXPnOu20+f7772sinXTSSVanR2edUj0tlNybKZYynTjdYbfbdc455zjdtnv3bus1BAAAvl14tiQlVn88eo4ePmmRPpeT4vH3kR9UN+kb72zRVSs26o2yeivUCwAAAAAAYKyZ7yCeLqjWha+u1eO73QvtyowM02+PmKU7l84htAsAgBEI9BosV89/vJ77SJ9/U1PTfse+/fbbvVqLUlFRoV/84hdWgI8vMrU3v/rVr6wAL38zb948nX766W7d97TTTjvgbUwA12hCu4aYBp+HH364022uGmy68sILL+wTrGWEhYXphhtucCu0a3iN1OWXX+502yuvvKKJZPbhRRdd5HTbBx984PXH+9rXvjbq0K7h7yUT8ubMjh07PJwZAAATJ9gWpPPy0vXkqUt01ewshdvdi7Qw30u+VFKri19bp5+s3qHdzR1enysAAIAn/OfMSQAAEFDaevt037ZyPbqz0kpId8f8xGj9YPE0zU6I9vr8AADAp1x1EDTCw8O9uqtcjbe/OfgDE8DkqiAkLi5OBx10kHyNCRD685//bHWJdGbWrFk69thj5S8yMzO1dOlSt+9/2GGH6e2333a53RR5xcTEuP2+X7RokdMujUVFRZozZ86IxjEFcvX19U7DokwRlQma8IS5v+kAuGXLln22vfPOO6PuTulNZm4mvMtZYaTpUDlz5kyvPZYJ3vLk376DDz7Yuv/e/6719vaqrKzM6rQIAAB8X35cpH5+6HR9bV62Ht1VqWcKq9XRN+D2eFsa23XT6h3Kjg7XlbOydFpOilXgBgAAAAAA4G3r61r0h/VF2t7U7tb9zUl4V87O0qUzMhXm5gl5AAAEokCvwXL12OP13Pc3h5GGez3zzDMqLy93WptzyCGHWDUhubm5Vj2YqWVpbm5WaWmpVdNj6oJMbYgzjY2N+u1vf6tbb73VK0Fm5rGnTZtm1cvk5ORoypQpVl2TCQ8yczUhamYpKSnR1q1btWHDBmuurvT09Fh1ZD/84Q81f/58+QsT7ORuvZSpjYuNjbUaGjqTlZWlo48+2qNatHfffddprdhImffTm2++6XTb5z//eSt4y1OmPtC872tqavYJnKqqqlJ6errHj+HJ3B566CGrNnO4zZs3e/VxTO2eaZrprpCQEOv1fuutt/bZZpp6HnPMMR7OEACAiRUVYtdX5+VYIV7/2lqmZ4tq3GpcaO7xelm9tRyfmagvz87inFEAAOATCO4CAAA+xXzx8kJRje7ZXKrGbucHHw8kISxY35qfq8/lpsjmYfgAAAAYWYCTK8HB3v3qwdV4+5uDP1i9erXLACxTQOLt/egN9913nwoKClwWuJkucp4GQY2nU045xaP55uXl7be4ZtmyZfJEfn6+0+Cu4uLiEY9hwrOcOeqoo5SRkSFvmDt3rrKzs62iwuF27dplFfRFR09cqK4JP3PGFDh5iynI86TozggNDbXCubZt2+b09Sa4CwAA/5IWGaZvL5yqL8+eoqcKqvXYrko1uPm9p1Ha1qVffbRb/9pWZgV4me9Ag22cAAsAAAAAADxX09mtuzaW6NXSOrfHOHlKkq5bkGt9JwIAAEYn0GuwXD32eD33/c1hOFfhWoaz0C4TZPXlL3/ZaW2OCcsyoVlHHnmkFXx0//33a+3atU7Hrqys1AMPPKBrr71W7po+fbpVw7RkyRLFx8e7vJ0J8EpNTbXqoY4//njrOZu6o2effVZ1dXUu992dd96p3/zmN0pKSpKvM89txowZbt/fZrNZ4Wfr1693WYvmaa2YM6bpn9nXI/lcfPzxx1a91t7CwsKs4C5vMPvh5JNPtgKy9rZu3TqddtppmiimBsvUsu39mTKfNW/WsXnjOZoQPWfBXaOpDQQAwNelRITqxoPy9MWZmfrntjK9XFwrd1sgvlXRYC1Hp8dbNVkLktxrrg0AAOANVHEDAACfsba2RVe+sUG3fVzgVmiX+cXmovx0PXHKEp0xNZXQLgAAxkl/f/9+CzO8yXT0m4zBXStWrHC5zRQ/+ZoXXnhhv3O+6qqrvNKRbzx50vXOMJ0wXRXzmKI3Twt9TBdGZ5qamkZ0f9PZctOmTU63HXHEEfKmefPm7bNuYGBA27dv10QyoVqm8MxZQZu3mGJLbxSMmm6mzuyveykAAPBtsaHBunJ2lp45/SD9+KA8ZUd71h2+vL1bv/64QBe8uk5PF1Srd8DdUjYAAAAAABDouvsH9O9t5bro1XVuh3bNiIvU346dp1sPn0loFwAAbgr0GixXjz1ez31/cxhpcJezho0/+tGPRtRQzwRl3XDDDTrppJNc3sYE+5jmeaNlAqZMoNYvf/lLK7hrf6FdrpoWnnjiibr99tt16KGHurydCUP617/+pUCoFdtfPZc3xjevkbN6M/PvhLMwLlfBXa7mFhERobGsFTO2bt2qieYqRM5b9WLm35OFCxd6PA61YgCAQJIVHa6bD5muR05erJOmeBb4urKqSde8tUnXvbvFOi8VAABgIhDcBQAAJlxpW6d+vGq7vvbOZu1o7nBrjCXJMfrPSQv1/cXTFBPq3c5KAADAfUFBQey+A6ioqHAZaDRnzpwRFW6Np1WrVumRRx5xud100Fu6dOm4zslTptDJFL95yoR3OeNJd8YDjd3Z2Tmi++/cudNp4aAJslqwYIG8yXQAdMYXOgA6K2hraGjw2vizZ8/2yjgJCQlO13d0uPf3EgAA8B1hdpvOmZamx05ZrP87YqbmJXoW8FrZ0a3b1xbo/FfW6sndVerpJ8ALAAAAAACMjMPh0LsVDbp0+Trds7lEnW58r2DCym9cMk33n7hQS1Ji2fUAAIyRQK7B8rXnbn6HGmmY0Ve/+tVRBY+Z52oaJh588MEub/P000/LnVCg3NxceSoyMlLf/e539xsutnbtWm3cuFG+bizruUwtWkpKyoTXi23ZssXp+v29v9xh3l/h4fs2LSoqKtJEi4mJcbreW/ViJhTPWSPJ0aJWDAAQiKbGRujXh8/UAycu1DEZzuumR2pNTbN1XupX3tpkfd85MMLf2QEAALyBVAsAADBhmrt79c9tZXpyd7X63PxCJCMyTNctyNUJWYk+d2AWAIBAsb8uhKbDW3Bw8Jh3lvTmY4y3FStWuNxmOhz6kg0bNujuu+92WYBmuvF96Utfkr9JT0/3yjiuinC8Mb6z4qbRBDkVFBQ4XZ+Zmen1z4+rrpwmpM5Tptvh7t27VVpaai2miKqrq8sqSDOXAwOjP6HEBJqZ/WiKCz3lraA9V10tR1p4BwAAfJ89KEjHZyXpuMxEratr1QM7yq0ukO6q7uzRb9cV6r7t5frSzEydPS3NCgkDAAAAAABwpqilU3dsKNSq6ma3dpD51uG8vDRdOy9bcaEh7GQAALwg0GuwzGM7a0rnaq7u2t94I3n+I7mNqSEyoV3u1rZfffXV2rZtm9rb2/fZtm7dOtXW1nolGMpdV155paqrq10GdL3wwgtebyTobd6o8XFVzzXWtWgjqRdraWlRfX29023eCHEbzoTTmZAxU7s1XF1dnfr6+jz6d6Wtrc36LAzVilVWVn5SJ2Yunf2bMRJm/3iDt15rasUAAIFsZnyUfn/UbG1uaNXft5S6/X2lsaG+VTd8sF3TYiJ0+axMnZKdrJBRBOkCAAC4w3/PagUAAH6ru39AT+yu0n3bytTW697B1Ai7TVfOztIlMzI5AQ0AgAm2v8IKTwsvnI032jn4MvN83n33XafbTIjQYYcdJl9hCmDuuOMOl6/BnDlz9J3vfGe/RYS+KioqakyLpbwxvquxe3p6RnR/V6FZJrhrvDoVNjY2ujWeCed6++239f7776u8vFxjwexHbwR3jfV7qbu72yvjAwAA32FO2FiSEmstu5s79NDOCr1SUqd+Nxsd1Hb26A/ri3T/9nJ9cWaWzp2WqvBg//sdHQAAAAAAjI223j79c2uZHttV5fb3DwelxOp7i6ZqRpx3josAAIBBgV6D5Sq4y9Vc3bW/8bwV3HXssccqOTlZnjTNO+GEE/T888/vs800XPzwww91+umna6KYoCYTLvb973/faRDapk2b1NTU5LL5ny/wRo1PaGjomI3tab2Yqxor89p5K2xquOjoaCvMbe/3qqkXG23InHlPrVq1yqoVM41GvR3eN5qauwPx1mvtKgSOWjEAQCCZlxijPy+dawV43betXO9Wuld3bhS2duqXH+7W3zaX6tIZGVYDxEjqpwAAwBjxz7NaAQCAXxpwOLS8rF73bCpRZYf7J5x/LidF35ifo5QI5we7AADA+HJVNDBUOLC/7aO1d1e2kczBl3300Ucuu7ctXbrUZXHPeCsoKNDvfvc7l4Ug06dP1w033OAz8x0tbwQ2TdT4psBpJFx1UFy5cqW1jAdnXUD3xxRjvvjii3r22WfHvAjJ3e6L4/1eAgAAk1t+XKRuPmS6vjo3W4/uqtQzhdXq6Btwa6y6rl79aUOR/rO93GqAcO60NIXa6SAJAAAAAEAg1229WFyruzeVqKHbveMiaRGh+vbCqTohK9EKIwcAAN4V6DVY5rE7Ozv3We/tmhFXz31oDgcyktuceOKJ8tRJJ53kNLhrKBhrIoO7jNTUVC1btkyvv/6603qmtWvXWtt91VjW+Ix1/dBI6sVc1YoNDAzoiiuu0Hgx9WKjCe7avHmz7r//fpWVlflFrZi3grsAAMBnA7x+f9Rs7Whq133byrSivEHutR+Qajp79KcNxfrX1jKdn5+ui/IzlBgewu4GAABeRXAXAAAYF2trW3TnxiJtaRzdyfrDLUiM1ncXTbW+gAEAAL7DdEtzpaOjQ3FxcV57LGfFWUZMjH/+frBixQqX23ylcKmkpES33367y32fm5urH/7wh4qIiJC/GusTC3zhxIXW1taJnsKoOhXW1dVZYXGlpaXyJ77wWgMAAP+XFhlmnQR71ewperKg2grxanTzhFpzIu4f1xfpoR0VumbOFH0uN1XBNn5nAQAAAAAgkGxqaNUf1xVpc2ObW/cPswXpi7Oy9KWZmQoPtnt9fgAAYFCg12CZ59/Y2Djiubprf+ON5Pkf6DaxsbHKycmRp0zYUVpamqqrq502YfQFRx55pNPgLmPnzp0+U/823jU+vlA/5Au1YqOpFzOBYg899JBefvlljYeRNssEAAATZ2Z8lH5zxCwVtHTo39vKtby0Tu61P5Raevt137ZyPbyjQmdMTdVlMzKVFe2fjeMBAIDvIbgLAACMqeLWTt21sVjvVO57IHWkUiNC9a35OTolO9knDmQBAICRFyO1tLQoIyPDa7vMjDfawjVfZYKJNm7c6HRbXl7eBNt+AACicElEQVSeFYg10crLy3Xbbbeprc15Ef+UKVP04x//2C/3f6AZTWjWWOnv7x/R7UwR5q9+9SvV1taO+ZwAAAB8WUxosK6cnaUvTE/X04XVemBHheq73Avwqu7s0a8/LtB/dlToK3OzdfKUJNn4rhUAAAAAgEmtvqtHf91UoheL3T/msiwrUdcvyFVmFCeyAQAw1gK9BsvV829ubvbq47h67iN9/gcK7po+fbpb83I1lrPgLrNPTADZRDdanDFjhjUHZ2FoZWVlEzIn+E6t2Gjqxe677z698cYbYz4fAADgf/JiI/XLw2ZYDQvv316ul0vq1O9mCGf3gMNqovh0QbVOnJKkL83KsgLCAAAAPEFwFwAAGBMNXb36x9ZSPVNYrX43G5KE2W26fGamvjgzUxF0awQAwGclJSW53ObtwilX4+1vDr7qrbfectm5zRe6DVZWVurXv/61y2I1Uwx40003WV0i4ftGWgQ10Z0KzW3uvvvu/YZ22e125efnW8V/WVlZVodR01U2MjJS4eHhstls1uLMDTfcYIXmAQAA+JPwYLsumZGpc/PS9GxhjR7YXq5aNwO8Stu6dPOanfrP9nJ9dW62jslIoFkCAAAAAACTTO/AgB7fVaV/bC1TR597x4jyYiP0vUXTdGhqnNfnBwAAnAv0GqzExMRxee5NTU0ut43k+ZugKlOj0tHR4XR7amqqvCUtLc3ltoaGBqtuZiIFBwdbNWQFBQX7bKM+Z2L5Qq3YSOvF3nnnnQOGdpn6sDlz5ljNUM3nIj4+3grRM7Vi5n1o6smceeaZZ6wFAAD4v5yYCP3skOm6es4U/Wd7hZ4vqlGfmwFeA5KWl9Vby+GpcVaA18EpsdRQAQAAtxDcBQAAvKq9t1+P7KzQQzsr3S78Mk7JTtI35+cqPTLMq/MDAADeZ4oiXKmpqfHa45girO7u7lHPwRcNDAxYBSfOhIWF6aijjtJEMp0aTWiXq0I1U2D2k5/8xCqAgX8ICQmRP1i9erU2b97s8jmcffbZOumkk9wOjDOfPQAAAH8Vbrfr4ukZOmdamp4rqrHCt2o63euWvau5Qz/4YLvmJUbr6/NyOAkXAAAAAIBJ4oOqRt2xvkjFbV1u3T86xK5r52br/Lw0BbtolAIAAMZGoNdguQq86urqshoPequ54P6ayY30+Zu5FhUVOd0WFRUlbzEBYa50dnbKF7h6XXxlfoHKX2rFzOf74Ycfdrl98eLFOv/8860mjwAAAEZmVLh+dFCevjw7Sw/uqLBqqLr63a8PX13TbC1zEqJ0+cwsHZeZqGBbEDsbAACMGMFdAADAK3r6B/RUQbXu21ampp4+t8dZkhyj6xdM1dzEaF4ZAAD8RHJyslXo0dvbu8+2yspKr4ZJuZKeni5/snHjRpddBY888kirM+NEMcVpJrTLdGV09Xqb0C5XXS7hm1y9py655BKdeuqp4zKHoKADH8R86aWXnK43HRJvvPFG5eXleTSH9vZ2j+4PAADgC8LsNl2Yn66zp6bqheJa3b+9TFUd7gV4bW5o07fe3WJ1jTQBXguSYrw+XwAAAAAAMPZK2zr15w3Ferey0a37m6M4Z09L1dfm5SghzD9O8gcAYLIJ9Bqs/T12VVWV14K7zFjOmH2flJQ0ojEyMzNdBneFh4fLW/ZXQ9bX5369vje5Chfr6XHv2BXG9r2TnZ2tX/3qV+O2m4OD93/q6sqVK61gPmcuuOACnXfeeR49PrViAABMXmmRYfr+4mm6Zs4UPbG7So/vrlKzB+e0bm1s102rdygzMkyXzMjQmVNTFRFs9+qcAQDA5ERwFwAA8EjfgEMvl9TqH1tL3T45zMiNDtc3F+Tq2IyEEZ3QDwAAfIfNZrMKOgoKCvbZVlJS4rXHKS4udrlt6tSp8icrVqxwue3444/XRDFhXSa0y1WoWEJCghXaNZHdNeEeV0Fr9fX1Cg0N9Yndat53u3btcrrtmmuu8Ti0yxQsuuoYCwAA4I9C7Tadl5emM6em6KXiWt23rVyVHe79vvNRbYuueWuTlqYn6OvzszU9znvd4AEAAAAAwNjp6OvXv7eV6+GdFeodcLg1xsKkGH1/0VTNTqDRIgAAEynQa7D299hmzjNnzhzT55+Tk2O9BiOd6/vvv+90W2dnp0fzG+lYJmjMF3R0dDhd7yv1SIHKVQidaerpS6/NqlWrnK4/+OCDPQ7tMtra2jweAwAA+La4sBBdMzdbl83M1PNFNXp4Z6Xb9VNGRUe3/rC+SP9vS6nOz0+3mismhfvO708AAMD3ENwFAADc4nA49HZFg+7ZXKqiVvcPMCaEBeuaOdk6Z1qqgkd4sBMAAPieadOmuSwaM2E5B+qcNhK7d+922aUwIyND/sJ0iPvoo4+cbpsyZYrXisxGq6mpyQrtqqmpcbo9Li7OCu1KS0sb97nBc65eN1ddRCfC9u3bna43n+9DDz3U4/F96bkCAAB4U4jNprOnpenzuSl6uaRO/9paZhWRueO9qkatrGrU53JT9NW52VZ3SgAAAAAA4HsGHIPNFu/eVKK6rl63xkgJD9F1C3J1SnYyjRYBAPARgVyDlZmZqbCwMKdN2QoLC73yGGYflpaWutz3I5Wfnz/qICt3tLe3u9wWGRkpX6mFcyYiImLc54JPpaamOt0dXV1dVp1gfHz8hO+ugYEB7dy50+m2s846yyuPUV1d7ZVxAACA74sItuui6RlWE8TXy+r1wPYK7Wpx/3fzlt5+q4nigzsqdHpOii6dkaFpsb7xOzgAAPAtpGMAAIBR+7CmWVev2KQbV+1wO7QrzBakK2dl6b+nLtEF+emEdgEA4Ofmzp3rdH1vb6/L4orR2rJli9P1s2fPHnG3Q1/wzjvvqL+/3+m2ZcuWaaIKqExoV2VlpdPtMTExVmiXKZCDf8rLy3O6fseOHdbn1Be4ev8ddNBBXhnfPFcAAIDJzDRGOHNqqh4/dbFuXDLNOvnWHQ5JLxbX6oJX1+qujcVq6enz+lwBAAAAAID7Nta36uoVG/XLD3e7FdoVYgvSFbOy9PipS3RqTgqhXQAA+JBArsEyj23m4MzmzZu91lTOVZ2Mq33vzPTp062QsbEOCtrfWElJSZpoJgjNVb1PcnLyuM8HnzIhfK7C07z1efJUXV2denp69lkfHR2tGTNmeDy+CQEsLi72eBwAAOB/9VOn5aTowZMW6o6jZ2tJcoxH4/UOOPRcUY2+sHy9vr9ymz6ubZbDYaqrAAAABvnPWa0AAGDCbW1s03XvbtE3392izY1tbo0RJOnzuSl64tQl+vr8HEWHeN75CQAATLx58+a5LOheu3atx+ObToemUMOZBQsWyJ+89dZbTteHhIRo6dKl4z6ftrY23XbbbSovL3e63RTC3HTTTZoyZcq4zw3eM2vWLKefUdNFcdOmTT7dgTMxMdEr4/tK0RkAAMBYC7HZdF5euv572hJdvyBXcaHufQfbM+DQAzsqdP4rH+uhHRXq7h/w+lwBAAAAAMDIVXd06+drduqatzZpS2O7W7vumIwEPXLyIn1jfo4ig+3sfgAAfEyg12DNnz/f6fra2lqVlZV5PP66deucrjf73NVju6rzmjNnjtNtu3fvlrfs2rXLZShWaGioJpoJk+vsdN4EPCsra9zng88G4c2cOdPpLvnwww99ulbMW6F027Ztc9lgFQAATH7md/yj0hP0t+Pm6x/Hz9dxmQnWea2eeK+qUV9/Z4u+vGKjlpfWqW+AAC8AAEBwFwAAGIHdzR360artuvLNjVpT0+z2Pjs0JU73n7hQNx8yXWmRzrsMAQAA/xQbG2sFAznzwQcfaGDAsxO8V65c6fKAyiGHHCJ/YYpBKioqnG4zzyMmxrOOLqPV0dGh3/zmNyopKXG6PTIyUj/60Y+Um5s7rvOC98XHx1vdPkcTJjfeXBVKeaPQsLm5Wf/73/88HgcAAMCfhNvtumxmpp46bYm+MneKotw8Gbelt193bizWxa+t08sltRqgayQAAAAAAOOqq79f/9xapoteW6dXSp0HbRxIbnS4/nT0bP3+qNnKjo7w+hwBAIB3BHoN1qGHHjrquY+U2XdmHzozd+5cq7nhaBx22GEuw4iKiorkqerqatXU1Djd5qoGaLy52p+Gq9AojB9Xn2kTYNfU1OSztWImGM8bXn/9da+MAwAA/N+CpBj99sjZevTkxTpzaoqCXYQlj9TWxnb9dM1OXfDqWj22q1IdfYSFAgAQyGwTPQEAAOC7Clo69JPVO3TZ6+u1orzB7XFmxEXqjqNn6y/HzNGs+CivzhEAAPiOY445xun6+vp6j7q09fT0aMWKFU63zZ49WykpKfIX+wtIWrZs2bjOxXQ7vP3221VYWOh0e0REhBXalZeXN67zwtg59thjna43gVY7duyY8F0fFeX8b4XGxkaPx3711VfV19fn8TgAAAD+KDokWNfMydbTpy3R5TMzFWZ37xBxZUe3bvnfLl3xxgatqp74Yn4AAAAAACY7h8Oh5aV1Vpj237eUqqt/9EEdkcF2Xb8gVw+dvEhHpieMyTwBAIB3BXINVmpqqsvAJzN38xzcZepjGhqc18MvXbp01OMdccQRCgtz3sj6jTfe0FiGDi1evFgTrba21mUtnAmC84U5BrrDDz/c6Xu0u7tbTz75pHy1VswboWKVlZX6+OOPPR4HAABMLlNjI/TTg6fr6dOX6LIZGdZ3p54wtVR/XF+ks176SHdvKlFdp/t/rwAAAP9FcBcAAHAZ2HXp8vV6vaxeDjf30ZSoMP3ysBn6z4kLdVR6gnUQDgAATF5HHnmky2KK//73v253fHz55ZfV2trqdNtJJ50kf9HR0aHVq1e7LDqbN2/euM3FFN/87ne/065du5xuNwU7P/jBD3ymOyO8wxQZuuoOet9996mrq2tCd3VMTIzT9Vu3bvVo3OLiYr3wwgsejQEAADAZxIWF6FsLcvXUqUt0QV6a290jdzR36NvvbdV1727R9qZ2r88TAAAAAABI2xrb9NW3N+una3aqqsO9E74+n5ui/566WJfNzFSIjZJ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"text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 594, + "height": 559, "width": 2423 } }, @@ -288,11 +363,13 @@ "\n", "az.plot_dist(idata_moderate, var_names=[\"tau\", \"beta\"])\n", "axes = plt.gcf().axes\n", + "for a in axes:\n", + " a.set_title(\"\")\n", "axes[0].axvline(TRUE_TAU, color=\"C2\", linestyle=\"--\", label=f\"true τ = {TRUE_TAU:.2f}\")\n", "axes[0].legend()\n", "axes[1].axvline(TRUE_BIAS, color=\"C2\", linestyle=\"--\", label=f\"true β = {TRUE_BIAS:.2f}\")\n", "axes[1].legend()\n", - "plt.suptitle(r\"Posterior of $\\tau$ and $\\beta$ under a moderate bias prior\", y=1.05)" + "plt.suptitle(r\"Posterior of $\\tau$ and $\\beta$ under a moderate bias prior\");" ] }, { @@ -300,7 +377,7 @@ "id": "74929da4", "metadata": {}, "source": [ - "The posterior of $\\tau$ is centred above zero; the posterior of $\\beta$ is non-zero, pulled by the prior. The data informed the sum; the prior shapes the decomposition.\n", + "Under the moderate prior, centred at zero, the posterior of $\\tau$ is pulled well above the true 0.30 and the posterior of $\\beta$ settles near zero: with no prior reason to expect a confounder, the treatment absorbs the sum. The true $\\beta = 0.50$ falls in the prior's right tail. The data informed the sum; the prior shaped the split.\n", "\n", "### Tipping-point analysis under a Gaussian outcome\n", "\n", @@ -309,14 +386,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "2f6ee70f", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:06.093146Z", - "iopub.status.busy": "2026-06-01T17:16:06.093025Z", - "iopub.status.idle": "2026-06-01T17:16:06.116375Z", - "shell.execute_reply": "2026-06-01T17:16:06.115801Z" + "iopub.execute_input": "2026-06-02T17:36:05.725817Z", + "iopub.status.busy": "2026-06-02T17:36:05.725672Z", + "iopub.status.idle": "2026-06-02T17:36:05.746568Z", + "shell.execute_reply": "2026-06-02T17:36:05.746120Z" } }, "outputs": [ @@ -378,28 +455,13 @@ "beta 0.065 0.29 0.065 0.287" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def gaussian_sensitivity_posterior(\n", - " d_hat, sigma_d, tau_prior_sd, bias_prior_sd, tau_prior_mean=0.0, bias_prior_mean=0.0\n", - "):\n", - " \"\"\"Closed-form posterior over (tau, beta) given the observed effect.\"\"\"\n", - " prior_precision = np.diag([1.0 / tau_prior_sd**2, 1.0 / bias_prior_sd**2])\n", - " F = np.array([[1.0, 1.0]])\n", - " data_precision = F.T @ F / sigma_d**2\n", - " post_precision = prior_precision + data_precision\n", - " post_cov = np.linalg.inv(post_precision)\n", - " prior_mean = np.array([tau_prior_mean, bias_prior_mean])\n", - " rhs = prior_precision @ prior_mean + F.flatten() * d_hat / sigma_d**2\n", - " post_mean = post_cov @ rhs\n", - " return post_mean, post_cov\n", - "\n", - "\n", - "# Verify analytical agrees with MCMC under the moderate prior\n", + "# Verify the closed form agrees with MCMC under the moderate prior\n", "post_mean_anly, post_cov_anly = gaussian_sensitivity_posterior(\n", " d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=0.3\n", ")\n", @@ -421,28 +483,28 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "e9d91b3f", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:06.117824Z", - "iopub.status.busy": "2026-06-01T17:16:06.117649Z", - "iopub.status.idle": "2026-06-01T17:16:06.637906Z", - "shell.execute_reply": "2026-06-01T17:16:06.637365Z" + "iopub.execute_input": "2026-06-02T17:36:05.747862Z", + "iopub.status.busy": "2026-06-02T17:36:05.747780Z", + "iopub.status.idle": "2026-06-02T17:36:06.105478Z", + "shell.execute_reply": "2026-06-02T17:36:06.104918Z" } }, "outputs": [ { "data": { - "image/png": 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vVlaWtGvXznJAt0SrywoXbJSXlxez927QoEFS12gBsZbbOFvOfvl46XRQ64iX4d8UkJfOmyzfvMb3CQASFcErAAAAQJLTRq5wISP68LgudHnnnXeefPXVVxIPGpaiHYsrKyuj7sitRQCp0IgZLxp6ceqpp8a00GR7U6dONSOfxHrUFQ2I0CITO0feirdk/R6my/6pbeO+fs6lS5fGpFBQR8vZnp77rrnmGvnoo49iUviohRV2f3dT9RwUq5HpHnvssbjeX1x00UXy/fffx2T5es3WkXCSTSqfm5NVoo+moqPnAOkUwJLv1QCWHOnYIEty3NtChzxOhzTN9krb3CzJ92gAy5YIFv3ltt5fLgs3lsqKEj8BLAAAAAAAAEAIhx12mOTn51tuHzvaCV999VW59tprw3b4s4OGg2j75vvvv2/rcnV52k67bt06SSf6uR9++OH6Xo2EoJ3MtU1v/fr1MX8vHWxCBwCJV/jKJ598ErPBG7Qd/aabbjKhFLBnEBoNJ1izZk1MNucrr7xigrLiYdKvr8m8tb/F/H2GDRsm//3vf+PSPl5d0zNr1qyolvPCCy+YIJxU8Prrr5uBgWJBQzr0XKmDP9mF+o3QtCZPzz8LFy6UWF1nb7jhBup0UWsacGKlQ4cOMduaVsvWc1OiDRwYrv5d6zLq671Xrlxp63kcSEaZOR459cl+0uPUbhEvI1hZJRNv/UTeufdzCVYmXy0zAKQ6htoEAAAAklxtGkeaNGlSp2Vq5/raJN17vV45/PDD5ZBDDpFddtnFvI/P5zNFBdp4rB1hNeH+448/DtsgqqOxNG7c2BQHREKXf+ONN4Ydtcftdsu+++4rBxxwgHTq1Elat25tUro1nMblcsnmzZuluLjYNK7qA3V94K+TNrJq8EAqWbFihRlBSv8Mp3nz5rL//vubbbfzzjubgi6ddLvrg/Tly5fLF198YQo8wqWav/vuu/Loo4+axtRYPfzXY1gf8tekadOm5pjde++9pWHDhlJUVCQej8fsd93nP/zwg3z44YdS35Lxe5hO+6c2br31Vvntt/8tPHE4HLL77rub4kht2GvUqJHk5uaaRm/9Pn7++efmM65evdpy+S+++KIMGDDAnMvUU089Zb5fNWnbtq15P31f3a6FhYXmGNH30PObFoGFG2FJRwQbN26cGRXMDql6DtJ9qaNwdO7c2ayr7mMdaUL/Xifd/3o86zR//nzzHdVtq1O4xksNXtlrr73Mtoi1u+++O2S4SIsWLeTAAw80+0O/ozrp59KCMR3VTUdT/Oyzz8IGw2nx0bHHHmuWkyxS9dycrPQ7E25UnPrWrVvkjaxAMmvgcZupOFAhy0r8Uly+pQDc7cyQxlleKfRWydpAQDYEKs13Wa+AGwIVZsrzuKTQ6zZhLQAAAAAAAAC21Tr07NlT3nnnnZCb5Lvvvotqc7355pty++23h32ddvbbY489pEePHqadp1mzZlJQUGDawrQNTNvv5s6da9rudLIKHdDwCA16ee2110zbWrS0jUrbAWsTSlG9Tffbbz/zPL+6fkTbebRmRNtvNbhD23x/+uknM7DAL7/8IolI1/eWW24J+zqtjdH9pu2N7du3N59Z2zK1HUvb+3Q5Om3cuNGEb2xfMxOPEBM7aHvlhRdeGLatsrrd85hjjjHHgLbrahu0Ht/Vn//rr7+WKVOmmOPZitYq6QAgGlwUy7YhrZm48sorazy+ta5in332kYMOOkjatGlj2nD1eNbPot9JPT9oOFO4js+63W677TZTj4DoBjq55JJLaqyN0WNM25B1X+kgDlq/oZN2/tbzpdZuaFuytkuHa0u+6667zHJ0X8fKrL9myE8ram4jz8zMNN8frR/Q71P1caft4loLoudPvQ7Upo1d6yGefvrpsK/T+j699uiktUVaZ6TvmZOTY87bug118B99X61d0OuS1fGu+2n8+PF1rq9UOhCThsWEo+un513dVlq/06pVK7O+eu5Vet7V9dT1nzdv3tZzr35va3Mus4PuozvuuMPy2qH7uWXLluZ41Wu+Xhe07kjPlXp++fPPPy3fQ2svNdjlnnvusWWdqd8IHaJ1+eWXm5qkcPQ47N27t9m/el+g10Ftu9V9q8fijBkzzPmopuNw8uTJ5t8Btb0/C0Wvi3pejBW937WidVyxDH6pK722WtF7u1ipzbJ1e+l1wG56z6O/ObSGUn9z6X3vsmXLzDVS5+lvNL1uag2dXvu1Zlbvo6rrF4F4croccswtB0nDdvky5T+fS1UwsgGDv3j+B1m7aIMMfqCveLPdtq8nACAyBK8AAAAASU4brsLRBq/aevbZZ8OOJqQPuk866SS57LLLanyAqg0yWhCj0+DBg82D1vvuu0/ee+89y+U+99xzpkG0T58+UldTp061DA3QAhHt4H/OOeeYB69WDZ3VjdG6/kcccYT5b21Q0kIabUjSTvu1aZhS+vodXX/99aZgqSYaonDvvfdKvBr4wwUe6INp3WZHHnmk2YY1yc7ONg0f+gBbG+2mT58uDzzwgGXBkQZEaMObFjHZTbftX3/99T9/ryEIOtKC1Xvutttu0r9/f/M6fYBfX5L1e5gu+6c2NMhECzl21K9fP1M4qEEioeh5Rz/jE088YY6FUIU0Wkyl37URI0aYRicNE9nRrrvuapal+zSUgw8+2BQfaqiKjhxkVTA3fPhwOf74400RYjRS7Ryk1xUNtunbt69ZbrjtUx1WUh2Io7QAZdSoUaYYL9Q+12vRzTffbApqtXAuVrQgpqYAGy0S1ONXi4Fq0rFjR/P59ZyjxUwPPvigjB071vK9dF/pqEnJIFnOzdq4HCqgSsOrtOgzlLoEW2mDdiIEEGoDeyLT8xiQznI9LjNtKq+Q5SV+E6yiXBrA4tsSwLLeXy7rAhVbA8g2BirMlPt3AIuXABYAAAAAAADA0AFmrIJXtIO/dmrWgR/q6vvvvw8b3KGd3bW9VtvvtF2oJtUBAjpf2/jKysrk+eefl2eeeSbkM33toK/tfBMnToyq/UE7CNcmdEU7kJ9xxhly+umnh+w0WF07oh01tZ26enAK7WSvHY01jCORQli0jdFq9Hndrueff76pm9GglVA0QEenHds4tP1SO+VrDYzW52gnyESkASjaTqfhBVZ0sBJt99RBImpqh9b5Omkb9MUXX2za0P7zn/+Ytr1QNDRD33vChAkmkCAWNHRFB5TasXOuHs/nnnuu5fvqgBHaTqjtklqXpMdyKNrRXkOMNGQhUQwcONBM29P9oYEBoeh+q0vNnJ0eeuih/zlHaFuynhd1P2o4Tii9evWSoUOHmpqTO++8U2bOnBnytVrfoefXq6++WmLBX1Em78+dWON5VAOO9JxSHR6yI20T13qU8847z3wWq2uTHpcavGJFj3U9F5911lkha26qv7tag3TccceZc4HWh7z88sumTqUmGhxyzTXXmHoN3Ud1ofU6oZar9Hyr1zetDbDqSF997VR6zammy/7yyy+31iuuW7dOYkFDNXQb7Hj91Oug7j/dz1bXZ61j0HpIrQ/ROhGr9XzrrbfMfUS0IQfJUr9RH3Tb6HFjRb+3um/1Xki3Q010YCj9zun9m97LjRw50lzrtqd1ZvqdA8Kxuu/QYyhc2Eg0wt0LWN3f1YdQ38lqVoFi0arNsjVEZ/trlV30t6gGR1ldq3TSmkS9x9L6xupzvdbIapii1t9b/dYB7Lbfv3aTglZ58saV70ugxHrg4FB+nbZQnjn1TTntqX7SoKn19x8AEB8ErwAAAABJrvrhYSj6QLq2DVX6QLSmjvs7Npw+/PDDpmG0Lg+utbP+6NGjTYOw1WgcOmqKdtquaxGCBgaEoh3gNcBAR/iIlD6c1YfFOmmDqBYYvPDCC3VucE0Uug91NKZQtLBEi0cuuuiikGEHVgUA2ilf9+WYMWNqfI12qrz11ltl0qRJtnfcrinUQ4tHtNHW6XTWahn6ulDBArGWzN/DdNg/tbVj6IruM93++v2oDW3g1sIYHU1Eg1OqOyLvSEfo0SAo/T7t2PCkRRG6XWt7nho0aJB06dLFFEyEakTToBQtMDj66KMlGqlyDtJGu7PPPtuE5dR1PXekRZv/93//Z4r7tMhPAyVCNfa+8cYbctppp0ms7Bi6op/txhtvNO9Z2+NJg2XuvvtuM1KXnnOsinh10lERE1kynZu16DlU0YBVIUP1+iSTdu3amVHSoi1S12K6mtx0002m0AhA9HLcLunYwCWbyytNAMv6wJaCA5cjQxr6PFLgdW8NYAn+fd9THKgwU47bKYWZHskkgAUAAAAAAABpTtumatPOowELdaEd2LRt0qrzuAa/a5CAdsKtC61Z0c75OkCFBkPoyOU10bYxrevQQIJIAze0fTRcYHv37t1Np2qrgTKstGjRwnRU1kk7NT/55JMxHXXejpoZ7TivncR1kIVIaRu9bjudtC1TAwC0ZiYRO5vPmTPH8jV77rmnCXioS0dxDffQz661DZ9++mnI12lbnIYOaFt0PNpxtX1fvzfNmjWr9TIOP/xwExigbWBz584N+Trdv4kUvJJsdtxX+fn55rjTMJ/a0jAIDQPRc5uGPYWiA51obUWoAJRolFWU/s/f7bPPPuZ6YDXwWU2fJRStQ9F22VB1MapTp07yyCOP1DmsQ89/1113nRx11FFywQUXmM7aoQa+09CQ6pCt2tAAirffftuy7V3DKrQWJFI6II/WH+ikNRsa7PTiiy+K3TQoZcewFK290fNZ9QB24WhdyQknnGCOcT2/aBhcTbS2ST/DHXfckRb1G/FWm22jx6ReO/R7VdsQiEsvvdQMRqXnmu3rTvS+K9EHywlH7+MS4V4uWho2kaiBExoaZzUQXKzDe8ItP1wtVbw1adKk3oJiarPsWG0vq9AVK3r/oHWoOun9yamnnmp+q9X2+gVEq/MhbeWc0QPk5fMny8bl/wzJrK3lv6yWEUPGyWkj+knzLnUP0gUA2IvgFQAAACCJaWrz9OnTLV+jQSHaAbc27rnnHjPakFVBjBZjaBFCJE4++WTTEKNFGKEaS3X0pccff9yMglCXhsxvvvkm5HwtfIgmdGVH2ulcl6dTuJGSEtHs2bMtC2A0qEaLg6IpoNBj7q677jKFAzraQU20oOq1114zI+/Eiu4rTUI/5ZRTJFkk6/cwXfZPJKq/B5Ek/WsKv4YKaDFGTXQfaoHi9o1eWsygBQA6GktdaUiGjm6loSdWo89EE7ySCucgLdDQAictorCbHie6bA3ACVV4qucAbSSMR/iXBtNoMU2kI/VoWMvy5ctD7ofqYyrRg1fS6dycTrSIL1ToSnWxKgB7Zbud0qFBlpRWVMqyEr+s828JYHE6MqTI55F8r1s2BCrM31f+fT7cVF4pm8pLzb8t9LrF56pdWB8AAAAAAACQanTQBm1L05CRUEKF+1vRDmpWney6du1qAi0KCgokUm3atDFtc9oZ+48//qjxNdomqm1LjRrVvaPRM888EzZwQwei0I7eta2hCadnz55mimSb293J2modtO04mtCVmkJYNEhHp0SqmdF2H21ntqLBQXosa3tvXWnneu2orp3PrQbJ0uP8xBNPtAyasMP+++9vAgK0jbGudBAN/b5pu3+ozuZffPGFaZ+M5PuI/w1s0nOUnsMj+b498MADsmzZMpk1a1aNr9mwYYM5JvU7GWs6CM5TTz1la8jL7bffbhl6oPV5GiQRzXtqHYYG1Gi7vB7XNdFgFx0op7aD9+j+0DCBmmgth15bowld2ZGulwab6GQVCGIH3Q7/+c9/zD1HJIOn6PGu19xQ9ysaJKQDE2mwTCSo3whNzxdWQXqtWrWSl19+OWywQ030uqb/VuuVahp4LVlpEJAGYyW7Sy65xNyjJKK1a9dazq/v4JUdg6fqmwZE6nUkVP2Y/ubR73mk59BQ9P1++umnsK9LtO21vdLSUnMN0lBKDULU4DUgHprt3FAuGDNYXrlwiiydXfO9ZjjFKzfLM6e8KUOG9ZVderezfR0BALUX3VC8AACgXpRVaoeTCia2Qb0cA3r8ITFoA5qOwhNObTtK//rrr/Lhhx9avkYLUCLtUFytf//+cv7551u+RosQwj1s3968efNCNhhlZ2ebRtNYsasgJ57uv/9+ywZYbTi1a9QaDb3RYo9QtCO+VWNftPRYS6ZQj2T+HqbD/omUBplEErpSTUcL0QCRUHYsgjz77LMjCl3Z/rqho3WFoiO3RTNaSSqcgzRkIxahK9W0wEGLlUIVsejIETriUjzoqEmRhq5U0yAfLSYL5YMPPpBElm7n5nQLMQxFixhiXRALpDMNT2mflyVdC3KkyOuR6igxDWApzHRLuzyfNMr0iGu7kLHN5ZWyeFOZLNlUJiUVPJsAAAAAAABA+tHOddph1ooG4tc1tGP06NGWHf+03Sqa0JVqhYWFpmN7qNALHXRHg+7rSttOrAYBUEcccYQZuCEWNR5t27aV+m7LCkU7/etnT4eaGQ1UsRpIQQNEdJCDSEJXqmn7rXZqt9rnwWDQBLTEkgYZaSfxSEJXqjVu3FiuuOIKy88Rro0UtTtv6/EQSejK9uErt912m/mzPtvc9Xxid+jKzJkzLY8zHSjj4YcftuU99fo5bNgwM5hRTTSQZcyYMbace7WeJJo6oXCsjoVoaUCV1jlFErpSrXPnzmawoVCKi4vlq6++imjZ1G9Y19C+++67luE9ej6KJHRl+/tCXUY0xwfSjwaEWcnLy4vp+4db/vr16yWR6L2q1b2mhlqFC5yMxPz58y2D0BJ1e4UKh9H7XB0UMpGCIpHachtny1kvHy9dDo/8vr+8tEJGX/yOfP789yHDlwAAsZc4TzsBAIClFSV+GTFnsfyweqMs2exna6Fetcz2yu4N8+T8Lq2kSVbtEv5hP20I1M7v4RpvdXSS2ghXvKKj9Bx33HFiB+2ArSMX/PnnnyFTp1999VWTwl4bVgn6u+66q60Nvsnum2++kRkzZoScr42eOmqFXbSxWkMW+vbtKyUlJTU2Wk+bNk2OPPJIsZs2PmhgRTJJ5u9hOuyfSAwZMkQOPfTQqBv/9Hs5atSoWhUvXHbZZRItPReECvbQxrsffvhBDjjggDovN53OQdHSwisd8U8LBGvy/vvvxzT8RWngykknnRT1cvQ6rJ9FRx2qie6HBQsWmJGPElE6nZvTjVUhghasRlOsCqB2Ml1OaZvnk2aVXvPsa01ZuQSlShwZGVKQ6ZZ8r0s2BCpkbVm5VPxdVKChKyWbKsXnckih1yPZ7tgVlwIAAAAAECs///xzvWxc7WzXsWPHOv+7uXPnmkCE+tC1a9c6/xsNkqhNh6FY6NChgwlwr4uVK1eaZ+WJvE2RODQsQTvF2RW8op1ntf0vVEj5o48+at7Tzu/I1VdfLXfeeWeN88ePHy9XXnllnUZwf/7552Xz5s0h5+v66/uF6myf7HTQhlD23XdfSQc6cEi4wIRrr73WhP9ES9uPtBPnWWedFfI12par16JwQUmR0M7uWjOmA1FF68QTT5Thw4ebzqmh2vftaC9OZ9dcc40tgz3oMg455JCQISW6r2JNByOxuwZPrzGh6HvpNcqOY72a1liceeaZIWsAdCCU0047Lepzr9VgR4ksNzfXhEvZEexy7rnnmjqnUB3e9ZiNZFAm6jdCe/HFFy07aQ8dOlR22mknidYuu+xivkfhQu+AauF+m8e6Nifc8sMFw9SH3Xff3dTShfL222/LHnvsYet7Tpo0qVavC3XfmIhef/11c73W+wl9HgbEmsfnlhMfOULeHzZDpj8zK6Jl6KV86r1fyJoF6+Xomw8UJzVRABB3BK8AAJAEPlm6Vm6fOVc2M5ovEoSG/yzZvEo+XrpWbt23oxzcPPpGadReIBCQu+++2zT0hTNgwABp2LBh2NdpAYo2+ls12t9888227SYtkLnhhhvkwgsvDPmat956y3Q+1kKecKwKaGrz+dOJdta2CneIRUdu3QeDBg2Sl156KeS+jkXogR5jyfSwPNm/h6m+fyKh+8xqhKi60FHIahO8oqErdSlCDEWLG7SIRUMeahJp8Eo6nYPscPLJJ4cMXgkXvhYtLTy97rrrbFueBsH997//lcrKyhrn//jjjwkZvJJu5+Z088svv1gWCwGIH6/TIa1zfdI0yysrSv2yunRLAIue6/K9bmngccnGQKWs9QekPLilYLC0IihLK8ok0+mQwky35Lhp8gIAAAAAJI9u3brVy/t26dIlotAXDZ+OxYjKtRHJCK9XXXWVjB07VurD7Nmz6xxsop2Abr/9dokXRs1Nbo0aNbKcv3r16lovSzsZTp482fK7r4PdxGLwiieffLLGddUR1D/++GM5/PDDa11DE+77roMDNGjQQFJVTQNQpFvNzLvvvmu5Hfbee2/bBlJQ2laux+h7771X4/xgMCgTJkyISfv3McccY9v30uVymbby0aNHh2zDReQ0eEcHn7Fz34cKXtEQNw3eatq0qcSC1qzogCR2mjdvnnz11Vch52u4UbNmzWx9z+rlvvzyy+b6saM//vjD3CvX5l4uFc+9Z5xxhrRo0cKWZWnQ1f777y+ffvqpbecX6jdC0+NZB/kJpUmTJnL++eeLXbSmRWtX4hkeieRldb5UWVlZMX3/cMsPVR9Zn7R2U79joeg8DbO0KxBNgzhr+wwlFttLr/dap6XhUJ06dZKioiITmKOBYHp+09+IOi1cuNAMJvjdd99Z9hvY3vTp0+Xyyy+Xxx57zNz7ArHmcGTIEdf2lIZtG8jE2z6VYEUwouV88/ocWbtko5z48BHiy0vtWnsASDSpGd0NAEAK0dF+CV1BotIwoNu/mWuOU8SeNspPnTpVBg8eXKvQFX3gqB3wa0OLVqwebvft29eMOmSnww47zLJDq4788tNPP9VqWVYjhunDVmzbFladx3VUg/z8/JhsLl12KDNmzKixMTvaB/EHHXSQJJNk/x6m+v6JtPDErmKKPffcM2ygihY/6D61gzbK6cgJoViNohdKOp2D7CzE0sbEUKOL6shtsXLooYdK69atbS0CtjrfRHJMxUM6nZvTkVVHEe2AAiD+PE6HtMrxSbeiHGnq84rj75ApDWBp4HVJ27/DWbzbjUxbVhmUvzb7ZVFxqRQHKug4BAAAAAAAgJQWLkCkrKys1svSkdL9fn/IkH7toBYLOkCHDkAQyieffFKntpy1a9eGnN+9e3fp1auXpDJqZsSys7m64IILbN/uOlBCNOsUTTCCnQ455JCQ85YsWRLyHIHwTjvtNHMutcvBBx9sOTiHBpnEyoknnmj7Mq06eGvd4znnnCOxoLULGmIT7TXI6tyrwWbJRmuSrK7Ndp9fIjleqd8I7bPPPpPi4mLLuik7wy2ys7PlzDPPtG15SG0a6mEl1mEYOqhWNOtXH/Sab3Wd0e/7Cy+8YNv7aZ+E2gYp2bW9tB5Ww2MmTZpkzu8azHnllVeaa7SGvWnQYNu2bU0Yi/6m09DD8847T5555hn55ptv5KGHHqp16O1HH31kXg/E095DusgZzxwjmVGEpsz7fImMPHm8rFu80dZ1AwBYI3gFAIAEN2LOYhNuASQqPT71OEXtaGO0NkqHm7RD7a+//mo6g48fP15uuOEG6d27tylq+e2332r1XjfffHOtO/xrorOVY489VmIh3Ggy4darWkFBQch5mmwdLjE9XegIElYPvfv37x+z99ZAiPbt24dMQJ81a5at73f88cfbWrwQD8n+PUz1/ROJgQMH2rYsp9NpGpKs6HfYzu1qFSah16m6SqdzkJ208TAUHW0pVnSkQbt17tzZ1mMqHtLp3JxuNm7caO67QyF4BahfbodDWuRkyq6FOdIsyyvO7QJY8jwuaZPnM3/vdW679/FXBmVZiQawlMlGAlgAAABQD/7880956aWX5LrrrjMh+trBVwvI9TfmPvvsYzoc6Wjnt912m0ycOFHWrVvHfgIAAHUWbrCGuoQkfPDBByHn7b333tK8eXOJ5SjuoXz55Ze1Xs60adMs55966qmS6qxqZr744ouUD6vWQTZmzpwZcr7WTh1wwAG2v+/OO+9s2ZaroQLLly+39T21c6nVAA52t+HqIGFLly619f3ShdZ42FkzUh100LJly7i3ueu1QDtA2+3DDz8MOU/rJO0MiYjFNcjq3KshGMlGB+exa3Cp2pxfVq5cWedgJ+o3JKJjTmvJ+vXrJ3bTcIR0qP9D9MIFdeg1M5bCLT8Rg1c0gOyoo46yfM3jjz9u+hnY8Ux72LBhtX59NNtL72VOOeUUmTx5sgl70SAVq/tpq32q5zXtX/Hoo49KXl5e2H/z7LPP1ul3JmCH9j1bynmvDZTC1uGP0VBWzV0nI04YJ3/Osve3JQAgtNjGAgIAgKj9sJp0SiS+H1aHTkrHDtvqhx9Mw2CsnXXWWWE77G7vq6++CjmvsLDQsrEx2saX+++/Xyoraw6Y+vrrr+Wiiy4Ku5zWrVuHnKehKw8//LDceOONku409CCUbt26SatWrWL6/vvuu6/Mnz+/xnk///yz9OjRIy4jZiSqZP8epvr+iaRxZ4899rB1me3atZPff//dsvjRTqGCStTq1avrvLx0OgfZqaioKOS8RYsWmc47dtNO7XYfT8qqoTSSYyoe0uncnG5++eUXy/kErwCJweVwSPPsTGns88qq0oCsLPVLxd+dBHI9LjNtDlTIGn+5lFUGzd8HgkFZXuKXNY4MKfS6zWscFiNAAgAAANF28nz77bdl1KhRlr81dRRSnZYtW2Z+148ePdo8Q9RwFh2xPhYdQeNFO0rVdkTUeNJRzL1eb9J9Jh3N1u7ObgCA1BLu+lZWVlar5WgthY4UHkq4jn52BEhop/qaBtLRoAcNqbPq1F6bjsZ6P9CnTx9JdVbtrBrEoPeqes+Zqr7//nvL4/7oo4+OWYderc3SNj2rtsa61G/Vps3bbs2aNTOde/X3Sk3WrFljWTuA0IETtekEXFedOnUKGbASqzZ3bRO3O1xBax10qq9rkIajhjJnzhwTWKV1E5Gee/V3v4abJdM1yGqbRHO8WtHzS11C3qjfEMtjLpTu3btLkyZNxG5NmzY1x43VeyeDSy+91EyInVC1T9Vcrth2bQ23/IqKCklEQ4cOlQkTJpggvFABKDqYq97rR/odX7t2rTn+deC62opme40bNy5skGhdHXHEEabGS7eF1oCGotf2O+64wwSiu91uW9cBsNKoQ4Gc9/ogefXid+TP7yILT9m8tlSeP32CDLj3MNntaOv7KwBA9AheAQAggVUEq2Tp5rolegP1YenmMnO8uhx0ZEoE55xzjlx77bW1fr02nFuNTqKN9rF6sK1FLtrAFip1O1yH2O0bcdq2bSsLFy6scf6LL75oGoCvuOIKU7Carr777ru4FmfUpcO9Hcnr1bRgRkcWSiap8D1M5f0TCd2mmtBvpwYNGoScpwUfu+++e9zeb/369XVeXrqcg6xog+fmzZtNhxz972hHl9MRgGJ1/MaiCMyqOHbDhg2SaNLp3JyOrLah3ltqsA6AxKHPG5ple6VxlkdWlwZkRWlAyv8uMMr2uMxUUl5pAlhKK7YUbpUHq8zr9O8KvG5pQAALAAAAbPbJJ5/IXXfdZUYFjbTTgS5DJw0FvvXWW6VDhw5J2dH19NNPl0SjnQ4iDVuuz8+kYTw6AisAAKGEa1+qbef42bNnW45WHosO2NvTNhYdeCJU5zhtI+nZs6flMv766y/LoAH997HuyJkItJ04VIiNuvfee00HyTPPPDNmAST1KVy7WbjjKBoaoGgVvKLrZmfwyl577SWxoO24oYJXErEdNxnEal9ZtWHGal/pQDp2s6oficc1qHHjxpKfn19j7cumTZtkyZIlYQcP2m+//SznX3XVVaaD9/HHHy/pesxq3ZFed0KFLugxW9vgFeo3QtPrf6iBqFQsw3512ckevILYC3f/GS6YJVrhgkIS9feChrgde+yx8tZbb4V8jdbMn3rqqeZZYps2beq0fA0I1/4Gc+fOrdO/iya0xO7QlWp6zX7hhRfkhBNOkAULFoR8nZ4rNcxm8ODBMVkPIJTsQp8MffE4efPGafLjpD8i2lAVgUoZc9X7smbhBjnkor3DhgQCACJnb/QsAACwvVNJi+zIRuEC4qlFdiahKwlAG6oefPDBOoWuqN9++81y/i677CKxpEnToWjj5ooVK2q1nHAjRDz//PNy5JFHytNPPx2zET4SmTZUWnUej0cxt1WIgxZE2UU/S7IF7KTK9zBV908kNAzKblosZ1UIZfUds/v9NDikLtLpHFQ9GsT7778vDzzwgFxwwQXSr18/UxikBUna2UNHhDrssMOkd+/eYSftIBKKjvQXC1rkGgtWYUQ6inCiSadzczrSkcoi2fYA6pczI0OaZHmlW2GOtMrJFM92nUiy3E7zdy2zMyXLta14S4NiV5UGZMHGUllTFpDKKIPPAAAAAO2gfPvtt8t5550XcehKTaM2a2csHfUTAADASrg2Fa/XG/Vzcu0cGav2ou1pp/do2u+sPoPaY489JB1o58eDDz445PxgMCj//e9/5ZhjjpFXXnklZMBGsvr9998t58ey3adjx46WnU/DrVtd0Y6bPFJpX8XiOxQqdEs1adJEcnJypD6vQVb1Lduv52677Wa5P6677jrTuVs7zZeVlUkiS/RjlvqN0DQ0wSqYT8MbYiUdBl9D9MIFdYQLRolWuGCXaIJEYk2vI0VFRZavWbx4sQloGT58eK2uNVr3qXX0Ws9Y19CVuvzejDcdZE4/l9X1vbovAVAfXB6nDL6/jxx2aXSDVE579GsZd92HJogFABAbiRnLBwAAttq9YZ4s2byKLYKEtnvD3PpehbSmCdADBw6Uyy+/3HJUi1B0hAYrse54Gq7Tsq6fNlSGM3ToUHn55ZctHxxrQvewYcPk4Ycflj333FMOPfRQM/qErkMqjuqzvXAPyLWRM9yxEC2rBpLly5fb9j51TW5PBKnyPUzV/RNpQ47drIJQ4v1+dQ1eSYdzkI56NGnSJJk4caIZEVcLGGMtVoU5dof41Kagpq7HVDyk07k5HRG8AiQ3R0aGNPZ5pWGmR9aWlcvyUr/4K4NbA1h0Kq2olHVlFbLp73sADVxZU1Yu6/zl0sDjlgKvmxBZAAAA1Flpaamcf/75JijFbvp85MYbbzSjcl5zzTXsnSgx4iMAIFWF66Rc20EwrNrvGjduLCtXrpRYsxrxvDbtdzrCuxWrDvGpRgeDmDp1qmXHax3h/Y477pB77rnHDBahNTP6p4aHJPO9k1WbntZRxbI9TTvqdurUKWS7k91t4LTjJo/62FcaEposAw/Nmzcv5LzmzZvHvH5E+Xy+qGtI9Nx70UUXWb7mp59+Mh3nb7vtNunVq5cJytJzb+vWrSVR6LnM6tiKhi5348aNUdeJUL8R2qJFi+otHIXgFSRD8Eq45Sdy8Irey953333mebRVgIzWED722GPy3HPPmQHhDjroIGnRooUJbXE4HLJq1SpzbZs+fbp88sknlkGMhxxyiHz88cdJF7yi9NqqfSk0NN3qd/Ds2bPNAHpAvOnv7kMv2VeK2ubLmzdMizg85YcJv8v6pcVy8vAjJbsw9D0tACAyBK8AAJDgzu/SSj5eulY2V5BIicSU7XKa4xTxp51xNXFaQ1caNmwY8XLCFcvEOiQh3GgJtS3m0W1w6aWXyv333x/2tfoAeubMmWZSOkqGBrHstddeZtp9990tG1eTUbgG4SuvvFLqU6gG1kjk5iZfGFSqfA9Tdf9EIhZBKNoIlijvV1epfA7ShksdCeHZZ5+N++hwsRqxKhbHk93HVDyk07k53eh3R4uL6ytUB4C9ASwNfR4pynTLWn+5LC/xS9nfASw+l1N8OU7xV7pNOMum8grRLgfBKjHhK+v95ZLncZkAFo8zua5RAAAAqB/aIejiiy+OSejK9kaOHGk6IV922WUxfZ9Upttvp512qu/VAAAgJtatW2c5v7a1DjpwjdW83r17S6K334Vrg+zQoYOkC+34fPLJJ8urr74a9rUazqCdLnVSBQUFsvfee2+tm9FOiFahOInGqs0sHgPDaJteqOAVu9vzYhXmYdWOaxXmg8Rqc4/FvtL3i0Ugh9U1aNasWUlxDVK6nuE6qG8f5Pr++++bqTrkbJ999tlar6jn8foaOC5Wx6udxyz1G6FpoEIo+v2NZQBZ06ZNzcBeJSUlMXsPJD+rwd9UrI+fzZs3W85P9FpxDVK5+eabTYBXOLot3333XTNFQp+nah2n1XUtVvejdhkyZIgJoFm8eHHI1+i1mOAV1Kfdjukk+c1z5JWL3pGSdZENvrho5jJ5+sRxctqIo6VR+wLb1xEA0hnBKwAAJLgmWV65dd+Ocvs3cwlfQUKGrujxqccp7Kcp2jppSIGGijRr1syMMqONbPvuu29UYSt1KcqJdUhCuOWvXbu21ss655xz5LfffpOJEyfWaR02bdokn332mZmUbnftcHvAAQeYxlEdBSmZR/dJhs7ZGlxgFw3SSTap9D1Mxf0TidqOJJes71dXqXoO+vnnn+Xqq682oxDXh2BwS8fydDue4iWdzs3pRu8XrUaCIXgFSD76e60o0yOFXres81eYAJbSv7/nXqdDmmV7pbzSI+v8AdkQqJQq8z+RDYEKM+V6XObf6msBAACAUO666y75/PPPw24gHU306KOPNh1XdaRuLd7XwF4d0fvrr7+WqVOnmnYJK48//rjpKKzLQd316dNH8vPz2XRIaDq6bn2IdGTiCRMmxCwMPBYefPDBWnVKioVIgh4uuugi0zkIiLZzrWrUqFHatN+tXr065Dyt+wjXyTPV3Hjjjeaes65Bgdom9sEHH5ip+lqhAxb16tXL1Mx07txZEplVm1k8BoaxqoHQoAWd7OrQSztu8kiVfaWhDbGomUuFa1C1YcOGyYknnihz586t8zaYMmWKmaq3tQaw6Ln3sMMOk9atW0u8JMPxSv1GZPdD8boOEryCaMKdwgWjRCvcc9hEDxJRGrCo4Vy33nprzOoF9VnGCy+8IEuWLLHl92Z90d+Bp59+utx9990hX/PNN9/EdZ2AmrTeq5mcP2aQvHz+FFk1z7pONZS1f26Up08cLycPP1La79eCDQ0ANiF4BQCAJHBw80IZ3Xd3GTFnsfywuliWbLavczoQiZbZmbJ7w1w5v0srQlfqqHv37vLSSy8lVUNhrEMSwjXu1LWA77777jOFzCNGjIh4JA8d3eeHH34w0xNPPGGWd9xxx8kJJ5xg/jsZhWs8qG92FmrGYqSVWEu172Gq7R9ELxXPQTqywxVXXGEK5ZCa0uncnG5CjTpYPaqjBh4CSE5a/FuY6ZYCr0s2BipkWYl/a5Cx25khjbO8UphZJev95bIuULH1N2NxoMJM2W6nCWDxuepnREEAAAAkLu0I9frrr1u+RoM+/v3vf8uAAQNqHNFZR9HUtoYbbrhBnnzySTPyplWhvI5kqv+mTZs2tnyGdDJo0KD6XgUgrK5duybVVtIBQpJJq1atJJk0btzYTEBtLF++3HJ+bZ9xx7qDY7QCgUDY11i108Wjo3EidjJ85plnTIfM8ePHR7wcbYPSwECdNMiqffv25h538ODBUlhYKInGqs0sHgPDhDvWtM3RruAVIN58mVmy/q/irf8/p2GWuDzRtaFo20yihzTU5hq0/Xlm9OjRcs0118gnn3wS8Xvqdbl64Lh77rnH/F7Q37bHH388tVbUb0R8PxSP66C+R6KHKaF+hQs22bBhQ0zfXwOxrSRLeLPWr+uzDr3eWAUuRaJnz57mvl/v9cOFktg1aGwsaYhZuDBkHbBLw2yA+lTYqoGc+9pAef3yd2XeF9ahR6GUbfTLi2dPkuPuOFj2GrSL7esIAOmI4BUAAJJEkyyv3LLPlkKWsspKqQhGFiYARMvlyJBMHjSlFKuGQi3KiHTUs9oK17hTlxEklBYzX3nlleZB8P3332/LaHF//fWXKYIeOXKkHHvssXL55ZdL06ZNJZnUdTsms2R8GJ5q38NU2z+IXqqdg7799lu57LLLCL5Icel0bk43VsErXbp0ieu6AIhdAEsDr9tMGqiyvNRvgliqn2s09HmkwOuW9f4KWR8ol8q/A1g2l1eaSYNXNIBFg1gAAACAjRs3yh133GG5IbQzqrYhtGzZsla/+a+99lrTjnHppZeG7HCmna5uv/12E9CSyHr06CG//fZb3N5v1KhRliOWaoD+/vvvH9N1iOfnBQBge3rfsHTpUsuNUttahkRvZ6jNQDtWgRt5eXmSjjwej+mwf8ghh5jOkwsXLox6mfPnz5dhw4aZgYtOPPFEufDCCxOqg6pVm14iBK8wmAKSmb84IG/f8enW/3/MLQdJfvPogq30OxHpYGrxUtf102vOU089JWPHjpXhw4fbEkLx888/m+mRRx6RM844Q84666y0DnGifiN5r4NAuOC+NWvWxHQjhQsp0QGakoU+T3777bfl4YcfljFjxpjwkGhkZWXJxRdfLEOHDt1a1xvu92aLFi0k0emzel3PUJ9F70U00DQZPgtSny/PK/96+mh5+87PZObroWsarQQrgvLmjR/J6oXrpc+V+4nDkWH7egJAOiF4BQCAJGRCL+j3ASAO4tHIaTWao6ppVMja2G+//Uxj5ocffmhGofz888+jfshcUVFhRgWaOnWqXH/99aagJFlE+9lRf5L5ewik4jlIO9pcddVVtSqQ0wZJHZVYpw4dOpiONzrqgzbYasOlTvr9crlCP6LTopzHHnvM5k+BaHFuTm6//PJLyHkErwCpJ9fjMpMGqqwo9cs6f7n5e6cjQ4p8binIdJlQlrVl5VLx9713aUWlLK2olEynwwS05LidJswFAAAA6enRRx+VdevWhZzfrFkzeeGFF6RJkyZ1Hnnz8ccfl3POOSfk8zNt2/jggw+kT58+dV7vVKXtNFYGDBjAM20AQMr6448/wrZRaJtUurTfWT2zS4XPF40jjjhCevfuLZMmTTK1MzqwRLTtW6Wlpea+d+LEiXLnnXcmxT1quFoIO4Q71ni2DPxv/V0q0tqPE044wQzqpufdcePGWQ4IUlsbNmwwzyX0t/B9990n++yzjy3rm0qo36jfaxB1hQgnOzvbhPatX7++xvmrVq2K6UYMt/xkC9/QukMN69Znyi+99JJMnjw5bLhMTYFhgwcPljPPPPN/nmkvWrTI8t926tRJkkHHjh0tQ2R0XrLte6Qup9spx95+sDRsly/v3veFRPrT/bOnZ8maRRtk0H29xeNz272aAJA2CF4BAAAA0pzX67Vs6NTCiViOlrBp06aI1682DUda6KGTjiTx/vvvy4wZM+Trr78O+RC/tqNI3XLLLTJ37ly56aabJBlEsx0Re6n8PQRS7RjSEBQd8SBcA6M2TGpBY7QjuyT6SE+pjHNzatLi199//z3kfIJXgNSV7XZKe3eWlFVoAEtA1pQFRK+yjowMyfe6pYHHJcXllbK2LCCB4Jbrb1llUJaV+MXj0ACWLQEu+noAAACkjxUrVshrr71m2blk2LBhdQ5dqbb//vvLJZdcYkawDkXnJUOn1njQ0b6tAlW1XWjgwIFxXScAAOJp1qxZlvP1Wti1a9daLcvj8Ugqt+UUFxdLutPBHzSUTqc///zTBPppzczMmTNl8+bNES937dq15h72uuuuk6FDh0oiHAdaxxNJLYQdqLcA0rd+pCaZmZly2mmnmem3334zg8bpuff777+v1QA/oSxZssTUofz3v/+Vfv36SbqhfiOye7porve1lez3XDoAl07JToM0dEpUGnARqmZbQ0P0/Bir64OeP63oQGrJqFWrVnLjjTeaQUS/++47M82ePVsWL15sahv1/jgQCJjrkh4bbdu2lZ133tk8j+7Zs2fIc4dVTZXb7ZY2bdpIMigqKrKcnwrfe6QWfZ5zwNA9pKBVnoy95gMpL40srHDOu/Nlw1+b5NQnjpLcxtm2rycApAOCVwAAAIA0Fy7MQQsEYhn4EK7hRR/62qFx48Zy6qmnmkk7sWvDpo7oow+btTjJKtk6lFGjRknTpk3l7LPPlkRntR212OaHH34wf6J+pMv3EOkrVc5BWkRo1dFGnXXWWXLNNdeI0+m05T1p5Ks/nJtT0/z586WsrCzkfIJXgNSX6XJKm1yfNMvyyopSv6wuK5dgVZUpYsjzuCTX7ZRNGsDiLxd/5ZbRUAPB4JawFn+5FPwd0kIACwAAQHp44YUXpLy8POT8IUOGyN577x3Ve5x77rkyceJEWbBgQchi908++UQOPvhgSXc6YriV/fbbL2k7SwAAUBuff/655fzWrVvXelAAq3YQDeq49957E36nZGdnp2wnYLvpsaHtmDrp4C9z5sz5R83MqlWr6rQ8rbu57777TM3MUUcdJfVJj+VEDl6JZa0HkIy007bWh+i5qCYa7HTppZdKKujcubOZLrroIhMq8OOPP27tHK9BLHUdOE6fT2jolYa/RvssItlQvxFZTVYiXAcT3YsvvmgG4Up2iX7u1JAQDVQOdV+pIYE60Fks6LKtJPuzRA0G32effcxkx2BWWlsfyk477WSu48mgoKDAcr4Ohgkkoi592ss5rwyQly+YIsUrIwtQW/rTShlxwjg57al+0nTnhravIwCkOkd9rwAAAACA9E51Dlfs0rCh/Q98tEOdJndrCIuORDlt2jRTtKxFIf3795f8/PxaL+uBBx6wTPhOhv2sDdnLli2L6/rgn9Lxe4j0kirnoMmTJ1uOQHTxxRebIhe7QlcUwSv1h3NzatJCYqsC7WQZmQVA9DxOh7TK8cmuhTkmhMWZkbH192Kux2XCWVpmZ0qWa9t1vSJYJatKA7JgY6msKQtIZVUVuwIAACCF6YigY8eODTlfRwW1o0ODFqtffvnllq955ZVXJN3p/nj77bctXzNo0KC4rQ8AAPGmnVq/+eYby9fsu+++tV5eYWFhyHk6SnoyaNSokWWnwWTvCBwrGniw2267ydChQ2X48OEyffp0ee+99+TOO++Uww8/XLKysmq1HO0ke+ONN5rBKxK1TS8eATxW7bk5OTni9Xpjvg5AskmFa1Bd6blAr9Pnn3++jBgxQmbMmCGTJk2Sm266SQ466CDzjKG2v42vvvpq82c6oX4jsu8TwStIFBrYYWXevHkxHaDJ6txMndA/a6pCBRqq7t27S7LQ3ypAsmretZGcP2aQNN0l8tr9Dcs2yciT35TfP1lk67oBQDogeAUAAABIc40bN474obMd5s6dG9X62UVH4Tn++ONNkIqOEjVy5EhTUKJp4FaCwaA88sgjkuiaNWtmOT9VG62TBd9DpLpUOQdpSFcou+yyixmlyG4Er9Qfzs2p6ZdffrH8HmvgAoD04nI4pHl2puxalGuCVtzb/QbMcjulZU6mtMrJlByXa+vfa+DKmrJyWbCxxASxaCALAAAAUs/HH39s+WzmmGOOsezsWxfaHtGiRYuQ87Xdor47tNa3999/XzZs2BByfl5entmOAACkqnfeeSfsqNyHHnporZfXvHnzpG+70zoPK1ajteOftMPpCSecYIJYNAzg0UcflQMOOCDsZtKOmU899VTCtulpzVOsO11adRSOV80TkGxS4RoULW2X1iCC008/3dQpfvnll3LPPfeYYKxwdHCj0aNHSzqhfiM0q2dT+lwrls+TdNkE3aE2unbtGvEgStHQe1WrGng9D2soIbb4+uuvLTdFMgWvrFu3znK+z+eL27oAkWjQNEfOeWWAdFwpOwsAAQAASURBVD60bcQbMFBSLi9fMEVmvPwTOwEA6oDgFQAAACDNtW7dul4eaNem86tq1aqVxJs+SNeRJLSgZOLEiWEf+n/44YcJX/Dctm3bekuMR3h8D5HqUuUc9P3334ecN2TIkJg0xP7++++2LxO1w7k5NVnd22rwCoD05czIkCZZXulWmCOtc3zidW5rQvO5nNI8xyttcn2S63ZJdUST5q2s828JYFlR4pdAZbDe1h8AAAD2mzx5suX8QYMG2fZeTqdTjjvuuJDzKyoqZOrUqZLOxo0bZzlfg3B0lFoAAFLVa6+9Zjlfr4O1CcqoZjWy+8qVK6W4uFgSXfv27S3n//DDD3Fbl1Six9IRRxwhzz33nLz66qth28zGjx8vlZWVUl+s6or0OF6yZEnM3tvv98uCBQtCzg+37YB0ZXUNikdgUiLKycmRgQMHypgxY+SJJ56QwsJCy9ePHTtW0gn1G5Fvm1gG0YWr/QWqhavB/umn2IQC/Pzzz2ZwzVC6dOkSk/dNVh988EHIeZmZmdKjRw9JleCV3NzcuK0LEClvtltOefxI2f/M3SNeRlWwSibf+ZlMvuszqaygpgkAaoPgFQAAACDNdezYURzbjegd78YRq+VrGn+4RsRY69Spk4waNcqyI6429uqoE4lMC03y8/NDzv/000/juj74J76HSHWpcA5avXq1ZYFpnz59bH/PFStWyNKlS21fLmqHc3NqsioqIngFgHJkZEgjn0e6FuRIu9wsyXI5t24YDWNplu2VtrlZku/RAJYtESxa/rshUCELi0tl2Wa/lBHAAgAAkPS04+jnn39uOdLy3nvvbet7Hn300Zbzk+EZWqzoaN7h2mEGDx4ct/UBACDepk2bJrNnz7Z8zVFHHVWnUbt33XVXyxqIzz77TBJdt27dIh5UAbWj97wvv/yyNGvWLORrtA01Vp1la2OnnXaynB/Luidtd9KQxFA6d+4cs/cGkpnVNWj9+vX1ek5JBL179zb1itnZ2ZaD+GhQWrqgfsO6vtXKr7/+avv+iMeykVq0Frxdu3Yh53/33XcSCARsf99wzxP3228/298zWWmd4qxZs0LOP+SQQyyvS4nmjz/+sJzfvHnzuK0LEA2H0yFH3XCA9L/1IHE4q4eJqrsZL/0kr140Rfyb7D/XAkCqIXgFAAAASHNZWVmWD7S/+eYbM0JLLGhn8nnz5kWcch7PESVuv/1220ZKysiI/MFXNPbYY4+Q82bMmCElJSVxXR9sw/cQ6SDZz0FWBStut9t0trGbNiqj/nBuTj2rVq0yhXpWxVoAsP3vtsJMt+xSkCMd87Ikx+3aOs/tzJDGWV5pl+eTQq/bhLVUKy6vkD+LS2Xp5jIprai/UV4BAAAQHe3kZRXC26tXL9uf9evvUquC76+//tqyU2cqGzdunOXotDvvvHPCtCkBAGA37YD4wAMPhH3dqaeeWqfl7r777pb3Mx999JEkOh3Ip3Xr1pZtkGVlZXFdp1TUpEkT+fe//21bzUy8A3hiGSIULhyxS5cuMXtv1F/9VTrJapApx9xy0NYpp2FWzOtHqgPH0p2GaVxwwQWWr/nxxx8lXVC/EVpubq4ZECuUr776SmJFa4uB2tp///1DztN79lgcT9OnT7e8jyB4ZZvXX3/dBHCGcswxx0iyWLBggeVgd1rraRUsCSSi7qd0k9NGHC3eHE/Ey/j9kz9l5MnjZf1fodu+AAAi26pEAQAAAKQtfXgcKgBl06ZN8uGHH0q/fv1sf9+JEydaPqjt2bOnJAotOmrTpo0sWrSoxvlr1qyp9bKczm0jpu8oViE31YnjH3/8cchirQ8++ECOPfbYmL0/rPE9RKpL9nNQaWlpyHn5+fkxKeqaMGGC7ctE3XBuTi2h7uOqtW/fPm7rAiC5NPC6zVQcqJDlpX7ZGNjS2dXlyJCGPo8UeN2y3l8h6wPlUvn3b9zN5ZVm8rkcUuj1SLY79O9AAAAAJJ5wnVJiVZSvyx0/fnyN8zZv3iyzZ88O20Et1Wg70ptvvmn5msGDB8dtfQAAiLeHH37YckAbtddee8luu+1Wp+UWFRWZfxMqMOOTTz4x9x+JPqr5wQcfLC+99FKN8zZs2CBvv/029wo26NOnj+n0HmowjbVr10p90QA+7XQeKjhx6tSpcvPNN4vHE3kHtVAmTZoUcp62H/fo0cP298Q2Lpd1V5hY1mClC4fLIfnNc2MSmNSoUSMzcEao7+1ll10mDkd6jzN93HHHybBhw0LOr89zb32gfiO07t27y+LFi0MGT+gANVrbZKd169ZZhloki0svvdRMiM99+yuvvBJy/pQpU+SAAw6w7f30O2EVDrjrrruaIEdsCb554403Qm4KDQs/9NBDk2ZThQte1N8P4e4jgUTU6cDWcu5rA+Xl8yfL+qWRhaes+H2tjDhhnJz6xFHScrcmtq8jAKSC9H4SAQAAAMA48MADwwakxEK45eqIkYmkQ4cOIedp41Rteb3eiDr2R6t3796WoS9PPPGEVFYyInt94XuIVJfs5yAd6SCUUAWG0QZEaEEr6hfn5tSybNmykPO0IFcnALCS63FJpwbZsktBjglbqeZ0ZEiRzy3t83zS2OcR13aBbKUVQVm6uUwWFZea4Bar8FEAAAAkjp9//tlyfl07NteWFvxHs16paMaMGbJkyZKQ87UDbf/+/eO6TgAAxIuGOjz33HOWr9Fwh+uuuy6i5R9++OEh52loyYsvvijJ0AZp5dVXX43buqQyvedq1aqVZefr+qJt0Pvvv3/I+Rs3bgw5QEg0vv/+e1m4cKFlsERBQYHt74ttwoXpaCdiJCa9dvXt2zfk/AULFlgGG6WLJk2aSE5OTkKee+sD9RuhWYVVlJeXy7vvvmv7/njnnXfMsoHa0vs1qwAgPU41+NEu4YKcjznmGNveK9mNGjUqZBia+te//pU0QSUVFRUhgzmr7b333nFbH8BuTToVyvlvDJJWe0QemrJpVYk8e+pb8v2E32xdNwBIFQSvAAAAADANL1YPtLUA4ccff7Q9dGX+/Pkh5++8887SsWPHhNo7VoEpVmECO2rQoEHIeWvWrJFYady4sWXquDZah2tsQOzwPUSqS/ZzkNUIF9roqwV7dho5cqQEg0Fbl4m649ycWqwKzyh8BVAXWS6ntM/Lkq4FOdIw0yMZ2xUK53vd0i7PJ02zvOJxbAtg8VcGZVmJXxYWl8oGf7kECWABAABIaHPmzAk5Lzs7W9q2bRuT99XOmVZ++eUXSTfjxo2znN+nTx/bR20GACARvP/++3LDDTeEDfLVALI99tgjovcYMGCAZXCBhr7UZRCa+rDffvtZBoJocF2sBhtKN3bVzMTC0UcfbTn/8ccft30QkEceecRyPp15Yy8vL888lw9l9erVcVgLROrEE0+0nD98+HBCHRL83Btv1G+Edsghh0hmZmbI+Rqmp2EEdtHAFQ1qAOo66NlRRx0Vcn5xcXHY54C15ff7ZfTo0Zbnz379+tnyXslu6dKl8tRTT4WcX1RUJCeccIIki7Fjx8qff/4ZcQApkAxyGmbJ0BePk279Iu9nUxGolHH//lCm3veFBCupUwaA7RG8AgAAAMAU0lg1+Gshzx133GHbyNzaQf3++++3fM3AgQMTbs/89ddfEXXI31HDhg1Dzgv3wDdap59+uuX8//73vyb8APHH9xDpIJnPQeHO89OnT7ftvT755BMZM2aMbctD5Dg3p5bS0tKIR8QDgJpkupzSJtcn3QpzpbHPI46/C7y10DvP45K2eVnSPMsrXue25rjyYJWsKA2YAJa1ZQSwAAAAJKKSkhJZsmRJyPnt2rWz7NwXjfbt21vO/+239BqBUDtcaKdzK4MGDYrb+gAAEA9al/Hss8/KZZddFrbDebNmzeTGG2+M+L20E51VYIVei6+//vqEHixA78tOOeUUy9fceeedsnz58ritU6patmyZLTUzsaADgFiF8f3666/y2muv2fZ+7777rnzxxReWHYs1FAmxpZ2mrY69WNdgITo6KFv37t1Dzl+8eLHcc889ab2Zy8rKZO3atQl77o036jfEMiRYg2lDmTdvnrzyyiu27QsNXUnU+i4ktlNPPdXyueqIESNk06ZNUb+PBkhanT+POOIIadSokaQ7DWTS33ta0x/K5ZdfLjk5OZIM9N7h4YcftnxN69atZffdd4/bOgGx4s50yZBhfeXgC/eOajmfP/e9vHz+FCnd6Ldt3QAg2RG8AgAAAMA488wzxeVyhdwaP/30k20p9dqxfuXKlSHnazHE4MGDa728eIywpEU4ug1C6dChQ62X1aZNG8vPMnfuXImVHj16SK9evULO37Bhg1xwwQXmz3ixe1ShZJbM30Mg1c9BWVlZlqPlvfrqq7asj466ddNNN9myLNiDc3PqsCre0OJxAIiUx+mQVjk+2bUwR5plecW53fkmx+My4SwtszMly7Vt5MGKYJWsLgvIgo2lsqYsIJVBe4JOAQAAED0NXbEKord6xh8tLWLXDtChWAXCpKJJkyaZjmahNG/eXPbff3+JN31+qZ1tdZCBs88+23TW2HvvvaVr167SrVs32WeffUzn35NPPlmuvvpq03n+66+/lkAgEPd1BQAkl/nz58u//vUv05YbLuxEgx0eeughKSgoiOo9L7nkEstw8o8++ijswDr1XT+gwStWHSc3btwo11xzTcpei+NRMzNr1ixZtWqVLTUzsaDHcLhBQLQTph3309qJN1wYhA42ZXVfD/to59lQZs6cyaZOcFdddZXlfA2KsDMsws5rUDzOvR988IHl84mOHTtKuqF+I7QzzjjDcts99thjsmLFClsGL3ziiSeiXg7SU6dOneTggw+2rJt74IEHonqPhQsXytNPP235Gn2eF62vvvpKOnfubDklMr2+3HrrreaZZSj6rHPIkCFRvU+8Qjy17kvrTtetW2f5uqFDh8YsVB2IN4cjQ/pc0UMG3neYON2RxwT88dmfMmLIOFk13/r7AwDpguAVAAAAAIZ2Jj/uuOMst4YW92hRTTReeOGFsKPInHXWWSaFv7b+7//+Ty6++GL58ccfJVa0kMjqAbCGCdTWLrvsYjl//PjxEks33HCDKcKyanjQYi79M5Y+/fRT0+AX7TGVSpL5ewikwznIqhPHN998I2+99VbURXr63bMqWkT8cW5OHVYjsGiBkRYIAUA0XA6HNM/OlF2Lck3QituxrRkuy+2UljmZ0ionU3K2C1usrKqSNWXlsqC4RFaWBqQ8gUcPBgAASBfhOmO2aNEipu/fsmVLy+dHViOQpppx48ZZzh8wYIA4trvvjpf99ttPLrvsMnnmmWdk+vTp5lmmjsaro8SWl5ebQn99zvDdd9/J22+/bZ7r6zPP6n/32WefWXaeAwCkn99++03+/e9/yzHHHGPanMLR699//vMf2XPPPW259wjX8VBHjL/rrrtiGlyi11INKzv++OPr/G8zMzPliiuusHyNbteLLrrI9nsp7aRfm30WS7r/tA123rx5MVm+fkarDrDacbEuNTOxovdbOsCMVQDPhRdeaO6pI6XHj9YnLVu2zDIE5txzz434PVA3Xbp0sawJWLNmDZs0gel17Nhjj7V8zd1332060MfyN5QeJ4888kidOuLrNVuvxbFqY9br4vDhwy3bvq2O/1RF/UZou+22m2Vdk14H9RoWzb2QHpd6P6V/AtEEP1o9zxs9erQJY46EHpv6u6CkpCTka3r37m2Ck5OFPmuMxTKvv/56GTt2rOVvLH2mGe2z12nTppnnobH6raKWLl1qAlXCDXqqv30HDRoUs/UA6suex+8sZz5/rPjyvREvY83C9SZ85fdPFtm6bgCQjAheAQAAALCVjvBjVYSgBaOXXnqp5cPWUDS0RBsow4360q5dO/MAtK7L1hEeNFn7hBNOkDfffNO2YhktILn33ntNYWwobdu2lV133bXWyywsLLQc7WfUqFFmpMRY0dEudITFcIVdOgLPhAkTbG241lEgx4wZI/379zeFJjNmzLBt2akiWb+HQDqcgw466KCwQWCRBiNpeNjgwYPNZ0fi4dycGnQUbCt33HFHyo52CSC+nBkZ0iTLK90Kc6RNjk+8zm3NcT6XU5rneKVNrk/y3C6pHkspWCWy3l8uCzaWyrISv5RVEsACAABQX8J1mmrYsGFM37+oqMhyvlUnz1Ty+++/y+zZsy07+OozxPoQ6Uit2nal7T/nnHOOHHXUUTJlyhQCWAAgTWn715w5c0yIl17PtNO5totpfUI42vFN23vDdVSvC+2Eq6OZW3nppZfk1FNPlfnz54udtHPcfffdZ0a+1459kd7raDtbr169LF+j4WdaU/LTTz+JHZ0VNSSuX79+JpSmPmkNgQ7wo+uigzzo/YZd7R1lZWVy7bXXysyZM0O+Rjt5h7uHjYe8vDzTphfuHvO0006TBQsW1Hn5y5cvN3UUGq5n5fzzzzfBAIiPffbZJ+Q87XStx8T69evZHQns5ptvtmzH1WvjsGHD5LzzzjODadhJrwe33HKLHHroofLEE0/IunXr6nR+fPHFF6VPnz5bAzZrcx2vDV0PvTZbDVik53zXdkH/6YT6jdCuu+46y5AEPeb1XmH16tV13u76/dMBrn755Zc6/1tge1prHe6ZnoaCWNVshzp3aj2g1TGqAXkaWJhMtB5R78etnpPWhd4Pn3jiiWEHmNPzidZ52kF/nxx99NEmBPHLL7+09Xmo9h3Q46k2v/H0nsPrjTyYAkhkbfdtLue/PkiK2obufxCOf1NAXj5/skx/ZhbtFgDSWnr+0gYAAAAQMhBEHyxadYjX4pGbbrpJPvzwQ9OIZRUgUk2LDrRQ5vvvv7d8ndvtNiNB6MPtSP3www9m0o6zhxxyiBx22GFywAEHmM9WF/pgV0c+efTRR8M+sNaH9VrgWxd9+/YNmeCt21gbZHUUAm2c7dy5szRu3FiysrIsG0wLCgokOzu7Vu9/5plnyqxZsywDXrQAWEfVeuqpp0yDmwYVaIp5JI1uH3/8sbz33nvy1VdfxSSBPZWkwvcQSNVzkBbbaDBRqEI8XbY2EGqxno7wUptrj464MHLkSHnjjTf+pwhHry377ruvfP311xGvM+zBuTk1hCsa1+CkI444woxustdee5mOdDpSWHVhkn4nmzRpEqe1BZAKHBkZ0tDnkaJMt6zzV8iKUr+UVGy53msYS9NsrxRVemSdPyAbyiu3Fi4UByrMlOVySqHXLVluZz1/EgAAgPSydu1ay/mNGjWK6fuHW3649UsV4cLH99tvPzNKabLSZ4xXXnmlGcVXn8nTORcAkoPf75clS5aEfZ0+59HgC5101PVVq1aZDq6LFi0yHd00iF8HDKgrHcDjgQcekAMPPFDspG3EDz/8sOmAZ3WvoQMJaIc5rWPQ9rs999yzzu+l7WHaGfL99983bYWRBGCEotdUDWBZuXKlZdCLfs5jjjnGfIadd9651svX/aoBJJMnTzbrXr2t6rKMWPv888/N1KBBA+ndu7eZ9L5J2zvqup/eeecdGT58uGXH/+qamUSh+3/q1Kkyffr0kK/RWh3tnKkBKdquG27b6Pde7021fihcgEeXLl1MOATiRwdP0Q60up9q8sUXX5hzlrYBavuftvfn5uaa2oNQtV5am9W0adMYrzm2D03Sa9Dpp59uwkxC0To+Padp7YjWnGg9XV1pTYfWFWr9iF6HwgWv1vZ8qdcEnfT3/OGHH25qS7TWo641Lnoca6iX1slYhczoMZrOA2pRvxGa3pNoOMrzzz8f8jVaO3j88cebUAW9HwpX96rhsxMnTjQBeWvWrPmfe0g97u34LiG9aM2rBlaFOtdpsKDWz2qtnz4/C1eDp+EbGoYYLsRRBzxMtmdw+ptSw5v1e9itWzdzndF7m9rUDG/v559/lpdffrlWgZ8auHnKKaeInfS31LRp08zUokULE0qt10utk69rrbKuv+7zp59+utaBNP/6179MnwIglWnoynmvD5TXLp0qC76O7NqsZUvv3v+lLP9tjRx35yHiziR+AED64cwHAAAA4B+0MUU7w+sDVivVD0C1kVAbsXfZZRfT6KyN2aWlpaYxRR9o6mt0pKba0A722shtBx21RB8266Tat29vktI1gVsfnGuQiRaa6Ppqo6qusyae6+hMWuSjD/W1+CkcLSSKZERFHUVJR6/SBgKroiWdaktHtartumiD2f33328Kgb755hvL1+o20dR0DbPRbaj7XBuvtahLt6EWJOj21oKxjRs3mkkLVXQ7/vrrr7XajkjN7yGQaucgp9NpGmCvuuoqy0ZCHfHv9ddfN+Ff1eur66pFNbquOhqarpsWPWpxZqgRcrUYQgv9CF5JDJybk58W/GjBq9U1Ua+dWkRcE72H1IZ7AIjk3qcw022mDf5yWV4akE3lW34Lup0Z0jjLK0XBKlnvL5f1gQqp/DuARUNadMp0OqTA65Yct7POoZ8AAACou3CdoPV5VCyFW34knbSTjbbbTJo0KWyn2lSgz/6OO+44efDBByn+B4AkoJ21tdN3fdh7771N6Erz5s1jsvzWrVvLiBEjTPuUtmeFou1a2mFdp6KiItlnn33MumnnOW2/0w702nFO2+6Ki4vNpO11Gjaj7XcaPGPVsT4aGp7+5JNPmk51Vp9BO+pph0OdtIakZ8+ephOjhrrpZ9D110EiNGRD60h0nXUEdW17Dxe8kSj0nnH8+PFm0oB5ba/UgHr9vPo5tWZG2yH1s2pnTq0v0HAgbZvVTplaM1Ob+04N4tHtlyj0+al+TzRkXwfACEWPj4ceesh01tTvdI8ePUznVd3/ugxtc9ZQIG3L1XAGPQ7C0X+r4SwMchNfehwfe+yxMmbMmJCv0fOQhueEC3espuczrbNB/Oy+++7mO3nJJZdYdgbX34rV5za9Hur1R2uctFZKazL0e6jnvOrrj05an1F9DdLwrVgOGKbXu1deecVMGkihbdNay6XnXl1fba/WQdX0PKEhK3ou0rAwPffqPYYGBVldv6ppcJjWQqYz6jdC05qmL7/80tQlWR2rGnyhtREaZtS9e3dzHdTvkdY96T2A1l7NmDHDPJ8JFTx48cUXm+OW4BXUlQ4yqc/CNHTL6ryvA5npMahBIzoAp55P9feGnkM1tEXPnRq698cff4R9z169eiVUYGAk9PeITrrt9J5e7+/1OtOpUyfz/dVt4/P5zO8t/R5r8Kfe2+s5obaBlxrsorWasaT36Vo/r5NeL3X9NThKP4v+ptPn49WTXrf1s+jvMA2E1FrTb7/91lzja0v3vYZNAekgKz9TTn+2v0y89ROZNT70vUA4P0z8XVbPXyenPHGU5DWpW5ArACQ7glcAAAAA/I8bb7zRFFToA+lw9CFmuE7ztaEPtPUheqxoA6VOdtIHvDrihjbY1pU20msq+Isvvij1RcM5tIjksssuM0Uz4WixjT6w1gmxl4rfQyAVzkH9+vUzDbofffRR2PXV729tvsM10eI+HblDC1yRODg3Jz8dkSXSAgEtjAOAaDXwus2kwSvLS/yyIbAlgMXpyJAin2dLOEugQtaWlUvF3wEsZZVBWVbiF7cjwwSw5Hlc4iCABQAAIGbCdTDVjn2xFG75ydLZNxrayVFDm0PRTgR9+/aVVKEduy+88EITPj1kyJB6XRcNhY9GqM5YAIDIaSdt7RirQV2xDuXV0ca1huG8886rVdDEmjVr5N133zVTotAAFe3Ap59Bw1/C0U74OqUyDcvRwAGd7LTTTjuZ+5dE7Mj77LPPmgCecIN06H3YxIkTzRTtPfzIkSNNiD/iTzv+T548uVaBFUhcOrDN448/LldeeaUJgwpHgx50ChfaWV+0o7gGAuhkJw27uvzyy21dZrKifqNmGuyjQXQ6MGC466CGMjz22GMRbX8Na9FaQw1eASKhAY6333673HzzzSbwJxS9Jui5PprzvYZ6aFhJKg1yos/gdLLzt5gO/HnnnXdGVBMfzfVSB9Cq7cCSdaWhK0888YQJeAHShcvjlAH/OVQatS+Q94d9KRanWEtLZ6+SJweNlVMeO1Ja7dHU7tUEgIRF8AoAAACA/+F0Os0IMJmZmfLWW2/FdAvpg2xtAL/00kuTak9ocIoWauiIGdGMLvD999/b3sBaF1lZWfLUU0/JfffdJ6NGjaq39cD/4nuIdJCM5yC9bg0bNkxOOukkM7pdrApCtcGP0dASD+fm5KdFAjoC2nfffVfnf6sjqwCAXXLcLunYwCU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tj8vFF1+s/Px81RQmFOeSSy7RjTfeWK4Ai90BLm+88YZOPfXUuAaUvPTSS3aSt5kEHqtAiokTJ6q2Mc/5pJNOislxNONzTBDOV199pXiaNm2ajj76aNv3UCh21x3NQlomfOb//u//Sh00VdWZOQwmMMWEFZWXCTkyr5l4n2egrObPnx+xPl7hIPvuu2+5+lXVmc9b8z3B7/f/o65hw4a6/PLLK6VfAAAAlcWMm+l16r46+43hqtM8vVxt5azfoRdOe1dfPztbIbPqEYCYIngFiLPJkyfrrbfeirhNZmamTb/+7LPPdMUVV9gU7E6dOqlVq1bq0qWLhg4dakNCzMXfs846S449Vgsszk033aSVK1eqJklOTtYhhxyiiy66yIbLmIu63333nU1ON8VckH333Xdt8r25QJ6UlFSidh999NEanTgOAACqPvNdMJKPP/7YDjRBbJjVkqLd9ONmPwAAAAAAZR8skpHoVvuMFHWpm6bG3kS59ghZSXQ61DA5UW3TvWroTbT/v1uwqEhbC/1akZuv1dsLtN0f4JoIAAAAAABVnBnPYMa9vvnmmzFt1wQ7nHLKKTEJeCgpE55gwjlivUCOGfdhwhnKG1JSFWzfvl1nnHGGfU6xZAIezj777LgE1Nx77726++67yx2ksyfT1rXXXmtDgmqLV1991T7nWB5HE7ZzwQUXxG3B0XHjxum8886LazCKWZjVLK66bNky1QTPPfecrr76avl8vpi1mZeXZ8f///DDDzFrE4iVBQsWhK1LSUlR69at43KwzRyhSBYuXKjqzPx+/Pnnn4utu+GGG0q0IDUAAEBN1Gzfhjp/4ih1PqJtudoJBYv0yQPf6rXzJytva0HM+gdA+nOpMQAxl5OTo9tuuy3iNm3bttWzzz6r5s2bR23PXGC46qqr1Lt3b40ePdpeiCzOjh07dOutt+qFF15QdZaRkaEjjjjCBql0795dHo8n7LaNGjWyZe+997Y3vrKysuwF8+effz7qxV9zrPbff3+1a9dOFWHRokUVsh8AAFA9mO+D0W6iTZo0Sccdd1yF9akmMyGHJuwm0kpJU6dOtavqAAAAAACAsjOhKs1Sk9QkJVHbCgPaVOCzYSqGIyFBmYkuW/IDQVu/3R9UkXaGz+YFgra4HAnK8LiU4XHb/wYAAAAAAFXLAw88oI8++igubW/atMkGlowfP15NmzZVPJlxpiYYYPny5XFp/6efftKFF15oQw0ijYWtyswxMiEZsQ6m2TN8xSxiefvtt8esTbPQYzzHUpv+mvHfe+21l2oyM47GLCAaD36/346NN2PkDz300Ji1+/LLL9vzUxFWr15tA4lMOI1ZdLW6MgvN3n///XFpu7CwUJdeeqnef/991a1bNy77QNWzYcMGeb1epaenqyoyc3HM+zecNm3a2LD5yhgzWp3nm6xdu1aPPPJIsXX9+/fXkUceWeF9AgAAqEq86Yk66dEjNPO1eZpyzwwF/aEyt7X4y5Uae/xbOvHhwWp5QJOY9hOorQheAeLosccei5iS3aRJE7300ks2MKQ0+vbta28GnHXWWQoGg2ETtD/77DMNHDhQ1U23bt3sSgXmokpZbzCZi7LmAq0JbjH/rlixIuLF3FtuucVe8AYAAKhovXr1irrNzTffrGbNmtkwOpSP2+22QYaRBn6ZAU8AAAAAACA2TMhK3SS3LSZkZVO+T1sK/QoV7QxZ8bqctgRDRcr2BZTt88sf2lkXCBVpS4HfljS3SxmJLiW7nJwaAAAAAACqgK+//toGiRQnKSlJhx12mL0/3759e7tAisPhUHZ2tg03mTlzpj7//HO7wGG08JVzzz1Xb7/9thITE+P0TKTHH39cP/zwQ7F1DRo0sGNxe/ToYUMV0tLSFAgEtGXLFi1evNiO1zXHwvwsEvOcTahILINFKpIJ3jDPoThmTEu/fv104IEHql69eraY822O0e+//66vvvrKHqNwC07uZkJ2zMJEpp3y+vTTT+047pLo0qWLfb127tzZhvwkJyfbscXm9WfO8fTp0/XNN9/8Y8x2KBTSNddcYxfgrKk2b95sn6N5rn9nAgl69uypQw45RJ06dVLDhg3tuG+zgOiqVatsSI85D5GCDQxzXC+77DL7Po8WRlASZvx8SUNX9ttvPzvW3IzJatmypX1/m/6Y1+6SJUvseTcLZpkFQSPZuHGjzj77bE2YMMG2Ud38+OOPYRebNe8Hc57Ne9wEDZkx+uY5mkWvzOtj1qxZNpzHnPNIzPvJBLvcfffdcXoWtVNBQYFd2M0cf/M6NJ+zTqfThp2Y92SHDh3s7y7zs4pkfqdecsklev3116ts8Ir5bCradZ+iOPEMUjJhU+Z3pfmsCde36srMyynu9735LDHjYAEAALDz7+lep+6rFvs10luXfqKtqyNfH4skZ/0OPX/quxp0eS/1+b/95WBRI6BcCF4B4pjQ++abb4atNzcUHnzwwVKHruzWp08fm67/6KOPht3G1FWn4BVz4f28886L6YTivffeW2+88YZOPvnkiOEr33//vb35dfDBB8ds3wAAACVhbtqbAShr1qwJu01+fr7+/e9/2+9/ZoUUM0AJZWe+80UKXjE3oc3NfjN4CgAAAAAAxI4JWGmZ5lWz1CRlFfhtCEv+rgkrTsefAS07/AFtKwwoLxDU7mHPuf6ALR6nQ5kel9I8LjnjtNokAAAAAACIPtG7uMmzZjGU008/Xeecc44NWynO/vvvr2HDhtlJuWbBvKeeeipiIIcJvjCT9W+88ca4nJZFixbZYIW/q1Onji6//HLbV/O8/s4EypgwgtNOO82OGTZjdt95552Ik7hNsIgZKzto0CBVJyZUYf78+cUGllx11VVhFx3afYxGjhxpJ5c/9NBDNlwjkgceeEBvvfVWufprzkdJXi8HHXSQrr76au27777F1u+11152vLYZs7N+/Xob5PL3c2zChO644w7VVCYoyDzHvxswYIA9923atCn2cSbExizAaY7vxx9/bN/DkcZGmbCWK6+80p774t5vJWWCfkxQTDT77LOPbrjhhmLHrJv9m7FcpvTv39/2y3xWmYCmSJ9VK1eu1LXXXmsXV61OzHMyz/Hv4VEZGRn2s9x8xkUKvjJzFczzNu9t8x6PtGjtu+++axeebdeuXYn69sUXX/zjZ2ZfEydOLHZ783l9zz33qKZbt26dJk+ebION5s2bJ7/fH3F7r9drA63M4r8mZMqMV4wXE1z09NNP68knn4waSFbZooWbmM+AeDJBRuGCV0zYk/lcTElJUXXy3nvv2bC14pixr/E+pgAAANVNs30b6vyJo/TuDVO14JNlZW4nFCzSx/d/q+Xfr9WIewYouQ7zjYCycpT5kQAieumllyJexBo1alS5A0ZMMna4C9a7bzZNmzZNVZ25cWFuJpnE91iGruxmkrXNqgrR0pJNojIAAEBlOOGEE6Ju4/P57M3pww8/XA8//LCWLl1aIX2ricINmNlTcQOGAAAAAABAbJjAlAZejzrXTVXHzBTVTXTLoT9DVFLcLhvO0jotWfUS3XLtEbDiC4a0Md+n5Tl52pBXqILgP1fbBQAAAAAA8WUmCq9du/YvP6tfv75eeeUVG7QQLnRlT8nJyXZSv5ms37p164jbmtCDn3/+WfGwYMGCf0wO32+//fT+++/b8RwlCYEwizDeddddNnDBPK9IxowZo+3bt6s6+fsYCrP4pAk2MecuXOjK39WrV0933nmnbrrppojbzZkzx5byuPfee7Vt27aIK0tfccUVGjduXInGkBiNGze25/iZZ575x0T4uXPnqqYyoQ57crlcuu2222yoQqQx7Hu+Vo466ig7Gd8EPkR7nZnx9+VRkvfXSSedZANeSjpm3ePx6P/+7//s6z1aYMVnn30WcTGoqsgEpfz989wEdJhQDxOSEil0Zc/zbD4vzWKpDRs2DLtdKBTSyy+/HJN+10bmPXLJJZfY4KP77rtPs2fPjhq6snvRNxOGYT7DTPCXeQ+YeRPFhSqVN8jslFNOsSFVVT10xfj76/7vzPeaeDK/F6MF7FQnJizm7rvvLrauU6dOdsFBAAAA/JM3PVEnPXaEhtzYV053+SIfFn+5Uk8OG69Vs9dzqIEyIngFiAMzKTZSIru5ADt69Ohy78fczDEXzyJ57bXXVJWZi2vmRpu5SRVPLVq00HnnnRdxGxNSU91uZgEAgJrh1FNPLdGgo92Dl8yqEEcffbSOOOIIO5jBrGARaVUY/FXbtm2jDoxasWIFhw0AAAAAgAqQ6napTXqy9q2XqmYpSfI4/ryF7XYmqJ7XozbpXjVJTlSyy/lHXahIyvYFtCo33xbz36EIK0oDAAAAAID4qVOnjg2x6NatW6kf265dOzuOtFWrVmG3KSoqCjuZN9b2339/vfjiixHDA8Ixk+HNIoSRggo2b95swzuqK/PcHn/8cZ122mk2wKQsY2TMwpORvPvuu2Xunwno+fDDD6OGc5jQn7L0/9BDD7XhINECdmoic7weeOABnXjiiaV+bFpamp544gn7HonEjIkyk/fLwoSeTJ8+PeI2ZuL/rbfeWqJApeI+q0wIVPPmzSNuZ8ItCgsLVV0dd9xx9jxkZGSU+rEmjMcslhrp+JpxbmauBUoXEHLZZZdpxIgRmjJlioLBYLkOnwlsMe+DQw45RNdee62+/fZbG4pTVub32h133KHhw4eXOzirIkX7rGnQoEFc9x+t/bJ+FlYW89lngpyKC2a6/fbbbXAXAAAAwv+93eu0rjrr9WGq0zy9XIcpe912PX/qu5r+/GyFzMAaAKXCXy5AHHz55ZfKyckJW3/MMcfE7ELM4MGD1axZs7ATbWfMmGEvutStW1dVUUknGMeCucnzwgsv2It7xTGJzz/++KO9KQIAQHmYwQXmRnFFmTVrljp27Bjzdi+66KKYhMUhutTUVBuoZ25oloYJBzFld9ieGchkVuLZs8R75YXqyAQhmmC+ZcuWhd1m/XpSfgEAAAAAqEguh0ONkxPVyOtRji+gTQU+G6aye5BJmsdlS2EwpOxCv3L8wT+CVgqCIRXkFWrzru0yPS55nKxBAgAAAABARTB/tz/yyCM2lKCsTMjJk08+aSeVFxQUhJ0obiaH9+7dW/FixliYMT8pKSllbqNHjx665ZZbdP3114fdxoQ3mPCR9PTyTaapDOZ5DRw4sFxtXHjhhREXGTIBGiYcpSz++9//Rqz/17/+pZNOOknl0bVrVxsycPnll6s2MeftqKOOKvPjzaR7E9wyatQo/fbbb8VuYxbQNCFOl156aanbN58hkRx88ME2ZKI8GjVqZPczcuTIsOEhGzdutAu4mtdadXPAAQfonnvukdP5ZwB2aZlxjLvH7BcnNzdXM2fOVL9+/crR09rBBKyYoCfzeykvLy/m7ZvftxMnTrTFvLbN+7t///7q3r27HV8Xzdy5czVhwgQblhXud3dVlp2dHTUwKp6itR+tf1XJV199pUmTJhVbd/LJJ9vfmwAAAIiueddGOn/iKE28/gst/HR5mQ9ZKBDSx/d9qxXfr9XwuwcouU4Shx8oIYJXgDiIlpRubgzFirmwOXTo0LAXiwOBgE02PuWUU1TbmQuAJlTFXOALZ/78+QSvAACASmG+r5nvIubGe1mZFQPMTSxTdmvatKn2228/O7Cnb9++at26dYx6XL2ZQVsErwAAAAAAUDUna2Ukum0xISub833aXOBTYFfISqLToYbJiapfVKQcX1DZPr/dzggWFWlbod8Wr8uhDI9bqW6nHGVYvRgAAACIp21rcyPWJ2cmyZPsLnW72eu3qyjCKpbe9EQlpkafRPl3uRt3KBjY+b27OElpHiWlJZa63e2b8xTwBcPWm2NgjkVp7cjKl79gZ5BjcdxJLqXU9Za63bxtBfLl+cPWuzxOpdZPLnW7BbmFKsgtfvK24XQ5lNaw9CEQhdt9ys8p/MfPM5vGdxIpap/jjz9evXr1Knc77du315lnnqmxY8eG3ebFF1+Ma/DKFVdcEZOFFc04YTOR/fvvvy+23kygHz9+vM466yxVJyZwpbyhJYbX69Xpp5+uu+++u9j6TZs2afny5WrTpk2p2l29erW++OKLsPXm3MYqLGXIkCF2PLJZILM2MOONzj333HK3k5ycrJtuuklnnHFG2G3eeOMNnX/++UpMLPl3CxPKZMZdRRpDbsJ8HI7yhzWbYJH/+7//09NPPx3xs8qMBTPXOqsLEwJhgnHKE7qymwmWMgE6Zh5DccxnI8Erkf3++++6+uqr9dNPP6kibNiwwYa8mGLep2bBt3322Udt27a1i8GZ95AJRjKBWYsXL7bhOeYx1Vm0YBOzmF48RWt/27Ztqg7Md5pwYWlmjGZtCykDAAAoL3MN/+THj9R3r/yij+/7RkF/+Ovy0SyaulJPDhuvEx4erJbdGnNygBIgeAWIQ7JwpIvo5uKBSQGOJXPxPlJKt5l4S/DKn2nlkYJXVq1aFdNzAwAAUBpmxSNzQ/Lrr7+O2YFbu3atLR999JH9fzMo5eijj9awYcPUokWLWnuCog2U2rx5c4X1BQAAAAAAFM+ErDRLTVKTlERtKwxoU4FP2/07B+ubMJXMRJct+YGgsgsDyvUHVaSdk0zzAyHlBwrtdukelzI9Lnmc5Z9YAQAAAMTCB7f9uZBCcfqddYBa9WhS6nY/uf9bFe4IH+Bx4En7qGP/0i/U8OWTP9hQl3D2HbKX9ju2Q6nb/ebln7VxSVbY+g6HtNJBp3Qpdbs/vr1Qq35aF7a+5QFNdMg5B5S63XmTf9Pir1aGrW+4V10NvqL0gRCLvlypXz5cErY+o3Gqjh1zaKnbXfHDWn3/5j8noZ/69JBStwWEYyZnx3JCrZmsb8Z4rl+/vtj66dOn2/v59evXj/lJ6dKlix1LESs33HCDhg8frlCo+Aky7777brUKXjGBFddcc01Mxz7fd999dtx1cebOnVvq4BWzcGa44707WCeWE+lvvPFGHXvssWHDJWoSc+5N8EIsmKCmQYMG6dNPPw0bNjB16lQdeeSRJW7TvJ8iMUEpLVu2VKycd955eu+997Ru3bqwoRk//PCDDjzwQFUXJgynWbNmMWmrbt266tOnz18WD/v7+xvRz4cJOSkNt9ttw358vvB/D5Q0SMMEq5hS3sCmzMxMVVXmeUb7jhNP0drPz89XdfDQQw+Ffa2a70LxDrABAACoicz3+t6nd1WL/RvprUs/0bY1kYPcI8let13Pn/quBl3WU33+b385HNUnIBSoDIwsA2Lsl19+UW5u+F9kffv2jXl6tUn5b9q0adj6WbNm1YqL+iXRqFGjGpEMDAAAaiYzQMGshmJunMZrxROzIpBZHWrw4MF2JZo5c+aoNsrIyIhYX1BQUGF9AQAAAAAAkZnwlLpJbnXMTFHnOqlqkOSxP9vN63KqcUqi2qZ7bV3iHivXhoqKtK3QrxW5+fp9e75yfAH7MwAAAAAAUH4jR460CxLGitfr1X/+85+w9SakY8qUKYqH888/P6ZjNfbee2/1798/bP2SJUu0ePFiVReHHXZYTIMrzII55hiFs2zZslK3+fHHH4etq1Onjg1JiaW2bduqX79+quk6deqkww8/PKZtXnDBBRHrJ0+eXOK2/H5/2BCX3eOxzjzzTMVStM+q3UFA1YU5RieffHJM24z0+bd06dKY7qsmKiwsjFhvAk2OP/54G2A1adIkG2Yzb948O5dl9uzZ9mf33HOPXby3MhZnM98PJk6cGHWMXmWKNr/G5YrvOucmKCfaZ1tV9/PPP+u1114L+xlQmgAtAAAA/FPzro10wbsnaO9BpQum/btQIKSP7/9W486apNyNOzjUQAQErwAxFi3Z16R0x0Okdnfs2GEvpEGqV69exMPABFsAAFDZzA2766+/Xi+++KL22muvuO3HrPDz5Zdf6sQTT9To0aO1YcMG1SbRVuGJdvMaAAAAAABUDhOy0jLNq6710tQy1Suv0/lHndORoDpJbrVK96pFapLS3S4l6M8JU/mBkNbnFWpZTr425vtUGAy/AjIAAAAAAIgu1kEWxtFHHy3HHqGqfzd9+vSY7zM9PV2HHHJIhR+feDyXeBk1alTM2+zYsWPYut9//71UbZmFFxcuXBjxdRWPSfTHHXecarpjjjkm5m127txZ7dq1C1v/zTff2KClkjCLTpmx8pFCg8x7PB7HJdJrasaMGaouzDFKUrI+efDbiGX75ryYvL83btzI2KwyMoFVDz30kL7++mvde++9Gjp0qDp06KDExMQ/tklOTrY/GzZsmG655RZ99tln+uijj3TJJZeoTZvyTdiMxoS8PP/887rzzjttP6qyaMEm8Q5eidZ+VQ9eMf278cYb7TjUvzPn/uabb66UfgEAANQ03vREnfz4kTr6+oPldJcvEmLpjNV64ri3tGjqipj1D6hpCF4BYmz+/PkR67t27RqXY77vvvuWq1+1RbQJtHtedAQAAKhMvXv31nvvvWdXpYh0IzoWPvnkEzvAJNLKPzVNtO99BPIBAAAAAFC1ORMS1MDrUee6qeqYmaJ6iR459ghZMQEtjVMS1TbdqwZJHiXuMWErVFSkbYV+rczN1+/b85XjC9ifAQAAAACAkmvZsmVcxsQ2bNhQPXv2DFv//ffflziQoaQGDx4cdQGXsjj88MMjTjyPtthjVZGQkKDu3bvHvF0TDBDO5s2bS9XWjz/+WOzk792GDBmieBgwYECVDxco77mPR/CKEand3NxcLViwoETtRHsfxSscxywGevDBB4etX7VqldavX6/qoEePHgr4gtq4JCtiMduUVLQFx7Zs2RKDntceTZo00aOPPqqJEyfaz7PS/s5q27atLrjgAk2ZMkX/+9//7GJtsfzsMuFGV1xxhT744AP17dtX1UEgEIhY79wj9D0eorUfrX+V7ZlnntHixYuLrbvooovUrFmzCu8TAABATf7bvPcZ++ms14cps1laudrK21qgV8+brA9u/1r+gqr9nROoDPGN4ARqoUgXeVNSUtS6deu47LdLly4R6yOluNcmGzZsiFgfj0RzAEDtc8YZZ9jVAmLt8ssv188///yPn++33352FYNY4/di5TM318yqFKb89NNPNojl008/jcuN5+3bt+viiy+2r7Nzzz1XNV1RlMlUkVbPAgAAAAAAVUuq22VL81CSsgp92pzvV/6uCVhOR4LqJLltyQ8ElV0YUK4/qCLtvDaQHwgpP1AoR0KC0j0uZXhcSnRyXQAAAAAAgGj69OkT17a//fbbsOMbVqxYoXbt2sVsf5HCE8ojKSlJBxxwgKZPn15s/bx581QdmACFeIwjqlOnTti67OzsUrX166+/hq1zuVxRF7gsz8I/ZkGl2bNnqyZq06aNDXyI1/vcBEmEY94fJTlvkcbIm8lpvXr1UryY5zBt2rSI8woaN26sqs58TsVaRkaGHfsWLijLvMebNm0a8/3WNOY1fOqpp9oxfbEKSjGhaaZcc801dsE2MybRBBhFCq8Kx4RrnHzyyTbIpbqNNzW/GyIpy/EojWghctH6V5mWLl1qg1eK06lTJzuGGgAAALHXvGsjXTBxlCZeP1ULP1terrZmvvqLls9aoxMeHKRGHerFrI9AdVd1/xIDqqG8vDytXr064sVnc/ErHkwKcSSLFi2Ky36rm/nz50ddgQEAgPIyN5DicRPJ3KgP9/PmzZvHfH+oeje4Tbnlllv0yy+/aMaMGfrhhx9sGI8ZVBQrJsTH3LQ788wzVZMVFhaW6f0GAAAAAACqLpcjQQ29ibZs9wdsAMvWQr9Cu0JWvC6nLQ1CRcrxBWwp3DV4OlRUpG2Fflu8LocyPG6lup02kAUAAAAAABQ/sTZeTJBFJEuWLIlp8Eq0/ZX3OIULXtm8ebOysrJUt25dVWVm/HM8mAUtyzquo7hJ4JH67/F4FM9zXFODV+L5Pu/QoYNdGClcuIF5n5fE4sWLI44LT01NVbx07tw5at8OP/xwVXXmPeLPDsXlPZ6TkxOT93htVK9ePd17773q169fXNo358csMGjKtm3b9M0339hi5luYz9TizpF5jPmd2aNHDx122GHq1q1b3ObHxJvb7Y5Y7/f747r/QCBQrv5V5oJ3N954o3w+3z/qzGf67bffXqVDYwAAAKo7b0aSTn7iSH03bq4+vv9bBf1l/1tu4+IsPT3ybR15zcE66JR9qu13eyCW+GsGiCETumIuJITTqlWruB1vc1HYXFzbsmVL2L5BEVPFjb333jvuh8kkdH/33XeaO3euTdg358bcPCsoKLBfTswKB2lpaTbh3CR5m4vyJrF9//33j+uNHwAAUD2Ym1P77befLYYZfLB8+XK7yotZJcXc+DT/7tixo8z7uP/+++3ghnjdtK0Kot28N9/JAAAAAABA9ZXqdtnSPJSkrEKfDWHJ37WCpNORoDpJblvyA0FlFwaU6w+qaFdAS34gpPxAoQ1dSfe4lOFxKdHpqORnBAAAAABA7QlkiNb2qlWrYrYvMz6gdevWqsznUtWDVzIyMio8eKW4Cd2RrF27tlJeqxU19riyxPPYJScn22CUFStWFFu/cuXKqG2Ycfvr1q2rtHNj2jdjv8PNH6gO4/dNuIN5L27Lzq3Q4JXSvsdrGzN3wCzQ1qRJkwrZX2Zmpo4++mhbjGAwaOc3mHkPZpyd1+tVnTp17HyVmiJaOEi0YJTaGrzy+uuv66effiq27uSTT1bXrl0rvE8AAAC1jfk7tPcZ+6lFt8Z669JPtG1N2f+eCxQG9cFtX2nJ16s07K7DlFLXG9O+AtUNwStADEW7ONqsWbO4Hu/mzZuHDV4xifhm8m2kmxQ1nZmQ/PPPP0ecxNy9e/e496NXr15h09l3pyPn5ubam0DmotQHH3xgf27OXd++fTVq1Cj7LwlyAABg93cYs4qTKUOHDrU/M981Fi1apFmzZtnAN7NyUmluVpvBANdff72mTJlSY78/bt26NWK9uVkMAAAAAACqP5cjQQ29ibZs9wdsAMvWQr9Cu0JWvC6nLQ1CRcr1B2wIS+Gu+zihoiJtK/Tb4nU5lOFxK9XttIEsAAAAAADUdm3atIlb240aNbKhDHl5ecXWb9y4MWb7MsEPTqdTlXWcNm3apKouPT09bmNeYiXScWzYsGHM9lMZ7dfU9/nu9sMFr5TkvWHGzZtx1+HEM1TJMAtt1q9fP2xfY/lZVd3e39He45EWu4U0duzYuP5uisbs2/wuNqWmMt8zIgn3HSRWoi2sVxXHL65fv14PPvhg2N+Fl19+eYX3CQAAoDZr3rWRLpg4Su9cN1W/fr68XG0tmrpCY497SyPuG6B2fVrErI9AdUPwChBDkdLSDXNhNZ6iJQibRO/27durtnrxxRcjXqQ98MADbRJzvEUKXYl2ce3jjz+2xdxouPjii3XUUUcRwAIAAIq9aW1WVDHljDPOsCuHmO8QL730kn777bcSHTFz4//555+33zlqomiDMxo0aFBhfQEAAAAAABUj1e2ypXkoSVmFPhvCkh8M2jqnI0GZiW5b8gNBG8CS6w+qaFdAS34gpPxAoQ1dSfe4lOFxKdEZu8lBAAAAqF2OufmQiPXJmUllanfwVb1VFAo/Psqbnlimdvtf0EPBQPgxT0lpnjK12+eM/RTw7fxOXhxPctlWee8+cm91PWavsPXupLINne1ydHt16N8qbL3LU7bJsR37t1Kr7k3C1jtdZfvbo3WPpmrUIfKYQqC8gQPxbj/cpOfNmzfHdD/xFK396hC8kpRUtt9LFSk7OztsXWpqalz3He/2K1O83x+Rjl1J3udmYdKyth8rZh/h3sfR+lcVVIf3d21UmaErsWIWijPzJqrqaywjI6NcwSjlFa39zMxMVTVjxowJ2+8bbrihRv8+BAAAqKq8GUk6ZeyR+vblufr4/m8VinAdP5rcTXl66T+T1PfM/TXg0p5lvt4NVGcErwAxFO3iaLwnb0ZrvzpcvI2XpUuXasKECRG3GT58uKqL5cuX67LLLtMbb7yhu+66Sy1akCIHAAAir0wyatQojRgxQpMmTbLfH7Zt2xb1kL322ms655xzquzNz/KuvhBJkybhBzYCAAAAAIDqzeVIUENvoi3b/QEbwLK10K/QrpAVr8tpS4NQkXL9ARvCUrgrWD9UVKRthX5bvC6HMjxupbqdNpAFAAAAKKnMpvGZxJzROD4T3dIapsSl3dT6kVeZL6uUuvFZHd4E4pQ1FCeSpLREW2ItMdVjCxAPXq837pPCzeTdDRs2FFtXUFAQ0/3EU7T2Y/lcarPCwsIqe46rs5SU+HwHKMmxy8/Pj/r4aO+fijg3kcJpIr0ugVgx75Xff//dLrRmAsvM72czXrBhw4Z2joFZxK2imQWDR48erQcffFCtWoUPLqzKwStmsbt4itZ+tP5VtMmTJ2vq1KnF1vXv319HHnlkhfcJAAAAOyUkJKjPv/dTi26N9b/LP9XW1eX7Ljv9+Tla9t0ajXpwkOq3qXqBgEA8EbwCVFBaelVIxY/Wv5qqqKhIt99+uwKBQMSJtUOGDFF1M2vWLA0dOlQPPfSQvWAFAAAQibmJar47HHTQQbrwwgs1f/78iNubcBZzs+yoo46qUQfW3GBes2ZNxG0aN25cYf0BAAAAAACVJ9XtsqV5KElZhT4bwpIfDNo6pyNBmYluW/IDQRvAkusPqmhXQEt+IKT8QKENXUn3uJThcSnRWfGD2AEAAAAAqGgVEWYQaR+xDDOo7FAOghliw+/3RwwKqs7hJJWpMt8fPp8v6uOjbVPZwSvVKVjJk+xWh0NaRd0Glc/MiTBzCD799FN9//33+u233+x8iXCffx06dFCvXr3Ut29fdevWTW53fM/jF198oeuvv15bt25VVVa3bt2I9Zs3b47r/qO1H61/FcmMI73zzjuLrUtOTtbNN99c4X0CAADAP7XYr5EueO8ETRozTXMnLSnXIVo7f5OeGj5eQ27sp27DO9lwF6A2IHgFiKFowSaVfXPGXPCojV5//XV9++23EbcxE4/jfRExXnbs2KHzzz9ft912m0aNGlWpfZk9e3a5Hr969eqY9QUAAChi6Nxzzz2nE044wa52EcmXX35Z44JXlixZEvZm827t2rWrsP4AAAAAAIDK53IkqKE30Zbt/oANYNla6FdoV8iK1+W0pUGoSLn+gA1hKQyFbF2oqEjbCv22eF0OZXjcSnU7bSALAAAAAAA1UUVMtjCLy4QT7Z5/VXou0dqP5XOpzVwuV9jwlXiH2+Tn56umivQ+jHf7sXhvhHZdv4un4K4Q58o4frGUnJmkg07pUtndQJS5Km+88YZee+01bdy4scSfTz///LMtzzzzjDIzM+1YQLN4mwlhiaWcnBzdf//9Gj9+vKqDpk2bVungFTPGs6ow5zVcfy+66CI1a9aswvsEAACA4iWlejTqgUHaq19LTRrzlXx54YNqo/HlBTTx+qla8vUqHXdbf3nTEznsqPEIXgFiKC8vL2K9SXONp2jt1+QL++EsWrRI9913X8Rt9t13X40cOTLufXE6nerUqZP22msvmxzdunVrpaen26Rzkyadm5trw3GysrI0b948m0D966+/luiiv9nGJAWbdo455hhVlpNOOqncr+H27dvHrD8AACDyigh33323Tj311IiHac6cOTXuMEYLizODnvbZZ58K6w8AAAAAAKhaUt0uW5qHkpRV6LMhLPm7JlA4HQnKTHTbkh8I2gCWXH9QRbsCWvIDIeUHCm3oSrrHpQyPS4nO6jPBAgAAAACAkti+fXul7iMxMTGmi99V5rGK5XOpzTweT9jglXi/Xs3435oq3seuvO/zaNtU9meVeV0C5WXCo15++WU9++yzNtykPMxcBRPeYoqZzzBixAgde+yxqlevXrnCh9599109+OCD2rJli6qL5s2bV+qCspHaN2M7U1JSVFWYuS3hjuHAgQNjdqyivb63bt0acV9mLk15XssAAAA1yf5DO6plt8b63xWfavXckgU3hjPvo6X6/ecNGnX/ILXqUXUCAoF4IHgFiKFAIBD5DeeK71vO7XZHrA93Q6GmMheyL774YhUUFEQ8ZnfccUfcViwwQSKHHXaY+vfvr0MOOcSmRJfEcccdZ/81Fx9feeUVvf766zalOlr4yvXXX6+2bduqc+fOMek/AACo2Q488ED16tVL3333XdhtVq5caW/e1qSBPjNmzIhY37JlSxuOBwAAAAAAajeXI0ENvYm2bPcHbADL1kK/QrtCVrwupy0NQkXK9QdsCEvhrkD9UFGRthX6bfG6HEr3uJXmdtpAFgAAAAAAqjuzEKAZs+hwOColzCApKalC9lMRwS6xfC61mVkIMdyxruxzXJ1VZvBKSd4b0RYtrYhzE+k5mBAAoDy++eYbjRkzxo7hi7XFixfbhdvuv/9+9enTR0OGDLHzHUzoR0lf+x988IFefPFFrVixQtWNCQ0xc0iKinZe7/+7eBzzPY9dpJCaFi1aqDowISiDBw+usP2ZBZkjLco8YMAAPfnkkxXWHwAAgKqubssMnfX6MH3xxPf6+pmfFOarb4lkr92u5097V/0v6K5Dz+8hp4sFiFAzEbwCxFC0YJN4B69Ea782Ba+YG3pXXHFF1It4o0ePVqdOnWK+fxN+csopp2jYsGFKTU0tczsmcffSSy/V2WefbS9qmnTpSMyk6Msuu0yTJk0iJR0AAJTIoEGDIgavmBuL69atU+vWrWvEETU3Lb///vuogTQAAAAAAAB7SnW7bGkeSlJWoc+GsOQHg7bO6UhQZqLblvxA0Aaw5PqDKtoV0JIfCCk/UKhNCQlK97iU4XEp0ckgFAAAAABA9WXGEphAg3guahIpzMCMrayI/VRE+7F8LrVZgwYN7PiW4kSa3B4L8W6/MlXm+6Mk741oARE5OTmKt0j74P2N8gScmVCUt956q0IWH/7qq69sMYFqXbp00QEHHGAXgjVzIurUqWMXbTPv1zVr1tjAFjPecObMmREXyK3qTHCTCV/5/fffi61fvny5/b4TjwV+ly1bFrG+Q4cOMd8nAAAAaien26lBl/VS+z4t9PZVnylnQ9kDSotCRZr6xA9aOmO1Rj4wUHWap8e0r0BVQPAKEOOLTpE4nc64Hu9o7UfrX01ikmy//PLLiNuYZGYTaBJr3bp100cffRTTNlNSUmxadY8ePXTTTTcpLy8v7LYmbOa5557TBRdcENM+AACAmsl8v4imIgYhVBTzPc3cmI7ksMMOq7D+AAAAAACA6sXlSFBDb6It2/0BG8CytdCv0K6QFa/LaUuDUJFy/QEbwlIYCtm6UFGRthX6bUlyOmwAS6rHJWccBm4DAAAAABBvK1eutJOz4yErK0u5ubkRQzZiZdWqVXGbWL37OEUSy+dSmzVu3Fhz584ttm7RokVx3fevv/6qmsq8Pyqr/ZK8N0wghMfjkc/nK7Z+6dKliqetW7faz6twGjZsGNf9o+b617/+pfnz51fK4rfmszTc52lpeL1eW6oyEy4TLnjFBMyZeRlt2rSJ+X6jndu999475vsEAABA7damZzNd+P6Jeu+mL7Xgk8hBgNGsmr1eY4eO13G3HaquQ/aKWR+BqoDgFcRlIuO0adOq/ZE9/PDDNXjw4FI9xuVyRb0QFU/BXavqlbV/NcVrr72mF198MeoNlgcffNCmMseauYAfL8ccc4wNYTGhKpFeTyZ45bTTTovrahLFefPNN8v1+NWrV2vcuHEx6w8AAIiuJDf4ww1OqI6ifV8xq3McfPDBFdYfAAAAAABQfaW6XbY0DyUpq9BnQ1jyd92vczoSlJnotiU/ELQBLLn+oIp2BbQUBEMqyPdpU4HPtmFCWExgCwAAAAAA1YUJm4hX8Eq0oIyWLVvGbF9mYrUZu9iiRQtVRihHLJ9LbdauXbuwdUuWLLGLV8ZrHPXChQtVU8UzVMaMR1q+fHnY+pK+J8124QJW4n1uorXP+xtltWHDhqjbdOzY0Y5z69Spk1q1avXHvIFt27bZ32sm3GN3iEq0eSaxts8++9i5GlU9fMh8j/n444/D1v/yyy9xCV6JFmxjjh8AAAAQa8mZSTrpsSP04/8WavKd0+UvCJS5rcLtPv3v8k/12/TfNeSGvkpMjd+caqAi1Y4UBlQoc4Fm4sSJ1f6oN2rUqNTBK263O2K93+9XPJmbAuXpX00wefJk3XHHHRG3SU5O1hNPPKG6deuqOjrssMN0zTXX6O677454I/Ctt97SWWedVaF969atW7keb84NAACoWKmpqVG3MWEkNcEXX3yhefPmRdzmqKOOqvIrbQAAAAAAgKrF5UhQQ2+iLdv9ARvAsrXQr9CukBUTqGJKg1CRcv0BZfsCKgzuDNgPFUk5voAtHodD6R6XLaZNAAAAAABqayBDtLbbt28f8/1VRvBKZmamGjRoEJf91jYdOnSIGPBhwnziMZHdjN2O53uhssXzue0OxAlnr732KvG5Dxe8sm7dOm3dulV16tRRPCxYsCBqMAYQSyZc5aSTTtLIkSPVunXrsNt1795dQ4cOtf+dnZ2tGTNmaMqUKZo6dWpcF2FLSkrS+eefrzPPPLNazF056KCDItZ/9913Ou6442K+35kzZ4atM4v0xivYDgAAAEhISFCPEzqrVfcmGn/Fp1q/cHO5Dsrsd37Vyh/XadQDA9W8ayMOMKo9R2V3AKhJoiWhRwtGKa/aHrzy1Vdf6eqrr1YotHOgarhjMHbsWO27776qzv79739rv/32i7jNe++9V2H9AQAA1ZdZ4aI2hKOZG8YPPPBA1O3+9a9/VUh/AAAAAABAzZTqdql1ulf71ktTi9QkeZ3OP+qcjgRlJrrVKs2rlqleZXpcciT8GbDiC4W0ucCnZTl5WrujwIa4FBXtDG8BAAAAAKCq+f777+PW9qxZs8LWmcVU2rZtW2H7K++43tmzZ4et79y5c1z2WxuZkIFITOBAPJgwg5ycHNVUJtAkKyurUj5DSvr+iBaoM336dMVz7HqkyWy8x2PPHNfayOPx6LzzzrMLj1155ZURQ1f+LiMjQ0cffbQee+wx+5l1++23q2vXrjE/L2bBsw8++MD2s7rMWzFzSkyYTaTPj1hfozefq2vWrIkYBhNtXhIAAABQXg3a1dG540eoz78jz1EuiayV2Xr25In66r8/KWRWHwKqMf4aA2Io2oTUvLy8uB7vHTt2RKw3N5tqKnPxffTo0fL7/WG3cTqdevjhh9WnTx/VBJdddpkNYAln8eLFWrFiRakurAIAgNpn48aNUbdp2rSpqrtHHnkk7Oo2ux1wwAExv6kMAAAAAABqJ5cjQQ29ibaYAJXN+X5tLfQrpJ2DTJJcDiW5EtWgqEi5/qByfAHlBYJ/PH67P2iLaSfd7VK6xyWPk3VVAAAAAABVx6+//qrffvtN7du3j2m72dnZ+vrrr8PWd+vWLeYTuj/66CNde+21dpxpLJkJ25EWxDGTqxEbjRo1soE8y5YtK7behAFcfvnlMQ9teP/991WTmfAg8/6Ix0JGkyZNijjmvaRjeHr16hX1HB177LGKtXXr1kUMj+nQoYPq1q0b8/3WdpE+pwsLC1UTmd8VJiwlFnMC0tPTdcIJJ9iycOFCTZw4UR9++KE2by7bKvfm9/ERRxyhM888s1oGDZnX08EHHxw2nGvDhg368ccf1aNHj5jtc/LkyRHrDznkEFU1FbUg8OOPP64nnngibP3dd9+t4cOHV0hfAAAAagOXx6mjrjtY7Q9uoQnXfq4dW/LL3FYoENKnD36npTN+14j7Bii9UWpM+wpUFEZmATFk0oDLE4xSXtHaz8zMVE00d+5cnXvuuSooKAi7jblRctddd2nQoEGqKXr37h31AmU8V5QAAFS8V155RYsWLfpHMT8Hyira9wUzMKW6B/iZgRovvPBCxG3M98VrrrmmwvoEAAAAAABqj1S3S63Tvdq3XppapnqV7HL+5ZqECVVpnpqk1mle1Ut0y7XHBKBAqEhZhX6tyM3X79sLbEBLKMYrbAIAAAAAEI/ghLL6+OOPIy7CF4/F9zZt2qSZM2dW+PExk70RO5HGCK9du1ZffvllzBc7+vzzz1XTmdCaWFu5cqXmzZsXtv7AAw8sccDSPvvsEzHg5JtvvilzqES093coFApb37dv35jvE1JiYmLYw5CfX/ZJglWRy+WyC7W+/PLLcVmIde+999b111+vr776yo5BNfMxunTpEvW9l5aWpn79+unmm2/WtGnT9OCDD1bL0JXdjjnmmIj1JpwmVsxnRqQQE3POjzzyyJjtDwAAACiJvQ5pqYveP9H+W17LvlujsceN18LPlnPwUS25KrsDQG0KXsnJyYnr/qO1H61/1XXFhLPOOitq6Iy5sHf88cerpjEX5RcsWBAxlGbUqFEV2icAAFC9hFutYc+VoqqzTz/9VNddd52KokxIMivb7L///hXWLwAAAAAAUPu4HAlq4PXYkhcIakuBT1kFfgV2XbfwOB2q5/WobpJbef6gsn1B7QgEtPuqRn4gaIvDhrU4le5xK8nJWisAAAAAgMrzv//9T2effbZSU2Ozim0gENC4ceMibnPUUUcpHp5//vmYhrr8/vvvdsxCOM2bN1fXrl1jtj/snDz/zDPPhD0U99xzjw278Xg8MTlc9957b40LeijOTz/9pNmzZ8d0DJF5v0Vy9NFHl7gth8Nht3/11VfDfq48+eSTdix5LMfsv/jii1HHIiH2Is2H2LJlS4055CZM6KGHHlLPnj3jvi+n06mDDjrIlssvv9y+Z5YtW2YDq7Kzs+Xz+ZSUlKQ6deqoZcuW9veXed/VFIceeqjS09PDzsUxIUvmuNSrV6/c+zLfC8z3g0hzQyIFSQEAAADxklo/Waf9d4i+e+UXfXzfNwr6wweNRpO3rUCvX/iRDjp5Hx1xTR95vCULVgWqgprz1y5QBUS7yBGPtOzStF/TLsIsXbpU//nPf+wFvUiuvvpqnXLKKaqJoqWhr1ixosL6AgAAymb69OlatWpVpRw+s5LPnDlzauTqSiZoxQzSuPjiiyOuhGU0adLErt4BAAAAAABQUZJdTrVI9Wrfemlqk5asNPefa6YkJCQoxeNS09REtUlPVv0kjzyOhD/qQ0VF2lYY0KrcfK3Mzde2Qr+CUUJnAQAAAACIBzPJfezYsTFr74033tCSJUvC1vfo0cNO+I7X+I2pU6fGrL377rtPhYWFYeuPO+64mO0LO3Xo0MEGB0QaUxst8KOkZs6cqQ8++KDWHPo777wz6qJHpVl004Q2hZOcnKxBgwaVqs1hw4ZFrH/zzTftfmPl0UcfVVZWVtj6Tp06ae+9947Z/vCn+vXrhz0clTUGLx5eeumlCgldKY7L5bKfp/3799fQoUPtIrAmSMjMWzDBKzUpdMUwYVyRFro1v8sff/zxcu/HBNqYz45I/vWvf5V7PwAAAEBZmbEqvU/vqnPfHqkG7eqU+0DOemO+nh75ttb/Gt959UAs/Tl6C4iRK6+80pbaqGnTplU6eMVMKK0pVq5cqTPOOCPiRWvDTLQ988wzVVPttddeEevXrFlTYX0BAABl8/333+u8887TiSeeqHPOOUeNGjWqkEO5bds2jRkzJuI2brdbRxxxhKobs+KGWaXGHNtozHN8+OGH7YocAAAAAAAAFc2RkKC6SW5bCoMhbSnwaUuBX77QztWDXI4/6/MDQWUXBpQbCP4x0cU8ZmO+T5vyfUr1uJThcdlQFwAAAAAAKsorr7xiQw/MJO3y2Lhxo5544omI2/z73/9WPN199902uCMlJaVc7Xz99df65JNPIo5VqKkLCla2s846S7NmzQpbbybPd+7cWYceemiZ9/H777/r8ssvV23yyy+/6K233tJJJ51UrnaCwaBuu+02hXZd+yrOiBEjlJqaWqp2u3Tpol69eum7774Lu9+bbrpJr732mg1ZKI8ff/zRhkRFcvbZZ5drHwivVatWEcfD/fbbb2rfvn21P4T16tWr7C7UKmZeyrhx48Iu8DZ+/HgbQtOtW7cy7+O5556zCw+HY75HHXLIISqva6+9VhMnTgxbb76z3XPPPeXeDwAAAGquJp3q67wJIzXlnm/0/Zvzy9XWpt+26plREzT4qt7qddq+NtwFqMpqVtQoUMmipeivXr06rvuP1H7dunXLfSOoqjA3LMzFrU2bNkXczkxcvvDCC1WTZWZmRkyNzsnJqdD+AACAsjE37F599VUNGDDA3uRfvnx5XA/lhg0bdOqpp2rdunURtzOrK2VkZKi6WLRoka6++modc8wxJQpdMd+j7rrrrnLdEAUAAAAAAIiVRKdDTVOS1KVuqtpnJKtOolt7DjnxupxqnJKotmleNfQm2u13MzEsub6AVm8v0PKcPBvg4o8wgQUAAAAAgFiOeTBjNbds2VLmNvLy8nTRRRfZSfPhmIn0ZlxFvBcFNOMOTEhDeRaLibaA5fDhw9WgQYMy7wPhmUCV3r17h60359a81qZMmVKmw/jrr7/aMTfxXoyzKjJjbH766adyt2GCS8IxoSj/+c9/ytT26NGjI9bPnTtX119/fcTQl5K8v81+In1GtGvXTkcddZSqmx1Z+frqvz9FLGabyrb33ntHrH/nnXcqrC+oOcxieZGCpcx73gRumZC4svj2229t8Fckl1xySZnaBgAAAOLB43XruFsP1cljj5Q3M7FcbQV8QU2+c7pePfdDbd+SF7M+AvFA8AoQ4+CVSIlb5oZMvGzfvj3iTasWLVqoJjCTg03oSrRJwqeffrquuOIK1XRmsrAJXwmnoKCgQvsDAADKPxjJrI5gbr6bVZo++ugjFRYWxvSwmoEjJ5xwgpYsWRJxu8TERF1wwQWqyszKzgsWLLCrQZhBSSYo5r333ivRACjzPcqsVGUeAwAAAAAAUJWY+40ZHrfapiera700NU9Jktfp/KPe6UhQZqJLrdK8tpj/du5xj9IfKtKWAr+W5+RrzY4CbfcH7HUUAAAAAADiZdWqVTrttNPswnqllZWVpTPPPFM///xzxO2uueaaiAvVxcpnn32mSy+91IbBlNb8+fPt+NVIATKpqalMro4zs+iRGfcSjs/ns+fguuuuK/EkejMe9+mnn7ZjbtavX/+XujZt2qg2MGOYzjrrLE2fPr3Ujw0EArrtttvswlSRmDHizZo1K1P/evTooWOPPTbiNpMmTdLFF19sx92X1g8//GA/56KFTN1yyy1y7nEtr7rwFwS06qd1EYvZprKZxWhNuE0448aN08cff1yhfULNYD4b6tSpE7Z+7dq1NhjK/Fsa33zzjR2HaT4Hw+nTp48GDhxYqnYBAACAitB5YFtd9P6JatOzbH+r72nxtFUae9xbWvL1qpj0DYgHV1xaBWqp5ORkG74S7sbR8uXL7aDGSOEs5UnQjqRDhw6q7jZs2GAvqK9ZsybidieeeKJuuOEG1RYMlAWAqicnJ8eW6i49Pd2W8jA3i/4+2CCaaCvCmGO7evXqCn8uFf373axyYEpKSooOP/xw9e/f367IU69evVK3t2PHDn3xxRd688037SCAkjCDiMx323gNxCjJOTTHwQx2McUMeNi0aZN9fZhAw8WLF2vRokXKzs4u9f5NcN0DDzygfv36lfEZAAAAAAAAVAyXw6FGyYm2mAAVE6iSVehXaFeQSqLToYbeRDVIKtJ2f1DZvoDyAn+G0u7wB20xwSwZHpfSPS55nKzPAgAAAAAon90Lxu0ZMLJ06VK7aMrll1+uUaNGyeVyRR0TYBakMYumRAu/OProo3XIIYfE5bS1atXqHwsrfvLJJ/rtt99sgIeZDF2SUI4XX3xRTz75pB3jEMmVV15ZprEfKDkTymCCekzQRyTvvPOODeIwk93NuJx99tlHTZo0sePBzdgWM07FjE+ZMWOGPvzww2LHqCQlJenmm2+2k/FrotatW2vFihV/GYN09tln24ChCy+8sETjsebNm2fPRbRwpZYtW+r8888vV3+vv/56zZo1y445D+fTTz+1Cz2Z9+IRRxwRNSTFBK0888wzeu211yIGJxinnHKKevbsWeb+o2QGDRpkf+eEW/jMBGh07drVvrc7duyohg0b2vd1pN9LJnDDjNND7WU+z8zn+WWXXRZ2G/PdYNiwYbr22ms1dOjQiIFwZryj+ex4/vnnIy4kZ153Y8aMKXf/UT6lHZMcbVy0CfArbZvmc8qESwEAAFQ16Y1S9e8Xj9X05+fo80dnKRQIlbmt7ZvzNe6sD9TnP/tp0OW95PJUv+BS1GwErwAx1rlz57DBK+Zis7n4HI9kc5OSH8nee++t6sxctP73v//9j5tbf2cuZN16662qLUKhUMTJxuaGDgCg4r388st64oknqv2hv+iiizR69OhytWFuLgwYMECxZFalMKWin0tlMd8hzSAPU0yAn/kuab5zmpvCTZs2VePGjZWWlmZ/75sbdOaGnXmMGUBggknMAJAff/xR+fn5Jd7n4MGD7XeveDEDKWL9uiip7t2729AVc+wAAAAAAACqk1S3y5bmqUnaWuDX5gKfduwKWTHXjdI8Llv8wSLl+Pw2hCWwK6AlWFRkA1tM8bocSve4leZ2yhGHBSMAAAAAADWfmSR8ySWX6Oqrr/7HYjpm8rCZaHzsscfaBWZMCIaZ0G7+djX1ZqHBmTNn6oMPPrALGkZjgjDiOSH5gAMO0IEHHqi33377Lz83/TRhGiaMY8iQIXa8gQlpMWM0zPgMM67VjMmYPn26HdOxZwhNOIcddphOPvnkuD0X/Olf//qXDdf4+3ktLqTBBACZUhY33nijDQypqUwIwQUXXGDDhfYcu/zSSy9p/PjxNhTp0EMPVadOndSgQQN5PB47bsmMpZ8zZ46mTJlig1CicbvddjxPeYMvzIT1Rx99VKeddpo9t+GYRUBNuIIZP2TGMJnPAfP+Tk1NteEqW7du1ZIlS/TNN9/oyy+/jBqoZOy333667rrrytV/lMwJJ5yg5557LmIQzty5c20pKRMCZsLDULuZzzSzWJ75fAvH/L43wStjx4613w/2339/+1lixm+asZu7v+eYz7/c3Nyo+zTBVObzB5Ur1uNZP/74Y1tKw8yFuueee2LaDwAAgFhxOB065JwD1LZXM/3vik+Vtap8i5V/8+LPWvbdGp3w4CA1aFcnZv0EyovgFSDGunTpEvEP5F9++SUuwSvRLgyaGz/Vlbl4bSb+motQkZgLV3fddZe9OVdbmAt35gZGOCVJkgcAANWHWe3JfCcyxQxAioe+ffvagQyRVmOojszgDrNSjVlpojZ9XwQAAAAAADWPMyFB9b0eW/IDQW0p8GtLge+PkBW3M0H1vB7VTXIrLxBUji+o7f6girSzPj8QUn6gUJsSpDS3S+kel7wuVhECAAAAAJSOuf/+9ddf29CRv1u3bp3++9//2lIeJoTh6aefVkZGRlxPjwlLmD17tpYuXVrswojRFkcsiQ4dOtjxGKg4ZiFHM+m9tBOfS+rMM8/UqFGjtHr16oiBItWZCQMw4TKm/F1eXp4NtokWblPSc2WCS2KhW7dueuihh2ywSqRgDmPt2rV65ZVXbCnv+9t8VpngGcRfs2bNbLiSWSAPiLWbbrrJfq6b4KVITMCUed+Xx4UXXqhjjjmmXG0AAAAAFal510a6YOIJ+vCOrzV74qJytbV+4WY9NeJ/OvqGvuo+cm/m+aBKIHgFiLGDDjooYv13332n4447LubH3aTiRrrxZAJhqiOzusH//d//2VUBIhk0aJDuu+++GjdBOBqTph6JSU9GzeYLhbQqN7+yu4Fa8loDUPOdeOKJ9sZhdR/0safmzZvr9NNP18iRI8u9Kg4AAAAAAEBVYwJTmqc61TQlUdm+gDYX+JTrC9iIFRM+m+J22RIMFSnHF7ClcNf13lCR7GNM8TgdyvC4lO52yekgtBYAAAAAUDJmsTwTsvLDDz/E/JAlJydr7Nix6tSpU9xPR2pqqp555hmddNJJ2rx5c8zbb926tZ599lm7H1Qcl8ulhx9+WHfccYdef/31mLZ90UUXafTo0fa/CwoKwm5XE4I4TLjM8uXL9fzzz8elfbOQ0ogRI2La5uDBg/XII4/o6quvtgEx8WQCY5566inVrVs3rvvBX11++eWaM2eOfv75Zw4NYsp8bj/55JM699xzI87RKa+zzjpLF198cdzaBwAAAOIlMdWj4fcMUPu+LfX+LdNUuN1X5rb8+QG9d+OXWvL1Kg29rb+SM5Ni2legtAheAWJs3333VVpamk1IL8706dNVVFQU0/Qtk7C/Zs2aiGEw5uZBdbN9+3Z7QWnBggURtzv00ENtMnl1fI7lNWPGjIj1LVu2rLC+oHKYQdEFQQIxAKA6O/DAA+0NOnMTOFRJIUdNmjSxK9MMHDhQNYEJWDnssMN09NFHq3///nI6WbEZAAAAAADUbI6EBNVJdNviC4a0pcBvQ1h2h2qbMJU6SW5b8gPBnSEs/qC9b2mYx2zK92lzvk+pbpcNYfG6HKwoBAAAAACIOjnZBIpceumlmjZtWsyOVoMGDWzoigk0qCgtWrSw4Rxm3OqqVatiHspQr169mLWJkjNjRm655RY7lvq2225TVlZWuV+bY8aM+csYG7PIZDhJSTVjwpAJMPF6vXriiSdi1qZZGOrmm2/WCSecoHgwi3q+8cYbuuyyy7Rs2bK4LXJlxlzVhICd6sa8t1544QVdd911+uSTTyq7O6hhzOedCZu688477edILJnPC/N7ySwkBwAAAFRnXY/ZSy32a6T/XfWpfp+9oVxtLfh4mVb/vEEj7x+oNgc1i1kfgdJylPoRAKJeoD/44IPD1m/YsEE//vhjTI/i5MmTI9Yfcsghqm7y8/N1zjnnRE2hNsf68ccfr7UXrL/++uuI9V27dq2wvgAAgLLp27ev3nzzTX311Ve69dZb1a9fPzuwoCI0a9ZM1157rT7++ONqHbpijle3bt109tln67///a++/fZbPfjggxowYAChKwAAAAAAoNbxOB1qkpKoLnVTtVdGig1jcejPRSG8LqcaJSeqXbpXjby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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { "height": 823, - "width": 2223 + "width": 4023 } }, "output_type": "display_data" @@ -468,7 +530,7 @@ "above_threshold = sweep_df[sweep_df[\"prob_positive\"] >= decision_threshold]\n", "tipping_point = above_threshold[\"bias_sd\"].max() if len(above_threshold) else np.nan\n", "\n", - "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "fig, axes = plt.subplots(1, 2, figsize=(20, 4))\n", "axes[0].plot(\n", " sweep_df[\"bias_sd\"],\n", " sweep_df[\"tau_mean\"],\n", @@ -513,15 +575,17 @@ }, { "cell_type": "markdown", + "id": "e15e75ee", "metadata": {}, "source": [ - "Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The question has changed from what the treatment did to which prior commitments on the unmeasured bias are required to believe that it did anything; the machinery has not.\n", + "Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The problem has changed from what the treatment did to which prior commitments on the unmeasured bias are required to believe it did anything. Its proper characterisation has not changed: it is a posterior either way.\n", "\n", "The same machinery runs on the log-odds scale. Before mapping the full topology of the bias-prior space, we confirm that the one-dimensional fragility picture is not an artefact of the Gaussian likelihood." ] }, { "cell_type": "markdown", + "id": "22d603ff", "metadata": {}, "source": [ "## The same machinery on a binary outcome\n", @@ -531,14 +595,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "97c262c4", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:07.490259Z", - "iopub.status.busy": "2026-06-01T17:16:07.490138Z", - "iopub.status.idle": "2026-06-01T17:16:07.493791Z", - "shell.execute_reply": "2026-06-01T17:16:07.493269Z" + "iopub.execute_input": "2026-06-02T17:36:06.107055Z", + "iopub.status.busy": "2026-06-02T17:36:06.106914Z", + "iopub.status.idle": "2026-06-02T17:36:06.110905Z", + "shell.execute_reply": "2026-06-02T17:36:06.110266Z" } }, "outputs": [ @@ -579,14 +643,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "e0f3171d", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:07.495232Z", - "iopub.status.busy": "2026-06-01T17:16:07.495143Z", - "iopub.status.idle": "2026-06-01T17:16:17.061666Z", - "shell.execute_reply": "2026-06-01T17:16:17.061128Z" + "iopub.execute_input": "2026-06-02T17:36:06.112390Z", + "iopub.status.busy": "2026-06-02T17:36:06.112290Z", + "iopub.status.idle": "2026-06-02T17:36:10.955670Z", + "shell.execute_reply": "2026-06-02T17:36:10.954860Z" } }, "outputs": [ @@ -598,6 +662,7 @@ "NUTS[nutpie]: [logit_p_A, tau, beta]\n", "NUTS[nutpie]: [logit_p_A, tau, beta]\n", "NUTS[nutpie]: [logit_p_A, tau, beta]\n", + "NUTS[nutpie]: [logit_p_A, tau, beta]\n", "NUTS[nutpie]: [logit_p_A, tau, beta]\n" ] }, @@ -645,20 +710,27 @@ " \n", " \n", " 2\n", + " 0.20\n", + " 0.324\n", + " 0.193\n", + " 0.955\n", + " \n", + " \n", + " 3\n", " 0.30\n", " 0.273\n", " 0.267\n", " 0.835\n", " \n", " \n", - " 3\n", + " 4\n", " 0.50\n", " 0.191\n", " 0.342\n", " 0.716\n", " \n", " \n", - " 4\n", + " 5\n", " 0.80\n", " 0.110\n", " 0.401\n", @@ -672,12 +744,13 @@ " bias_sd tau_mean tau_sd prob_positive\n", "0 0.05 0.369 0.070 1.000\n", "1 0.15 0.358 0.146 0.998\n", - "2 0.30 0.273 0.267 0.835\n", - "3 0.50 0.191 0.342 0.716\n", - "4 0.80 0.110 0.401 0.600" + "2 0.20 0.324 0.193 0.955\n", + "3 0.30 0.273 0.267 0.835\n", + "4 0.50 0.191 0.342 0.716\n", + "5 0.80 0.110 0.401 0.600" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -701,7 +774,7 @@ "\n", "\n", "tipping_records_bern = []\n", - "for bias_sd in [0.05, 0.15, 0.30, 0.50, 0.80]:\n", + "for bias_sd in [0.05, 0.15, 0.2, 0.30, 0.50, 0.80]:\n", " with bernoulli_sensitivity_model(n_A_obs, n_B_obs, N=8000, bias_prior=(0.0, bias_sd)):\n", " idata = pm.sample(\n", " draws=1000,\n", @@ -726,35 +799,35 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "69190ddc", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:17.063086Z", - "iopub.status.busy": "2026-06-01T17:16:17.062988Z", - "iopub.status.idle": "2026-06-01T17:16:17.372536Z", - "shell.execute_reply": "2026-06-01T17:16:17.372017Z" + "iopub.execute_input": "2026-06-02T17:36:10.957347Z", + "iopub.status.busy": "2026-06-02T17:36:10.957218Z", + "iopub.status.idle": "2026-06-02T17:36:11.260109Z", + "shell.execute_reply": "2026-06-02T17:36:11.259622Z" } }, "outputs": [ { "data": { - "image/png": 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ceuut0ezSCOnrrrtu0WleeOGFdlsfgEYmDtZOKhJ2xRVXtNi27777Fi3GBkD7EAcrH/non//8Z8H2+eefPwYPHtzm4wJA+xELy/fJJ58Ufe47zzzzRLUtsMACrV4nAABoBu5p6o98D4D2JRbWjpwPgPonDlZGzgdAYxEHyyffo/4pvAIAAHXgyy+/jIceeqhg+4ABA2LFFVes6jK32GKLou33339/VZeXV6VGiH3zzTfbbV0AGpU4WDtTpkyJo48+OqZPn/69tkGDBumUDlAHxMHKXXvttTF+/PiC7XvvvXd069atTccFgPYjFlampfu7r80000zRqVOnqLaZZ565aHsq5A0AAM3KPU39ku8B0D7EwtqR8wFQ/8TBysn5AGgc4mBl5HvUP4VXAACgDjz33HNFK1eutdZaVU+YX3jhhWOuueYq2D5s2DAJ8xEx++yzF92PxTr7AVAecbB2/va3v8WoUaO+9++dO3eOP/zhD9G1a9caLh2AcoiDlbvqqqsKts0222yx+eab+/IB5IhYWJkZZ5yxYFstiq6USv75uuALAAA0K/c09Uu+B0D7EAtrR84HQP0TBysn5wOgcYiDlZHvUf8UXgEAgDrw2GOPFW1fbbXVarLcYvP97LPP4vnnn49mN8sssxRtnzx5crutC0CjEgdrY+TIkXHBBRe02LbTTjvFMsssU6MlA1AJcbAyL7/8chbjCvnRj34U3bt39yUEyBGxsHrPK1OR6FJFUlqjVPHpmWeeuerLBACAvHBPU7/kewC0D7GwNuR8AOSDOFgZOR8AjUUcrIx8j/qn8AoAANSBF154oWh7rTpGL7300m1ar2bw+eefF23v0aNHu60LQKMSB6tv2rRpcdRRR8UXX3zxvbYBAwbEwQcfXIOlAtAa4mBlbr755qLtm222mS8iQM6IhZVZcsklC7Z9+eWX8fbbb0e1vf766wXb0j1m//79q75MAADIC/c09Uu+B0D7EAurT84HQH6Ig5WR8wHQWMTBysj3qH8KrwAAQB148cUXC7b17t07FlhggZosd6mlliraPnz48Gh2Y8aMKdo+44wzttu6ADQqcbD6LrvssnjmmWdabDvyyCOjT58+NVgqAK0hDlbm4YcfLtg288wzlywwCkD9EQsrs+KKKxZtf+ihh6KaPv3003j22WdbvT4AANDo3NPUL/keAO1DLKw+OR8A+SEOVkbOB0BjEQcrI9+j/im8AgAAHWzixIlFRyFdcMEFo1OnTjVZ9kILLVS0fcSIEdHsSlVgnW+++dptXQAakThYfe+++26cccYZLbatt956semmm9ZgqQC0hjhYecfv559/vmD7KqusUrP7ZwBqQyys3EYbbRQzzDBDwfYbbrghqunGG2+MqVOnFmx3jwkAQDNzT1Pf5HsA1J5YWH1yPgDyQxysjJwPgMYiDlZOvkf969rRKwAAAM0uFV2ZPn16wfb555+/Zsvu06dPzDLLLPHhhx8WXLdmd9999xVtX3zxxdttXQAakThYfccee2z2MPu7evXqFcccc0wNlghAa4mDlXn66aeLdvxedtllC7Z98MEHce+998YTTzwRI0eOjNGjR2dJPV9++WXWeX2mmWaKueeeOyt+utxyy8Vqq60Wc801V4VrCEClxMLK9evXL7bccsu4+uqrW2x/6qmnspiXCm+2Vbq3PPfccwu2zznnnFliEAAANCv3NPVNvgdA7YmF1SfnAyA/xMHKyPkAaCziYOXke9Q/hVcAAKCDlSpukjp+1dI888xTsPDKuHHj4rPPPovevXtHM3rttdfimWeeKdjeuXPnWHHFFdt1nQAajThYXWlk8/vvv7/Ftn333bfmvysAqIw4WJkRI0YUbV9iiSW+92+p0MrFF18c99xzT0ybNq3Fv0sFWNLnnXfeiWHDhsVVV12V/fsyyywTO+ywQ2y11VbRvXv3CtcWgHKIha2T7u9uvfXWmDBhQovtRx55ZBbP0rPf1kpx85BDDon333+/4DQHHnhgdOnSpdXLAACAvHNPU7/kewC0D7GwuuR8AOSLOFgZOR8AjUUcbB35HvWtc0evAAAANLt33323aPuss85a0+XPMsssRdvfe++9aFaXXHJJTJ8+vWD7yiuvHDPPPHO7rhNAoxEHqycVTDv55JNbbBs0aFAMHjy4iksDoBrEwcq8/PLLRdsXWmih//e/x4wZEwcccEDssssucddddxUsulLMs88+G0cddVRsuOGGMXTo0Ir/HoDSxMLWmX322bPiKoV88MEH8bOf/SxeeumlVs0/FSQ7+OCDsxhayHrrrRdbb711q+YPAACNwj1N/ZLvAdA+xMLqkfMBkD/iYGXkfAA0FnGwdeR71DeFVwAAoA5emBUz22yz1XT5peZfav0a1ahRo+Kaa64pOs22227bbusD0KjEweo56aST4qOPPvrev3fu3Dn+8Ic/RNeuXau4NACqQRyszKuvvlqwrUePHjFgwIDsfz/66KNZR/Bbb701qiEVcTnkkEPiV7/6VXz88cdVmScAXxELWy/Fuv32269g+xtvvBE77LBDnH322WU/4506dWrcdNNN2bxvueWWgtMtscQSceqpp7ZqvQEAoJG4p6lP8j0A2o9YWD1yPgDyRxysjJwPgMYiDraefI/6pbcFAAB0sFKdtvr27VvT5ZeafzN2Kps+fXrWQT11NChkzjnnjC222KJd1wugEYmD1XH//ffH0KFDW2zbaaedYplllqnSkgCoJnGw8gIoxe7ROnXqFHfeeWcceOCB8cUXX0S13XPPPbH99ttno+XOPffcVZ8/QDMSC9tm3333je7du8cZZ5wRX3755ffaP//88zjrrLPi/PPPj7XWWitWXHHFGDRoUMw000zZc+HJkyfHhAkTsiItTz31VDzwwAMxduzYostM95fnnXdezDjjjG1cewAAyD/3NPVHvgdA+xILq0POB0A+iYOVkfMB0FjEwbaR71GfFF4BAIA6v9ns06dPTZdfav7jx4+PZvOvf/0rHnnkkaLT7LPPPtGtW7d2WyeARiUOtt3EiRPjuOOOa7FtwIABcfDBB1dhKQDUgjhYWYeJDz/8sGB7//7949FHH61Z0ZWvpY7pu+yyS1x55ZUxxxxz1Gw5AM1CLGy7vfbaK1ZaaaU4/PDD4/XXX29xmlSA5a677so+rdW5c+fYfffds3tMz0UBAOAr7mnqj3wPgPYlFradnA+A/BIHyyfnA6DxiINtJ9+j/ii8AgAAdfDirJhevXrVdPml5j9p0qRoJiNGjIg//elPRadZeuml48c//nG7rRNAIxMH2+7000+Pd955p8W2I488suZF3ABoPXGwshe1xQqqpLbUEbzYNPPPP39ssMEGsfzyy8css8ySFWv55JNP4v3334+XX34564z+3HPPZQk/xbz33nvxq1/9KuvEUet7doBGJxZWxworrBA333xzDB06NC666KIYOXJkleYc0aVLl9h0002zQtQDBw6s2nwBAKARuKepL/I9ANqfWNh2cj4A8kscLJ+cD4DGIw5Wh3yP+qLwCgAAdLCpU6cWbe/atbY/20uNTlrLkcLrzaeffhr7779/TJ48uej+OuGEE6JTp07tum4AjUocbJtnnnkmLr/88hbb1ltvvaxzHAD1SxwsX6mioKlgSiELLbRQHHXUUbHmmmsWnCYVZEnFVEaNGhUnnnhiPPTQQ0WXN3z48Kxo53HHHVfG2gNQiFgYVS2QsvXWW2efJ554IotRqbBYa6XiYqnYyrbbbpsVKwMAAL7PPU39kO8B0DHEwraR8wGQb+Jg+eR8ADQecbB65HvUj84dvQIAANDsShU2qXXhlVLzb5bCK9OmTYvf/OY38frrrxedbr/99otBgwa123oBNDpxsG37LnUiTzGspQ5yxxxzTJuODQC1Jw6W7/PPP2/VPv7JT34SN954Y9GiK980cODAuPjii+P3v/99dO5c/DXalVdemXVsB6D1xMLqJjVdf/31sfPOO8euu+7apqIrX49OdcYZZ8QhhxwS1157bdM8JwYAgEq4p6kP8j0AOo5Y2LZ9J+cDIN/EwfLJ+QBoPOJg9cj3qB8KrwAAQJ1X+UyVK2up1PxLrV+jSCOV33vvvUWnWWONNeIXv/hFu60TQDMQB1vvvPPOi5EjR7bYtu+++8bcc8/dhrkD0B7EwfJNmTKl4v272267xfHHHx/dunWr+G933HHH+OMf/1i0+Mr06dPjtNNOq3jeAPwfsbA6hg4dGptsskkcdthh8eSTT7ZYoLO1iVIPPfRQHH744bHxxhtnRcdS/AMAAL7inqY+yPcA6DhiYevJ+QDIP3GwfHI+ABqPOFgd8j3qi8IrAADQwbp27Vq0vVpJ8oV8+eWXbVq/RnD55ZfHJZdcUnSaOeaYI+tQV2rEcwAqIw62zqhRo7IknJYMGjQoBg8e7KsIkAPiYPkq7eS98sorxxFHHBFtseWWW8buu+9edJqnnnoq6+AOQOuIhW0zYcKEOOigg+KQQw6Jt99+u6Zfw3fffTeOPfbY+NnPfhZjxoyp6bIAACAv3NN0PPkeAB1LLGwdOR8AjUEcLJ+cD4DGIw62jXyP+tT4PSgBAKDOlRp5O40q2pFVRlszMnie3HzzzXHCCScUnaZXr15x9tlnR//+/dttvQCahTjYupeQRx11VIujQKQCYX/4wx+aonAaQCMQB6u3r76pZ8+ecfLJJ0enTp2irQ488MC4995749VXXy04zVVXXRUrrrhim5cF0IzEwtYbN25c7LbbbvHyyy+XnDYV6FxzzTWzwmTzzz9/9OvXL/r27RsTJ06Mjz/+OEaPHp0VEhs2bFg8+uijRYuBP/zww7HNNtvEkCFDYuGFF27DFgAAQP65p+lY8j0AOp5YWDk5HwCNQxys3r76JjkfAPkgDraefI/6pQcGAAB0sFIdo0sVRmmrZi68cv/998ehhx5atCNB2v5zzjknll566XZdN4BmIQ5W7l//+lc89dRTLbbttNNOscwyy7T5uADQPsTB8lVyb7r11lvHvPPOG9XQo0eP+OUvfxmHHXZYwWnuvvvu7N5a4TOAyomFrZOKpey+++4li66sssoqsffee8fqq6/eYvtMM82Ufeabb75s2l//+tdZsbGLLroorrnmmoKjD3744YfZ8i+77LJYcMEFW7kVAACQf+5pOo58D4D6IBZWTs4HQOMQB8sn5wOg8YiDrSPfo7517ugVAACAZterV6+i7WnU0Vr67LPPSlaNbkSPP/547LfffvHFF18UnKZLly7xl7/8JdZYY412XTeAZiIOViaNQn7aaae12DZgwIA4+OCDq3JcAGgf4mBlBVDKteOOO0Y1bb755tGvX7+C7Z988kn873//q+oyAZqFWNg6xx57bIwYMaJogtNvfvObGDJkSMGiK4UstNBCceKJJ8Z5551XNP69//77sf/++8eUKVMqmj8AADQS9zQdQ74HQP0QCysj5wOgsYiD5ZPzAdB4xMHWke9R3xReAQCADpZGFG1LYZS2KjX/Ysn1efXss89mI5ZPnjy54DSdOnWKk046KTbaaKN2XTeAZiMOVua4444rGLuPPPLI6NOnT1WOCwDtQxys3r762iKLLBKDBg2KaurevXtssskmRad55plnqrpMgGYhFlbu5ptvjltuuaXoNKecckrstdde2TPO1lp33XWz0Xf79u1bcJqRI0fGX//611YvAwAA8s49TfuT7wFQX8TCysj5AGgs4mD19tXX5HwA5Ic4WDn5HvVP4RUAAKjzm80JEybUdPml5l/ug868eOmll2LPPfcsWXDmmGOOia233rrd1gugWYmDlT1sveeee1psW2+99WLTTTet2nEBoH2Ig5WNftSzZ8+S0y233HJRC8suu2zR9uHDh9dkuQCNTiyszLRp0+KMM84oOs3ee+8dP/rRj6IaBg4cGGeeeWbRAi6XXnppjB07tirLAwCAvHFP077kewDUH7GwfHI+ABqPOFg+OR8AjUccrIx8j3xQeAUAADpY//79i7Z/8MEHNV1+qfmXWr88GTVqVOyxxx7x8ccfF53u0EMPjZ133rnd1gugmYmD5Rk/fnyceOKJLbb16tUrKxgGQP6Ig5WZeeaZ21wgpbVKzfftt9+uyXIBGp1YWJm777473njjjYLtc801V/z617+OalpzzTVjk002Kdj+xRdfxOWXX17VZQIAQF64p2k/8j0A6pNYWB45HwCNSRysjJwPgMYiDlZGvkc+KLwCAAAdLCXD13PhlTnnnDMaQeqQMHjw4Bg3blzR6fbff//4+c9/3m7rBdDsxMHynHrqqQVj9r777htzzz13VY8LAO1DHKzMHHPMUXKaeeaZp9XHoy3zHT16dE2WC9DoxMLK3HjjjUXbf/nLX0b37t2j2tIz02Kuu+66qi8TAADywD1N+5DvAVC/xMLyyPkAaEziYGXkfAA0FnGwMvI98qFrR68AAAA0u1Kdt2o9anax+acKpL179468e+utt7KiK++//37R6fbaa6/YZ5992m29ABAHy/X8888X/B2x4YYbVu33woQJE4q2f/TRR0WX1bNnz5hlllmqsi4AzcD9YGXmnXfeeOqpp4pO07dv36iFGWaYIevIPmXKlBbbP/3005osF6DRiYWVefLJJwu2derUKTbaaKOohYEDB8bCCy8cr7zySovtY8aMiTfffDPmm2++miwfAADqlXua2pPvAVDfxMLyyPkAaEziYGXkfAA0FnGwMvI98kHhFQAAqIObzZQUP3369IIj99RK6hj24YcfFn3AmXfvvfdeVnQl/beY3XbbLX7zm9+023oB8BVxsG1SEZSNN9643b5Of/rTn7JPIRtssEGce+657bY+AHknDlamnM7cM844Y6uPRznz/uCDD1psmzx5cs2WC9DIxMLypcImheJQsvjii9e0EOYaa6xRsPBKkoqjKbwCAECzcU9TW/I9AOqfWNg2cj4A8k0crIycD4DGIg6WT75HfnTu6BUAAIBm16tXr6KVPl977bWCRVna6tVXXy3avuiii0aepZFWU9GVd955p+h0P/nJT+LII49st/UC4P+IgwA0M3GwMgsvvHDJaWp1/1xq3qmgKgCVEwsre9ZZzEILLVTTr2Cp+ZcqfA0AAI3IPU3tyPcAyAexEIBmJg5WRs4HQGMRB8sn3yM/FF4BAIA6sMQSSxRs++yzz+L111+vyXJfeOGFou1plNS8+vDDD2P33XePN954o+h022yzTfz+979vt/UC4PvEQQCamThYvqWWWqrkNBMmTIha+eSTTwq29ejRo2bLBWh0YmF5xo8fX7R95plnjlrq379/m9YPAAAalXua6pPvAZAvYiEAzUwcLJ+cD4DGIw6WR75Hfii8AgAAOXiQ+Nxzz9Vkuc8++2zR9iWXXDLy6KOPPsqKrrz66qtFp9tiiy3ipJNOMjI5QAcTBwFoZuJg+eaZZ56SncqLFUdpi0mTJsWUKVMKts8000w1WS5AMxALy5MKdBfTu3fvqKU+ffoUbf/0009runwAAKhX7mmqS74HQP6IhQA0M3GwfHI+ABqPOFge+R75ofAKAADUgVVWWaVo+6OPPlqT5T722GNFE/XLqSxdb9Lo5j/72c9i5MiRRafbaKON4k9/+lN07uy2CKCjiYMANDNxsLr766233mrT8WjtfOeYY46aLBegGYiF5enRo0fJ56K19PHHH7dp/QAAoFG5p6ke+R4A+SQWAtDMxMHq7i85HwD5Ig6WR75HfuhhCAAAdWDppZeOvn37Fmx/8MEHY/r06VVd5qhRo+Kdd94pegPctWvXyJM0quqee+4ZL774YtHp1l133Tj99NNzt30AjUocBKCZiYOVWWuttYq2P/PMM206Hq2d7/zzz1+T5QI0A7GwPMWeH389KnwtlZp/KuQNAADNyD1Ndcj3AMgvsRCAZiYOVkbOB0BjEQfLI98jP/QyBACAOtClS5dYc80149Zbb22xfcyYMfHkk0/GSiutVLVl3nzzzUXb11lnnciTSZMmxV577VWyM1zaz2eddVZ079693dYNgOLEwdJuuOGGdvkapRh59tlnF2w/+eSTY9ttt22XdQFoFuJgZdK9aqdOnQoWJ+2owitLLbVUTZYL0AzEwvLMOeecRdtfeumlqKXhw4e3af0AAKBRuadpO/keAPkmFpYm5wOgcYmDlZHzAdBYxMHyyPfIj84dvQIAAMBXfvjDHxbdFdddd13VdtW0adOKvszr2rVrbLrpprk5NJ9//nn8+te/zorTFLPKKqvEOeecEz169Gi3dQOgPOIgAM1MHCzfHHPMESuvvHLB9ldeeaVkx/DW3HPecccdRadZccUVq7pMgGYjFpY2zzzzZM9tC3nttdfivffei1p5+OGHi7YvsMACNVs2AADUO/c0rSffA6AxiIUANDNxsHxyPgAajzhYmnyP/FB4BQAA6sS6664bM844Y8H2oUOHxocffliVZaUOY2+99VbB9rXWWiv69+8feTBlypTYb7/94pFHHik63fLLLx9///vfo2fPnu22bgCUTxwEoJmJg5XZZpttirZfccUVUU0333xzjB8/vmD73HPPHYMGDarqMgGajVhYWiomvdhiixWdJj1DroVU8Pqdd94p2N6pU6dYeumla7JsAADIA/c0rSPfA6BxiIUANDNxsDJyPgAaizhYmnyP/FB4BQAA6kT37t1j++23LzrKz1lnndXm5UydOjXOPPPMotPssssukQdpW37zm9/EfffdV3S6pZZaKi644ILo3bt3u60bAJURBwFoZuJgZTbffPOYZZZZCrbfeOON8eabb0Y1pHvx8847r02jdgBQmlhYntVXX71o+0UXXRSffvpp1b9yZ5xxRtH2JZdcMvr27Vv15QIAQF64p6mcfA+AxiIWAtDMxMHKyPkAaCziYHnke+SDwisAAFBHBg8eHN26dSvYfvXVV8f//ve/Ni3jwgsvjFGjRhVsX3TRRWOdddZp0zJ+97vfZaOvFvqk9raaNm1aNp/bb7+96HRp1PGLL75Y4j9ADoiDADQzcbB8M8wwQ+y+++4F2ydNmhRHHHFETJ8+vSodzV977bWC7V26dImddtqpzcsBQCwsx6abblq0ffz48XHKKadU9ev073//O4YNG1Z0mk022aSqywQAgDzyfK988j0AGpNYCEAzEwfLJ+cDoPGIg6XJ98gHhVcAAKCOzD777LHjjjsWbP/yyy/j4IMPjrFjx7Zq/o888kicddZZRac54IADot6lznPHHHNMDB06tOh0iyyySFxyySUx00wztdu6AdB64iAAzUwcrMzOO+8cAwYMKNj++OOPx8knn9ymY3LjjTfGpZdeWnSa7bbbLuacc842LQeAr4iFpS299NLZp1ShlAsuuKAqX6v0PPn3v/990Wl69OiRxUMAAGh27mnKI98DoHGJhQA0M3GwMnI+ABqLOFiafI98UHgFAADqzP777x8zzzxzwfZ333039thjj+y/lXj44Ydj7733jqlTpxacZo011ogNN9ww6t1JJ52UdSAoZoEFFsiKrvTv37/d1guAthMHAWhm4mD5+vTpE4cffnjRaf7xj3/EscceG1988UXFx+Kqq66Kww47LBt9t5C+ffvGfvvtV/G8AShMLCztV7/6Vclp/vznP2cxcMqUKa3+ug0ZMiR+8YtflIyjO+ywQ8wyyyytXg4AADQS9zSlyfcAaGxiIQDNTBwsn5wPgMYjDpYm36P+KbwCAAB1ZsYZZ4xjjjmm6DSvvPJKbLPNNnHdddcV7QSWfPrpp3HaaafFnnvuGRMnTiw4Xe/eveO4446LenfmmWdmSf/FpMI1KVnn888/j7fffrvmn2LFbACojDgIQDMTByuz+eabx8Ybb1x0miuvvDK22mqrrBhpOUaNGhU///nPs/vyUvfbaZoBAwZUtM4AFCcWlpYKZ6+zzjolp/s6Bl577bUVFSG7//77Y9ddd40TTzyx5N+lgispeQoAAPiKe5ri5HsAND6xEIBmJg5WRs4HQGMRB0u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"text/plain": [ - "
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" ] }, "metadata": { "image/png": { "height": 823, - "width": 2223 + "width": 4023 } }, "output_type": "display_data" } ], "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "fig, axes = plt.subplots(1, 2, figsize=(20, 4))\n", "axes[0].errorbar(\n", " tipping_bern_df[\"bias_sd\"],\n", " tipping_bern_df[\"tau_mean\"],\n", @@ -781,6 +854,13 @@ " linestyle=\"--\",\n", " label=f\"Decision threshold = {decision_threshold}\",\n", ")\n", + "axes[1].axvline(\n", + " 0.2,\n", + " color=\"C3\",\n", + " linestyle=\":\",\n", + " alpha=0.7,\n", + " label=f\"Tipping point ≈ {tipping_point:.2f}\",\n", + ")\n", "axes[1].set_xlabel(r\"Prior SD on bias $\\beta$ (log-odds)\")\n", "axes[1].set_ylabel(r\"$P(\\tau > 0 \\mid \\text{data})$\")\n", "axes[1].set_title(\"Tipping point on the log-odds scale\")\n", @@ -789,6 +869,7 @@ }, { "cell_type": "markdown", + "id": "a03b1409", "metadata": {}, "source": [ "The shape of the curve confirms what the Gaussian case showed: the decision rule loses confidence as the prior on bias widens, and the tipping point is identifiable on the log-odds scale just as it was on the revenue scale. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve is the full posterior over that commitment. One dimension, two likelihoods, the same fragility picture. The complete audit — both dimensions of the bias prior, the full region of commitments the conclusion can survive — is the surface below." @@ -796,6 +877,7 @@ }, { "cell_type": "markdown", + "id": "ad6b05cf", "metadata": {}, "source": [ "### The sensitivity surface\n", @@ -805,28 +887,28 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "c46490fc", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:06.639807Z", - "iopub.status.busy": "2026-06-01T17:16:06.639714Z", - "iopub.status.idle": "2026-06-01T17:16:07.488693Z", - "shell.execute_reply": "2026-06-01T17:16:07.487821Z" + "iopub.execute_input": "2026-06-02T17:36:11.261451Z", + "iopub.status.busy": "2026-06-02T17:36:11.261355Z", + "iopub.status.idle": "2026-06-02T17:36:12.083932Z", + "shell.execute_reply": "2026-06-02T17:36:12.083206Z" } }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { "height": 1023, - "width": 1320 + "width": 3016 } }, "output_type": "display_data" @@ -845,7 +927,7 @@ " tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0])\n", " prob_grid[i, j] = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd)\n", "\n", - "fig, ax = plt.subplots(figsize=(6.5, 5))\n", + "fig, ax = plt.subplots(figsize=(15, 5))\n", "im = ax.imshow(\n", " prob_grid,\n", " origin=\"lower\",\n", @@ -872,6 +954,7 @@ }, { "cell_type": "markdown", + "id": "13d4a25c", "metadata": {}, "source": [ "The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right. It tells both parties exactly what they are disagreeing about: which region of the bias-prior plane their prior commitments occupy, and whether that region sits inside or outside the contour of defensibility. The audit has a legible geometry." @@ -879,6 +962,7 @@ }, { "cell_type": "markdown", + "id": "6bea675c", "metadata": {}, "source": [ "## Robustness as a posterior question\n", @@ -887,7 +971,7 @@ "\n", "This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. The sensitivity surface extends this from a point to a topology: the full region of bias-prior commitments the experiment can survive, with its boundary drawn as a contour. Two analysts holding the same data and disagreeing about a conclusion are not disagreeing about the data. They occupy different coordinates in the bias-prior plane, and the surface tells them precisely which commitment separates them.\n", "\n", - "The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In each: an assumption a conventional analysis leaves implicit becomes a parameter with a posterior — something to argue about rather than assume. The posterior is the constant; the question changes. We have just seen the second.\n", + "The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In each: an assumption a conventional analysis leaves implicit becomes a parameter with a posterior — something to argue about rather than assume. The problem changes; its proper characterisation is always a posterior.\n", "\n", "## Authors\n", "\n", @@ -904,14 +988,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "66eea177", "metadata": { "execution": { - "iopub.execute_input": "2026-06-01T17:16:17.374153Z", - "iopub.status.busy": "2026-06-01T17:16:17.374021Z", - "iopub.status.idle": "2026-06-01T17:16:17.395009Z", - "shell.execute_reply": "2026-06-01T17:16:17.394567Z" + "iopub.execute_input": "2026-06-02T17:36:12.085879Z", + "iopub.status.busy": "2026-06-02T17:36:12.085748Z", + "iopub.status.idle": "2026-06-02T17:36:12.100168Z", + "shell.execute_reply": "2026-06-02T17:36:12.099634Z" } }, "outputs": [ @@ -919,7 +1003,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Mon, 01 Jun 2026\n", + "Last updated: Tue, 02 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.13.13\n", diff --git a/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md b/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md index 994a725ff..21c113e6b 100644 --- a/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md +++ b/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md @@ -20,6 +20,8 @@ kernelspec: :author: Nathaniel Forde ::: +All applied inference is argument. Against every experiment you can set the contention that the working conditions were imperfect. Some aspect of the evaluation was flawed. Maybe treatment assignment introduced a subtle kind of bias, or the subjects didn't comply fully with the design. Against every experiment you can contrast the scientific ideal of perfect randomisation and clear adherence. This contrast is just due diligence and sensitivity analysis varies that contrast against the ideal limit case. + ## The randomisation gap Randomisation is an assumption you maintain by design, not a fact you assert by intention. The checkout experiment ran, the headline effect on revenue-per-visitor looks positive, and somewhere in the diagnostic notes there is a line about compliance being 73%: users on slow connections were silently routed back to the old flow by a CDN edge case, and the routing was not random in the way it touched the population. The intent-to-treat estimate now depends on a counterfactual the experiment did not produce: what those users would have done under the new flow. Without further assumptions, the data cannot say what the treatment effect would have been if randomisation had held. @@ -63,7 +65,7 @@ The structural picture is small and the notation traditional. Let $T$ be the obs ```{code-cell} ipython3 :tags: [hide-input] -fig, ax = plt.subplots(figsize=(6, 3.5)) +fig, ax = plt.subplots(figsize=(15, 3.5)) ax.set_xlim(0, 10) ax.set_ylim(0, 5) ax.axis("off") @@ -127,7 +129,55 @@ print(f"Observed difference d_hat = {d_hat:.3f} (sampling SE = {sigma_d:.3f})") print(f"True tau = {TRUE_TAU}, true bias = {TRUE_BIAS}, true sum = {TRUE_TAU + TRUE_BIAS}") ``` -The observed difference recovers the *biased* effect, as it must. $\tau$ is what we want; $\beta$ is what we cannot observe. The model below asks which decomposition the data find most plausible given a prior on $\beta$. +The observed difference recovers the *biased* effect, as it must. $\tau$ is what we want; $\beta$ is what we cannot observe. Before asking how a prior reshapes that sum, confirm the machinery is faithful: hand the model the belief an analyst would hold if they knew the confounder exactly — a prior on $\beta$ centred at the true bias — and the treatment effect should return unbiased across repeated experiments. + +```{code-cell} ipython3 +def gaussian_sensitivity_posterior( + d_hat, sigma_d, tau_prior_sd, bias_prior_sd, tau_prior_mean=0.0, bias_prior_mean=0.0 +): + """Closed-form posterior over (tau, beta) given the observed effect.""" + prior_precision = np.diag([1.0 / tau_prior_sd**2, 1.0 / bias_prior_sd**2]) + F = np.array([[1.0, 1.0]]) + data_precision = F.T @ F / sigma_d**2 + post_precision = prior_precision + data_precision + post_cov = np.linalg.inv(post_precision) + prior_mean = np.array([tau_prior_mean, bias_prior_mean]) + rhs = prior_precision @ prior_mean + F.flatten() * d_hat / sigma_d**2 + post_mean = post_cov @ rhs + return post_mean, post_cov + + +# Across many synthetic quasi-experiments, where does the posterior mean of tau land +# when the analyst's prior on the bias is centred at the true confounding strength? +recovery_rng = np.random.default_rng(RANDOM_SEED) +n_recovery = 500 +tau_posterior_means = np.empty(n_recovery) +for i in range(n_recovery): + y_A_i, y_B_i = simulate_quasi_experimental_gaussian( + N, TRUE_TAU, TRUE_BIAS, SIGMA_OBS, recovery_rng + ) + d_hat_i = y_B_i.mean() - y_A_i.mean() + post_mean_i, _ = gaussian_sensitivity_posterior( + d_hat_i, sigma_d, tau_prior_sd=1.0, bias_prior_sd=0.1, bias_prior_mean=TRUE_BIAS + ) + tau_posterior_means[i] = post_mean_i[0] + +fig, ax = plt.subplots(figsize=(20, 4)) +ax.hist(tau_posterior_means, bins=30, color="C0", alpha=0.7, edgecolor="white") +ax.axvline(TRUE_TAU, color="C2", linestyle="--", linewidth=2, label=f"true \u03c4 = {TRUE_TAU:.2f}") +ax.axvline( + tau_posterior_means.mean(), + color="C3", + linewidth=2, + label=f"mean estimate = {tau_posterior_means.mean():.3f}", +) +ax.set_xlabel(r"Posterior mean of $\tau$ across simulated experiments") +ax.set_ylabel("Frequency") +ax.set_title(r"Recovery of $\tau$ when the bias prior is correctly centred") +ax.legend(); +``` + +The sampling distribution of the posterior mean sits on the true $\tau = 0.30$: with the confounder's strength correctly specified, the estimator is unbiased across repeated experiments. The recovery is real, but read what produced it. The data fixed the sum $\tau + \beta$ through the observed difference; the correctly-centred prior on $\beta$ is what split that sum into its parts. A single difference in means cannot separate a treatment effect from a confound that imitates it — the two are not identified by the data, only by the data together with a commitment about the confounder. The prior is not a nuisance to be tuned away. It is the content of the decomposition, and that is why the honest analysis does not settle on one prior but varies it. Recentre the prior on $\beta$ at zero — where an analyst lands by default, having no oracle for the confounder's size — and the estimate moves. The moderate prior below makes the shift concrete. ```{code-cell} ipython3 def gaussian_sensitivity_model(d_hat, sigma_d, tau_prior=(0.0, 1.0), bias_prior=(0.0, 0.3)): @@ -151,36 +201,23 @@ with gaussian_sensitivity_model(d_hat, sigma_d, bias_prior=(0.0, 0.3)): az.plot_dist(idata_moderate, var_names=["tau", "beta"]) axes = plt.gcf().axes +for a in axes: + a.set_title("") axes[0].axvline(TRUE_TAU, color="C2", linestyle="--", label=f"true τ = {TRUE_TAU:.2f}") axes[0].legend() axes[1].axvline(TRUE_BIAS, color="C2", linestyle="--", label=f"true β = {TRUE_BIAS:.2f}") axes[1].legend() -plt.suptitle(r"Posterior of $\tau$ and $\beta$ under a moderate bias prior", y=1.05) +plt.suptitle(r"Posterior of $\tau$ and $\beta$ under a moderate bias prior"); ``` -The posterior of $\tau$ is centred above zero; the posterior of $\beta$ is non-zero, pulled by the prior. The data informed the sum; the prior shapes the decomposition. +Under the moderate prior, centred at zero, the posterior of $\tau$ is pulled well above the true 0.30 and the posterior of $\beta$ settles near zero: with no prior reason to expect a confounder, the treatment absorbs the sum. The true $\beta = 0.50$ falls in the prior's right tail. The data informed the sum; the prior shaped the split. ### Tipping-point analysis under a Gaussian outcome The model with a flat prior on $\tau$ and a Gaussian prior on $\beta$ is fully conjugate; the posterior is a closed-form bivariate Normal that we can compute analytically. This lets us trace the tipping point — the bias prior strength at which the posterior probability of a positive treatment effect drops below the team's decision threshold. ```{code-cell} ipython3 -def gaussian_sensitivity_posterior( - d_hat, sigma_d, tau_prior_sd, bias_prior_sd, tau_prior_mean=0.0, bias_prior_mean=0.0 -): - """Closed-form posterior over (tau, beta) given the observed effect.""" - prior_precision = np.diag([1.0 / tau_prior_sd**2, 1.0 / bias_prior_sd**2]) - F = np.array([[1.0, 1.0]]) - data_precision = F.T @ F / sigma_d**2 - post_precision = prior_precision + data_precision - post_cov = np.linalg.inv(post_precision) - prior_mean = np.array([tau_prior_mean, bias_prior_mean]) - rhs = prior_precision @ prior_mean + F.flatten() * d_hat / sigma_d**2 - post_mean = post_cov @ rhs - return post_mean, post_cov - - -# Verify analytical agrees with MCMC under the moderate prior +# Verify the closed form agrees with MCMC under the moderate prior post_mean_anly, post_cov_anly = gaussian_sensitivity_posterior( d_hat, sigma_d, tau_prior_sd=1.0, bias_prior_sd=0.3 ) @@ -214,7 +251,7 @@ decision_threshold = 0.95 above_threshold = sweep_df[sweep_df["prob_positive"] >= decision_threshold] tipping_point = above_threshold["bias_sd"].max() if len(above_threshold) else np.nan -fig, axes = plt.subplots(1, 2, figsize=(11, 4)) +fig, axes = plt.subplots(1, 2, figsize=(20, 4)) axes[0].plot( sweep_df["bias_sd"], sweep_df["tau_mean"], @@ -257,7 +294,7 @@ axes[1].set_title("Tipping point in the decision rule") axes[1].legend(); ``` -Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The question has changed from what the treatment did to which prior commitments on the unmeasured bias are required to believe that it did anything; the machinery has not. +Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The problem has changed from what the treatment did to which prior commitments on the unmeasured bias are required to believe it did anything. Its proper characterisation has not changed: it is a posterior either way. The same machinery runs on the log-odds scale. Before mapping the full topology of the bias-prior space, we confirm that the one-dimensional fragility picture is not an artefact of the Gaussian likelihood. @@ -312,7 +349,7 @@ def bernoulli_sensitivity_model( tipping_records_bern = [] -for bias_sd in [0.05, 0.15, 0.30, 0.50, 0.80]: +for bias_sd in [0.05, 0.15, 0.2, 0.30, 0.50, 0.80]: with bernoulli_sensitivity_model(n_A_obs, n_B_obs, N=8000, bias_prior=(0.0, bias_sd)): idata = pm.sample( draws=1000, @@ -336,7 +373,7 @@ tipping_bern_df.round(3) ``` ```{code-cell} ipython3 -fig, axes = plt.subplots(1, 2, figsize=(11, 4)) +fig, axes = plt.subplots(1, 2, figsize=(20, 4)) axes[0].errorbar( tipping_bern_df["bias_sd"], tipping_bern_df["tau_mean"], @@ -363,6 +400,13 @@ axes[1].axhline( linestyle="--", label=f"Decision threshold = {decision_threshold}", ) +axes[1].axvline( + 0.2, + color="C3", + linestyle=":", + alpha=0.7, + label=f"Tipping point ≈ {tipping_point:.2f}", +) axes[1].set_xlabel(r"Prior SD on bias $\beta$ (log-odds)") axes[1].set_ylabel(r"$P(\tau > 0 \mid \text{data})$") axes[1].set_title("Tipping point on the log-odds scale") @@ -390,7 +434,7 @@ for i, b_sd in enumerate(bias_sd_grid_2d): tau_mean, tau_sd = pm_a[0], np.sqrt(pc_a[0, 0]) prob_grid[i, j] = 1.0 - stats.norm.cdf(0.0, loc=tau_mean, scale=tau_sd) -fig, ax = plt.subplots(figsize=(6.5, 5)) +fig, ax = plt.subplots(figsize=(15, 5)) im = ax.imshow( prob_grid, origin="lower", @@ -425,7 +469,7 @@ The randomisation gap is not closed by a sensitivity analysis. It is mapped. The This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. The sensitivity surface extends this from a point to a topology: the full region of bias-prior commitments the experiment can survive, with its boundary drawn as a contour. Two analysts holding the same data and disagreeing about a conclusion are not disagreeing about the data. They occupy different coordinates in the bias-prior plane, and the surface tells them precisely which commitment separates them. -The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In each: an assumption a conventional analysis leaves implicit becomes a parameter with a posterior — something to argue about rather than assume. The posterior is the constant; the question changes. We have just seen the second. +The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In each: an assumption a conventional analysis leaves implicit becomes a parameter with a posterior — something to argue about rather than assume. The problem changes; its proper characterisation is always a posterior. ## Authors From 8a39e4a1f4053d4fa20627e9874b5b43babc4733 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Tue, 2 Jun 2026 22:07:00 +0100 Subject: [PATCH 6/9] update with ESS calculation Signed-off-by: Nathaniel --- .../assurance_planning_simulation.ipynb | 294 ++++++++++++++++-- .../assurance_planning_simulation.myst.md | 166 ++++++++++ 2 files changed, 431 insertions(+), 29 deletions(-) diff --git a/examples/causal_inference/assurance_planning_simulation.ipynb b/examples/causal_inference/assurance_planning_simulation.ipynb index 6b6abd667..9872bddad 100644 --- a/examples/causal_inference/assurance_planning_simulation.ipynb +++ b/examples/causal_inference/assurance_planning_simulation.ipynb @@ -1035,6 +1035,268 @@ "ax.legend();" ] }, + { + "cell_type": "markdown", + "id": "eb131822", + "metadata": {}, + "source": [ + "## The information content of a prior: effective sample size\n", + "\n", + "The three curves above differ because the three priors carry different amounts of information. The *prior effective sample size* (ESS) makes this precise: it is the number of actual experimental observations the prior is equivalent to in terms of information content. A prior with ESS of 128 is, before the first participant is enrolled, already as informative as 128 observed subjects per arm.\n", + "\n", + "For the two-arm Gaussian trial the ESS follows from equating information in the prior with information from $n$ observations. The prior $\\mathcal{N}(\\mu_0, \\sigma_0^2)$ carries information $1/\\sigma_0^2$; the effect estimate from $n$ observations carries $n / 2\\sigma_{\\text{obs}}^2$. Setting them equal:\n", + "\n", + "$$n_{\\text{ESS}} = \\frac{2\\sigma_{\\text{obs}}^2}{\\sigma_0^2}$$\n", + "\n", + "The simulation-based approach recovers the same number without the closed form. For each $N$ in a grid, draw synthetic datasets from the prior predictive and compute the posterior variance under a flat reference prior; the $N^*$ where the average posterior variance crosses the prior variance is the ESS. The machinery is the same prior-predictive loop already running in the assurance calculation — only the inner step changes. In the conjugate case the analytical posterior fills that step. In non-conjugate models — hierarchical likelihoods, logistic regression — the same loop runs with MCMC posteriors and the ESS estimate generalises without modification. Verifying that analytical and simulation estimates agree for the Gaussian case validates the approach for those more complex settings." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8518b730", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " prior_mu prior_sigma analytical_ESS\n", + "Informed 0.4 0.3 355.6\n", + "Default 0.4 0.5 128.0\n", + "Flat 0.4 1.5 14.2\n", + "Sceptical 0.1 0.5 128.0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def analytical_ess(prior, sigma_obs):\n", + " \"\"\"Prior ESS for a two-arm Gaussian trial: 2σ²_obs / σ²_prior.\"\"\"\n", + " return 2 * sigma_obs**2 / prior.sigma**2\n", + "\n", + "\n", + "pd.DataFrame(\n", + " {\n", + " \"prior_mu\": [p.mu for p in [INFORMED_PRIOR, EFFECT_PRIOR, FLAT_PRIOR, SCEPTICAL_PRIOR]],\n", + " \"prior_sigma\": [\n", + " p.sigma for p in [INFORMED_PRIOR, EFFECT_PRIOR, FLAT_PRIOR, SCEPTICAL_PRIOR]\n", + " ],\n", + " \"analytical_ESS\": [\n", + " analytical_ess(p, SIGMA_OBS)\n", + " for p in [INFORMED_PRIOR, EFFECT_PRIOR, FLAT_PRIOR, SCEPTICAL_PRIOR]\n", + " ],\n", + " },\n", + " index=[\"Informed\", \"Default\", \"Flat\", \"Sceptical\"],\n", + ").round(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1190134", + "metadata": {}, + "outputs": [], + "source": [ + "def simulation_based_ess(prior, sigma_obs, N_grid, n_sims, rng):\n", + " \"\"\"\n", + " Estimate prior ESS via simulation: find N* where the mean posterior variance\n", + " under a flat reference prior crosses the prior variance.\n", + "\n", + " Uses the same prior-predictive sampling as the assurance loop. Replace\n", + " gaussian_posterior_delta with an MCMC posterior to extend to non-conjugate models.\n", + " \"\"\"\n", + " flat_ref = EffectPrior(mu=0.0, sigma=10 * sigma_obs)\n", + " target_var = prior.sigma**2\n", + " records = []\n", + " for N in N_grid:\n", + " N = int(N)\n", + " seed = int(rng.integers(2**31 - 1))\n", + " pp = pm.sample_prior_predictive(\n", + " n_sims, model=gaussian_generative_model(N, prior), random_seed=seed\n", + " )\n", + " y_A = pp.prior[\"y_A\"].values.reshape(n_sims, N)\n", + " y_B = pp.prior[\"y_B\"].values.reshape(n_sims, N)\n", + " d_hat = y_B.mean(axis=1) - y_A.mean(axis=1)\n", + " _, post_sd = gaussian_posterior_delta(d_hat, N, sigma_obs, flat_ref)\n", + " records.append({\"N\": N, \"mean_post_var\": float(np.mean(post_sd**2))})\n", + " df = pd.DataFrame(records)\n", + " post_vars = df[\"mean_post_var\"].values\n", + " N_vals = df[\"N\"].values.astype(float)\n", + " # post_var decreases with N; interpolate to find where it crosses the prior variance\n", + " ess_sim = float(np.interp(target_var, post_vars[::-1], N_vals[::-1]))\n", + " return ess_sim, df\n", + "\n", + "\n", + "N_ESS_GRID = np.unique(np.round(np.geomspace(10, 600, 30)).astype(int))\n", + "N_ESS_SIMS = 200\n", + "ess_rng = np.random.default_rng(RANDOM_SEED + 99)\n", + "\n", + "ess_results = {}\n", + "for name, prior in [\n", + " (\"Informed\", INFORMED_PRIOR),\n", + " (\"Default\", EFFECT_PRIOR),\n", + " (\"Flat\", FLAT_PRIOR),\n", + " (\"Sceptical\", SCEPTICAL_PRIOR),\n", + "]:\n", + " ess_a = analytical_ess(prior, SIGMA_OBS)\n", + " ess_s, df_curve = simulation_based_ess(prior, SIGMA_OBS, N_ESS_GRID, N_ESS_SIMS, ess_rng)\n", + " ess_results[name] = {\n", + " \"prior\": prior,\n", + " \"ess_analytical\": ess_a,\n", + " \"ess_sim\": ess_s,\n", + " \"curve\": df_curve,\n", + " }\n", + "\n", + "pd.DataFrame(\n", + " {\n", + " \"prior_sigma\": {n: v[\"prior\"].sigma for n, v in ess_results.items()},\n", + " \"analytical_ESS\": {n: round(v[\"ess_analytical\"], 1) for n, v in ess_results.items()},\n", + " \"simulation_ESS\": {n: round(v[\"ess_sim\"], 1) for n, v in ess_results.items()},\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "839ea69e", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(20, 5))\n", + "\n", + "# Left: ESS crossing picture for the default prior\n", + "res = ess_results[\"Default\"]\n", + "curve = res[\"curve\"]\n", + "prior_d = res[\"prior\"]\n", + "ess_a_d = res[\"ess_analytical\"]\n", + "ess_s_d = res[\"ess_sim\"]\n", + "\n", + "axes[0].plot(\n", + " curve[\"N\"],\n", + " curve[\"mean_post_var\"],\n", + " marker=\"o\",\n", + " color=\"C0\",\n", + " markersize=4,\n", + " label=\"Mean posterior variance (flat prior)\",\n", + ")\n", + "axes[0].axhline(\n", + " prior_d.sigma**2,\n", + " color=\"C3\",\n", + " linestyle=\"--\",\n", + " label=rf\"Prior variance $\\sigma_0^2$ = {prior_d.sigma**2:.2f}\",\n", + ")\n", + "axes[0].axvline(\n", + " ess_a_d,\n", + " color=\"C2\",\n", + " linestyle=\":\",\n", + " linewidth=2,\n", + " label=f\"Analytical ESS = {ess_a_d:.0f}\",\n", + ")\n", + "axes[0].axvline(\n", + " ess_s_d,\n", + " color=\"C3\",\n", + " linestyle=\":\",\n", + " linewidth=2,\n", + " alpha=0.7,\n", + " label=f\"Simulation ESS ≈ {ess_s_d:.0f}\",\n", + ")\n", + "axes[0].set_xlabel(\"N per arm\")\n", + "axes[0].set_ylabel(\"Mean posterior variance under flat prior\")\n", + "axes[0].set_title(r\"ESS crossing: default prior ($\\sigma_0 = 0.5$)\")\n", + "axes[0].legend()\n", + "\n", + "# Right: assurance curves with ESS positions annotated\n", + "for name in [\"Informed\", \"Flat\", \"Sceptical\"]:\n", + " res = ess_results[name]\n", + " df = prior_comparison[name]\n", + " ess_a = res[\"ess_analytical\"]\n", + " axes[1].plot(\n", + " df[\"N\"],\n", + " df[\"assurance\"],\n", + " marker=\"o\",\n", + " linewidth=2,\n", + " label=f\"{name} (ESS ≈ {ess_a:.0f})\",\n", + " )\n", + " axes[1].axvline(ess_a, linestyle=\":\", alpha=0.35)\n", + "\n", + "axes[1].set_xscale(\"log\")\n", + "axes[1].xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{int(x):,}\"))\n", + "axes[1].set_xlabel(\"Sample size per arm\")\n", + "axes[1].set_ylabel(\"Assurance\")\n", + "axes[1].set_title(\"Assurance curves with ESS marked\")\n", + "axes[1].legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "95945bca", + "metadata": {}, + "source": [ + "The left panel is a calibration check. The simulation curve — posterior variance under a flat prior as a function of N — crosses the prior variance at the point the analytical formula predicts. The two estimates agree to within interpolation noise. For non-conjugate models where no closed form for the posterior variance exists, the same loop with MCMC posteriors in place of `gaussian_posterior_delta` gives the correct ESS. The conjugate case validates the simulation approach before the closed form disappears.\n", + "\n", + "The right panel reads the ESS against the assurance curves. The dotted vertical lines mark each prior's ESS on the sample-size axis. An ESS of 356 (Informed) means the prior carries more information than 350 subjects per arm before the first participant is enrolled; the curve climbs steeply early because the prior is already doing most of the work. An ESS of 14 (Flat) means the prior contributes almost nothing, and every observation earns its full weight. The ESS converts the abstract question \"how informative is this prior?\" into a number on the same axis as the assurance curve: experiments whose planned N falls well below the ESS are prior-dominated; those well above it are data-dominated.\n", + "\n", + "This is the quantitative form of the trade-off the sensitivity analysis in {ref}`sensitivity_confounding` interrogates from the other side. There, the question is not how much information the prior commits but what happens when that commitment meets an identification gap — the tipping point at which the conclusion turns is the price the prior's ESS is staking." + ] + }, { "cell_type": "markdown", "id": "fda04dc5", @@ -1065,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "b30e957b", "metadata": { "execution": { @@ -1075,33 +1337,7 @@ "shell.execute_reply": "2026-06-01T17:16:23.604855Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Last updated: Tue, 02 Jun 2026\n", - "\n", - "Python implementation: CPython\n", - "Python version : 3.13.13\n", - "IPython version : 9.14.0\n", - "\n", - "pytensor: 3.0.3\n", - "xarray : 2026.4.0\n", - "\n", - "arviz : 1.1.0\n", - "matplotlib: 3.10.9\n", - "numpy : 2.3.5\n", - "pandas : 2.3.3\n", - "pymc : 6.0.1\n", - "scipy : 1.17.1\n", - "seaborn : 0.13.2\n", - "\n", - "Watermark: 2.6.0\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext watermark\n", "%watermark -n -u -v -iv -w -p pytensor,xarray" @@ -1141,4 +1377,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/examples/causal_inference/assurance_planning_simulation.myst.md b/examples/causal_inference/assurance_planning_simulation.myst.md index c3e67000c..cf0623638 100644 --- a/examples/causal_inference/assurance_planning_simulation.myst.md +++ b/examples/causal_inference/assurance_planning_simulation.myst.md @@ -516,6 +516,172 @@ ax.set_title("Assurance under three prior commitments") ax.legend(); ``` +## The information content of a prior: effective sample size + +The three curves above differ because the three priors carry different amounts of information. The *prior effective sample size* (ESS) makes this precise: it is the number of actual experimental observations the prior is equivalent to in terms of information content. A prior with ESS of 128 is, before the first participant is enrolled, already as informative as 128 observed subjects per arm. + +For the two-arm Gaussian trial the ESS follows from equating information in the prior with information from $n$ observations. The prior $\mathcal{N}(\mu_0, \sigma_0^2)$ carries information $1/\sigma_0^2$; the effect estimate from $n$ observations carries $n / 2\sigma_{\text{obs}}^2$. Setting them equal: + +$$n_{\text{ESS}} = \frac{2\sigma_{\text{obs}}^2}{\sigma_0^2}$$ + +The simulation-based approach recovers the same number without the closed form. For each $N$ in a grid, draw synthetic datasets from the prior predictive and compute the posterior variance under a flat reference prior; the $N^*$ where the average posterior variance crosses the prior variance is the ESS. The machinery is the same prior-predictive loop already running in the assurance calculation — only the inner step changes. In the conjugate case the analytical posterior fills that step. In non-conjugate models — hierarchical likelihoods, logistic regression — the same loop runs with MCMC posteriors and the ESS estimate generalises without modification. Verifying that analytical and simulation estimates agree for the Gaussian case validates the approach for those more complex settings. + +```{code-cell} ipython3 +def analytical_ess(prior, sigma_obs): + """Prior ESS for a two-arm Gaussian trial: 2σ²_obs / σ²_prior.""" + return 2 * sigma_obs**2 / prior.sigma**2 + + +pd.DataFrame( + { + "prior_mu": [p.mu for p in [INFORMED_PRIOR, EFFECT_PRIOR, FLAT_PRIOR, SCEPTICAL_PRIOR]], + "prior_sigma": [ + p.sigma for p in [INFORMED_PRIOR, EFFECT_PRIOR, FLAT_PRIOR, SCEPTICAL_PRIOR] + ], + "analytical_ESS": [ + analytical_ess(p, SIGMA_OBS) + for p in [INFORMED_PRIOR, EFFECT_PRIOR, FLAT_PRIOR, SCEPTICAL_PRIOR] + ], + }, + index=["Informed", "Default", "Flat", "Sceptical"], +).round(1) +``` + +```{code-cell} ipython3 +def simulation_based_ess(prior, sigma_obs, N_grid, n_sims, rng): + """ + Estimate prior ESS via simulation: find N* where the mean posterior variance + under a flat reference prior crosses the prior variance. + + Uses the same prior-predictive sampling as the assurance loop. Replace + gaussian_posterior_delta with an MCMC posterior to extend to non-conjugate models. + """ + flat_ref = EffectPrior(mu=0.0, sigma=10 * sigma_obs) + target_var = prior.sigma**2 + records = [] + for N in N_grid: + N = int(N) + seed = int(rng.integers(2**31 - 1)) + pp = pm.sample_prior_predictive( + n_sims, model=gaussian_generative_model(N, prior), random_seed=seed + ) + y_A = pp.prior["y_A"].values.reshape(n_sims, N) + y_B = pp.prior["y_B"].values.reshape(n_sims, N) + d_hat = y_B.mean(axis=1) - y_A.mean(axis=1) + _, post_sd = gaussian_posterior_delta(d_hat, N, sigma_obs, flat_ref) + records.append({"N": N, "mean_post_var": float(np.mean(post_sd**2))}) + df = pd.DataFrame(records) + post_vars = df["mean_post_var"].values + N_vals = df["N"].values.astype(float) + # post_var decreases with N; interpolate to find where it crosses the prior variance + ess_sim = float(np.interp(target_var, post_vars[::-1], N_vals[::-1])) + return ess_sim, df + + +N_ESS_GRID = np.unique(np.round(np.geomspace(10, 600, 30)).astype(int)) +N_ESS_SIMS = 200 +ess_rng = np.random.default_rng(RANDOM_SEED + 99) + +ess_results = {} +for name, prior in [ + ("Informed", INFORMED_PRIOR), + ("Default", EFFECT_PRIOR), + ("Flat", FLAT_PRIOR), + ("Sceptical", SCEPTICAL_PRIOR), +]: + ess_a = analytical_ess(prior, SIGMA_OBS) + ess_s, df_curve = simulation_based_ess(prior, SIGMA_OBS, N_ESS_GRID, N_ESS_SIMS, ess_rng) + ess_results[name] = { + "prior": prior, + "ess_analytical": ess_a, + "ess_sim": ess_s, + "curve": df_curve, + } + +pd.DataFrame( + { + "prior_sigma": {n: v["prior"].sigma for n, v in ess_results.items()}, + "analytical_ESS": {n: round(v["ess_analytical"], 1) for n, v in ess_results.items()}, + "simulation_ESS": {n: round(v["ess_sim"], 1) for n, v in ess_results.items()}, + } +) +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 2, figsize=(20, 5)) + +# Left: ESS crossing picture for the default prior +res = ess_results["Default"] +curve = res["curve"] +prior_d = res["prior"] +ess_a_d = res["ess_analytical"] +ess_s_d = res["ess_sim"] + +axes[0].plot( + curve["N"], + curve["mean_post_var"], + marker="o", + color="C0", + markersize=4, + label="Mean posterior variance (flat prior)", +) +axes[0].axhline( + prior_d.sigma**2, + color="C3", + linestyle="--", + label=rf"Prior variance $\sigma_0^2$ = {prior_d.sigma**2:.2f}", +) +axes[0].axvline( + ess_a_d, + color="C2", + linestyle=":", + linewidth=2, + label=f"Analytical ESS = {ess_a_d:.0f}", +) +axes[0].axvline( + ess_s_d, + color="C3", + linestyle=":", + linewidth=2, + alpha=0.7, + label=f"Simulation ESS ≈ {ess_s_d:.0f}", +) +axes[0].set_xlabel("N per arm") +axes[0].set_ylabel("Mean posterior variance under flat prior") +axes[0].set_title(r"ESS crossing: default prior ($\sigma_0 = 0.5$)") +axes[0].legend() + +# Right: assurance curves with ESS positions annotated +for name in ["Informed", "Flat", "Sceptical"]: + res = ess_results[name] + df = prior_comparison[name] + ess_a = res["ess_analytical"] + axes[1].plot( + df["N"], + df["assurance"], + marker="o", + linewidth=2, + label=f"{name} (ESS ≈ {ess_a:.0f})", + ) + axes[1].axvline(ess_a, linestyle=":", alpha=0.35) + +axes[1].set_xscale("log") +axes[1].xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{int(x):,}")) +axes[1].set_xlabel("Sample size per arm") +axes[1].set_ylabel("Assurance") +axes[1].set_title("Assurance curves with ESS marked") +axes[1].legend() +plt.tight_layout() +``` + +The left panel is a calibration check. The simulation curve — posterior variance under a flat prior as a function of N — crosses the prior variance at the point the analytical formula predicts. The two estimates agree to within interpolation noise. For non-conjugate models where no closed form for the posterior variance exists, the same loop with MCMC posteriors in place of `gaussian_posterior_delta` gives the correct ESS. The conjugate case validates the simulation approach before the closed form disappears. + +The right panel reads the ESS against the assurance curves. The dotted vertical lines mark each prior's ESS on the sample-size axis. An ESS of 356 (Informed) means the prior carries more information than 350 subjects per arm before the first participant is enrolled; the curve climbs steeply early because the prior is already doing most of the work. An ESS of 14 (Flat) means the prior contributes almost nothing, and every observation earns its full weight. The ESS converts the abstract question "how informative is this prior?" into a number on the same axis as the assurance curve: experiments whose planned N falls well below the ESS are prior-dominated; those well above it are data-dominated. + +This is the quantitative form of the trade-off the sensitivity analysis in {ref}`sensitivity_confounding` interrogates from the other side. There, the question is not how much information the prior commits but what happens when that commitment meets an identification gap — the tipping point at which the conclusion turns is the price the prior's ESS is staking. + ++++ + The three curves disagree most where they should: at small sample sizes where the data has not yet overwhelmed the prior. The informed prior reaches high assurance fastest because it is willing to commit to a narrow effect range; the sceptical prior assumes a smaller effect and so needs more data to detect it; the flat prior carries enough probability mass over zero that even moderate datasets do not push the posterior reliably above the decision threshold. The point is not to advocate for a specific prior. It is to make the dependence of the assurance number on the prior commitment visible, so that the prior can be the subject of negotiation rather than a hidden choice. ## Planning as a posterior over future posteriors From 69d6abcc1078c77f04dc918c763a5d86c823fbc0 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Fri, 12 Jun 2026 21:09:20 +0100 Subject: [PATCH 7/9] adding triptych image and tidying --- .../assurance_planning_simulation.ipynb | 499 +++++++++++++++-- .../assurance_planning_simulation.myst.md | 127 ++++- .../experimentation_triptych.jpeg | Bin 0 -> 221365 bytes .../multiple_experiments_meta_analysis.ipynb | 518 ++++++++++++++++-- ...multiple_experiments_meta_analysis.myst.md | 195 +++++-- .../sensitivity_unmeasured_confounding.ipynb | 56 +- ...sensitivity_unmeasured_confounding.myst.md | 50 +- examples/references.bib | 18 + 8 files changed, 1234 insertions(+), 229 deletions(-) create mode 100644 examples/causal_inference/experimentation_triptych.jpeg diff --git a/examples/causal_inference/assurance_planning_simulation.ipynb b/examples/causal_inference/assurance_planning_simulation.ipynb index 9872bddad..e66ab1fa0 100644 --- a/examples/causal_inference/assurance_planning_simulation.ipynb +++ b/examples/causal_inference/assurance_planning_simulation.ipynb @@ -14,18 +14,26 @@ ":author: Nathaniel Forde\n", ":::\n", "\n", + ":::{figure} experimentation_triptych.jpeg\n", + ":name: experimentation-triptych\n", + ":width: 100%\n", + ":align: center\n", + "\n", + "The experimentation lifecycle as a Bosch triptych. *Left, Bayesian Assurance (this notebook):* before any data arrive, the planner reads possible effects from the prior and asks what the experiment will likely conclude. *Centre, Sensitivity Analysis:* a single experiment is wracked by the biases it cannot rule out, and the model is contorted to see which commitments its conclusion can survive. *Right, Meta-Analysis:* many experiments are pooled through a hierarchy of levels into a synthesis that becomes the next plan's prior. Three panels, one posterior machinery.\n", + ":::\n", + "\n", "Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records, orientated at a new angle in pursuit of theory, anomalous findings and the next question.\n", "\n", "## The question planners are actually asking\n", "\n", "When we experiment, we implicitly assume the experiment will be useful. Our questions are \"operationalised\" as power heuristics: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold.\n", "\n", - "An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery serves all three; what changes is the problem put to it.\n", + "An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (like revenue per visitor) or binary (like conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery serves all three; what changes is the problem put to it.\n", "\n", ":::{admonition} Where this lands in regulatory practice\n", ":class: note\n", "\n", - "The assurance focus here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\\Pr(\\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference, which is exactly the split the generative and inference models below are built on. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical.\n", + "The assurance focus here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\\Pr(\\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference. The generative and inference models below are distinct objects for this reason, and a later section runs the assurance loop with the two priors deliberately mismatched. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical.\n", ":::" ] }, @@ -53,7 +61,6 @@ "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", - "import seaborn as sns\n", "\n", "from scipy import stats\n", "\n", @@ -76,6 +83,7 @@ "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina'\n", + "az.style.use(\"arviz-variat\")\n", "rng = np.random.default_rng(42)\n", "RANDOM_SEED = 42" ] @@ -120,7 +128,7 @@ "id": "541c0d41", "metadata": {}, "source": [ - "## Power and assurance: two questions, different focus.\n", + "## Power and assurance: two questions, different focus\n", "\n", "Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null:\n", "\n", @@ -285,26 +293,42 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": { "image/png": { - "height": 262, - "width": 515 + "height": 559, + "width": 1445 } }, "output_type": "display_data" } ], "source": [ - "pc = az.plot_dist(idata_gauss_demo, var_names=[\"delta\"])\n", - "ax = plt.gcf().axes[0]\n", - "ax.axvline(0.0, color=\"C2\", linestyle=\"--\", label=\"ref = 0.0\")\n", - "ax.legend()\n", - "ax.set_title(r\"Posterior on $\\delta$ at $N=200$ (one simulated dataset)\");" + "pc = az.plot_dist(\n", + " idata_gauss_demo,\n", + " var_names=[\"delta\"],\n", + " visuals={\"title\": {\"text\": r\"Posterior on $\\delta$ at $N=200$ (one simulated dataset)\"}},\n", + ")\n", + "az.add_lines(\n", + " pc,\n", + " values=0.0,\n", + " visuals={\"ref_line\": {\"color\": \"C1\", \"label\": \"ref = 0.0\"}},\n", + ")\n", + "pc.get_viz(\"plot\").legend()" ] }, { @@ -526,7 +550,11 @@ } ], "source": [ - "def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng):\n", + "def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng, analysis_prior=None):\n", + " # `prior` is the design prior: it generates the synthetic worlds. `analysis_prior`\n", + " # is used to form the posterior; it defaults to the design prior so a single\n", + " # argument reproduces the matched-prior case.\n", + " analysis_prior = prior if analysis_prior is None else analysis_prior\n", " seed = int(rng.integers(2**31 - 1))\n", " pp = pm.sample_prior_predictive(\n", " n_sims, model=gaussian_generative_model(N, prior), random_seed=seed\n", @@ -534,7 +562,7 @@ " y_A = pp.prior[\"y_A\"].values.reshape(n_sims, N)\n", " y_B = pp.prior[\"y_B\"].values.reshape(n_sims, N)\n", " d_hat = y_B.mean(axis=1) - y_A.mean(axis=1)\n", - " post_mu, post_sd = gaussian_posterior_delta(d_hat, N, sigma_obs, prior)\n", + " post_mu, post_sd = gaussian_posterior_delta(d_hat, N, sigma_obs, analysis_prior)\n", " prob_positive = 1.0 - stats.norm.cdf(0.0, loc=post_mu, scale=post_sd)\n", " return float((prob_positive > threshold).mean())\n", "\n", @@ -559,7 +587,7 @@ "id": "191f92bd", "metadata": {}, "source": [ - "For comparison we compute frequentist power at a single fixed effect size. The convention is to set the effect to the prior mean — which is itself an arbitrary choice, and a revealing one." + "For comparison we compute frequentist power at a single fixed effect size. The convention is to set the effect to the prior mean, which is itself an arbitrary choice, and a revealing one." ] }, { @@ -577,15 +605,15 @@ "outputs": [ { "data": { - "image/png": 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bgy1EG4pVqbTqdvYZGlv2ViXO5tM+j/379zufiW2j9t3jxarb/u9//wvetudbpSqrdmfvzbYBW9drW70wlO0v7VjI2DHNwIEDneqSNr/2PWLrrR0v2DJeuHCh8xnbT6vaGsk+67vf/a6zP7Tt346xbP20fdXMmTODn+fbb7/tVNt95JFHVB/se8y+zwJsfbF9h+17bJ9j66ztg2z+HnroIadKmX2XRlohrz6OE/7xj38E1xM7rgsct1feZ1dmlXmtIlqAHYvZOta3b1/nGM3WDft8AuuGLeuGXkZ33HGHU+HRjlnsO9QqeNp82DQD+z+r3mnf23YsYfs6a9Fr65zNg62H9j7sWNDWQVvn7djOtgFbf7w09DErAAAAgCagsdNwAAAAANCcWaWW1q1bB//a/4033vCsWjJ27Niw07KqW1bZJFAN4amnnvKspmRVrKZNm+Z/7LHHqjxmlVgCr2nVi6yKkZuCggJnfq+66qpGqYwWeK9W7cKt2ofNX2hlNKsutXjxYs/Xt+oOF154YXD6U6ZMiajaRqAqiVV9WbZsWYVxtvwfeuihChUdZsyY4TndW2+9tcJYq07hVRHv008/9f/gBz/wL1mypMpjdl9oZTWrjOE2nW+//dY/adKk4LiTTjrJXxe2zgWmdfjhh1c7/uWXXw6O79evX9TWxZqoSSWV0IoigapGXbt2da2KFLo+WkW0wPM6dOjgf/75551KaZVZNZHQyic2zmuefT5fcNx5553n37NnT5X1+/jjjw9WWmyMymj2Ho8++ugK1QLnz59fZVx+fr6z7wu8p1atWvlXrVpVbfUg2xZt7D//+c8q+z7bJkIrHl1yySWe76Wm7z2S51jFtsDjWVlZzj7Ji82r7QtsG69J5Z76EFoZLbCeWLUw+86o/BnZfiV0Xp544gnXaVrFwGHDhgXHtW/f3v/OO+9UGWf3ZWZmVlg/3Cqu/epXvwqOuf32211fc8KECcHXqm5saHXNWbNmuX5PH3bYYcExtjy+/vrrKuNsmwutjGifs1WWchO63Oy7zLbz0GqebvuN+mbLNrQy42233eaPpmuuucbZT3tV0bKKTLbt2DYcqKjktd1HozKaVa8MPG7rhFeVOKuG9tFHH/nPP//8KhWnFixYEJyGVfB78803PefHvn9//vOf+//3v//VujKaPde2h2+++cbzdWwZnnDCCcFpXXbZZRHtxwPHNnZcULkKlS2DH//4x8Gxtq+ubTXGypXpQisEWtW1nJycKtvET37ykwqf/4MPPhj1ymg1/WzcKqKFTvucc87xr1+/3nWsHatapVi3/WR9L6PQ4xjb5uyzHDRoUJVjS9vnn3vuucGxffv2DVZQs/eyffv2CuPtO8OmE1pB00tjHbMCAAAAaFyE0QAAAAAgiiw0ERpKqdzyyU68hJ5Mqhx2CvXaa68Fx9lJ0trKyMgInoi0dlO1Fe0wml2GDBlS7+2h7ORWYPrW0tBN5ZZ5dlIu3LI666yzgmMtrODGwg2hLRbtBHNtHXPMMcHp2MnucPbt21chaBEuJFMdWwahJxTdAhuhQls1/uxnP4vautgQYTS72Huv7j3bcwInki2IGm6bNtOnTw9O3z4nt4BpaOtHC5y5BdsCJ5OtVVvoPDdkGO0f//hHcIzNR3Wf6d13313tdhMaCrCT6OHamll7ztCgiFuLvWiF0X70ox9V25YvUjUN4dQ2jGYXa6tp642Xyy+/vEJ7SrdlaiG10LDDZ5995jm9efPmBYPGdnn66aerjLF9VOBxW/fdwmP2Ovb4pZde6h8wYIDnWPv+CIRubPt1C7/Zvik0+OG13gRYSDgw/te//rXrmNBlbOttuJBytFgQJnQ+3JZ1Y3juueeC82SthxsqjGbtYQOPhwt3hfPwww9X2wY9UrUJPHmx9Xro0KHOtCyA6BXsrnyMZaE8r/XdvotCW696res1ceSRR1ZoxWhBVi8W1gqMbdOmTbDFfVMMo9nyDv3DE6/vs8ZYRpWPY+x3EQuvu7Hv7MBxWeBy3HHHeR5zWHAuku/cxjpmBQAAANC4DvaQAAAAAABEtUXnueee67QgC9WrVy+nJVmAtQbyEto2qn379rWep8B0rOVOamqqmrLf/OY3TuvG+nTxxRcHr1uboUj8+te/DrusLr300uB1ayvk5g9/+IPTdtNYa6JAm7KashZiH3zwgXPdWtzddtttYcdbq9K77747ePvf//63asuWgbVOjWRa1gLwjTfeCN52a8N4KK2L5tprr63SVrCyP/7xj07rrEALOrcWkKGspaW1jjPWcs3aGYaytrvWuip0+qEtQUNZ26sHH3xQjeX3v/998Lq1KqvuM7399tudFpbm2WefDW4fXk455RSdeOKJno9by0lrYWesfVigFWdDqK/9c0Oz9cXWm3AtJm0fYqw9Zeg2HfC3v/0teP3qq6/WqFGjPKdnLeuuuOKK4G231n/2/EBbPNufBlowB1ib10CrN2u7GWiV6TZ2zpw5wTaH9l1buS2mTcda1hnbrqwNb+Xv6cp+9atfBdvhRbI/tdbMRx55pBqatfELFdjWwvnXv/7l7OfCXerKWjMH9g2Rfgc3lW20qW7ntl4HvmOt5aJtI5Gw4xKv9d3W8UsuuaTaY5tI2febtboNsHbE1h7Ui7Wvthb0geX+zDPPqKmyFtuBtp7du3d3Wmc21WV05513Ou293dh2ae2mK7cM9TrmsFaa1jo03HduYx6zAgAAAGhc4f93BQAAAABQaxs2bAiegDEXXHCB67gLL7xQs2fPdq7/85//1M9//nPXEz+BEz7mpZdecsJMgeBFTdh0vv32W+3atcs5cXXeeeepKWrbtm0wpFMTeXl5mjt3rhYvXqxt27Y5JwgD4SCzcePG4PWFCxdWOz0LalgIJpwRI0YEr69Zs8Z1zNtvvx28bif0A2GGmnrzzTcrhBxiY2OrfY4FngIiPUntxdbj5557LniS8L777nMd9+KLLzonxc2ECROc4OWhui4GfP/736/R5xPJ+MDn88477wQ/n5EjRwYf+/DDDysEeaoLt1kwp0uXLs7+pyHl5OQEt6fOnTtr0qRJEW1bdjL7rbfecsKLS5Ys0dChQz3H2/oejm1Tw4YN0+bNm4Pb4uDBg9UQQvfPFs6yE/rVhZoaW9euXZ0wVzgWYDr99NODIQdbH+12gAUQPvvsM9dgrpfLL788GEKzgMv+/fuDgTdjy82CY7bPLCkpcbaJ0BDiRx99VGF9T0xMdKZX3Vi392rzvnXrVue6rYsWJKlOdna2sx1acMTW2d27d4cNekW6H6hvgXBMQOgy9mLhsNAQvZtAeC8c+/794osvnG3QQjKBQGBA4PvPxlkI1SvsUt/b6DfffBMM+dxxxx21mkaALSdblyNZrvXB1jM7tlm6dKl27NjhbHuhAd7QIJDti0899dSw07Pv5HDB0UiPbSIV+l1m++nQ7zk3tlztjzgs2Bx4/lVXXaWmKPT4zsK2tk9qqsvou9/9btjHQ78z+/btG/Y7OTB+3bp1zvXVq1dX+c5t7GNWAAAAAI2naf+vGAAAAAAcwixYFjhRaCeuDz/8cNdx55xzjm644QbnZG0gwHbsscdWGTd27FjnRPnatWu1fv16DRo0SBdddJFzwtEqbUVaQex73/ueU00hNFhk99nJn6ysLDUVw4cPr9EJaqsCY9UUrLpc5ZPwXrZv317tmP79+4etTGEyMjKC1y1UU9mWLVsqnMgNVPKpjdAqWXY9kko15Z2pytm6UxcWELSKMBb0syCZnRy39c+twk51QcxDZV0MVJ4ZMmRI2DEWEFixYkWFqjORhA6t+pnX5xMamLR9QHXs9WxcQ4fRQtdLW98iraBk61Doew934ru65R/JthgtVu3pnnvucfb5Fq4bOHCgU1XopJNOct5TQ4RtasrWk0jWTwtpBcJolSv3WdWbQNjXqupUF1wI7NstRGEhNHuuTcMCq6EsOBYIeFjAwi1g1qdPHyd4GRoyqzw2NNzhFkYLXW/t+yDS9daCQYF13QLO4cJo1QV+oiVQXS7Alne0WUDL9umh+8FwrDKdbacWPo82+26ZPn16sDrUu+++61QTO+6449SjR4+IpmHVF209tyCYbQt2fGDbuYVP7XOuXHmvPti+3KpJWcC7cqivLsc2Db0/Dd13hFYEDsfGBYJWFm5sqj799NN6Ob6L9jJKS0tzwuLhhG6L9j1WndDxoZUDm8oxKwAAAIDGQxgNAAAAAKIktOWmVT8LdyLHKm9ZtbPAyVy3MJqd5LSAj530tBM+Fr6ysItdLCxlFRSsFZg9fsQRR3iGDOwk7IwZM5xqbHbC57XXXnMupmfPns5z7fWt+k2bNm3UWGrSAssCevbeA9UZIhVJaM1O3lUn9AS0VedxC6MFWMUMq6xTW9YqL8CCi6HV9yJhVcjqwqoW2Un9QHUcq45WOYxmJ88DraZs2Vjg8lBeFwPbaXWVrqw6WKi//OUvNX6dyp+Phf7cqvJUV/GqoYWul3bdKg/VVHXrZk23xUArx4ZggWNreXnzzTc767JVYLLKS3axddcCXRaGOvPMM50AS1NQm/UpdH2sfNvGRRK6szE2NlDJyS04ExocC61uZoGqQCW2wBhrOWehCQt1ho61KpmB1oIWfrPKguHW26+//tq51Pd621jtHNu1a+caoAvnqaeeci6hbJlWF7Cxdf6yyy7Tk08+WeP5tO/hhgij2fxZAM1CXca+e+xi7DvZvm8sCG3bqNdnZsvU3qOF2IqKipwgolWztYv9QcCYMWM0efJkp511fYQQLZw0ZcqUGn9vR+PYpq7709B9RSQVCE1oSDCSgF1jsOPx/Pz84G23KrBNZRlF8pmHHufUdLzbOtLYx6wAAAAAGk/T+7NMAAAAAGgGrEpC4ES7hcLsxGU4oWG1l19+2fNEorW+syoy1gottDWUnRS1ClW//e1vnROhFox45ZVXXKeRkpLiVIuxEFvv3r0rPGYtdixE94Mf/MCpTPWTn/ykwkm2hhRppTdjyzcQRLPgx49+9COn7aG9H6tgYtV37GS5XUIr5YS2uPJS23aaoUI/T6uqUhd1rU4S2rK0tkIrnT3//PNVAnhWRSmwbK0yVGh1lUNxXYx0fayPSlyVl6Wtv6HLKxIN1TYu2u89GttiNN14441OCNOqB4aGsiysYPuj22+/3dk3W7jE2hM2ttqsT5W/m0LXz5qsd+GmaaySaKCy1+effx6suGPB1UDgITQgFQimVR5r342B6kFuVasaYr2tyXdZferYsaPTCjfA2opGy6OPPlohiGYBe6sOa21MLUxiFb0C38F2CQ3aRPI9XB9sm/zPf/7jhO0qV/CzwIx9l1mLQwumWftNC/x7VUG0QKS1HAytmmrfTxZu+9nPfuasv3YJhLJrw5aZtVQMhHHs87TqixYOtGpRFsy0ZRdYpqHLv6GObWqiNvuK6vYTTUHl+arLMV60l1FNP/P6WEeawjErAAAAgMZBGA0AAAAAosCqmwXYSUKrXGAndbwuZ5xxRoVqLoHKHW5sWo8//rhTQeH99993WlPaSfnQE97WImvq1Kn6/e9/7zoNOylvwYmVK1c6oQirYvT973+/Qvsem48HHnjAmXZ9hICidcJ5zpw5TujAWHjBgoA238cff7yzrOxEXWgwpDFOaIa2Sws92VgboSceX3311Qon+CO91Ed7v759+zrXbT187733Kjxu1dKqa9HZmOtitIR+Nta2rzafTeWqRKEntm05RCIa7fiq235D37vtz2rz3i+++GId6iwwbO0lrRqi7cetBbNVrQzdB1llGNuGAvutxlKb9aly68fQ9bMm6124aZrY2FhnWQbCCDNnzgzbdjMQTAsdG1olza1FZ+X11vZDtVlvvabd2GzfGtrad968eVF7Lds/B/ziF79wKlzavt/aidu+sHKr62h8D0cawLL25hbqt+8cO5ay26HVrCxcaPdblbPKlQBDW1xasM0ef/3113XLLbc41Q9DA48WjLT18oUXXqjV+7FquRbKNtaO1ub53nvvdf7gwG5bmDQ0LNRUw1p12VdUt59oCsehleerLsd4TWEZ1bemcMwKAAAAoHEQRgMAAACAemaVWKzCRn21+PRi4TOrsHPfffc54YYdO3Y4Jy+HDRsWHGOVeKyNVDiDBw/WD3/4Qz377LNOe0VrC2XtrAIs3OXWcq+61pTRqEDjZvr06cHrFmaxykPVtfRsaFbRJLTaSeV2jrWdlrUCbCyh1f6sfWyAVcJZtGhRsErdqaeeGvE0a7suNhWhn421xPMKMtREaLu4SNvQWtWc6tT39ttU1sumIjMz06lq9NBDDzmhFAunTZs2zbnfWKjSqjA1pkjXp9Bxgfl3Wz9tvYskOGCBkNB1tPI0A0JDXoEQWiBgZq1OQ9sd29hAMCcw1iu41pLW29DqcValqy7fPV7sswwsO2u3acGscKxyXSSt90Lb/0XjGMOqcVqVWQsAf/vtt06I/8c//nHwde0+O74Kx77jrDX6b37zGycYb20SbXqB1om2rtt3Wm1C1KHHNjfddFOFdbWpHNtE+7ss9D257SeawnGorQOhfwwSCBA2lWXU2Jr7PhYAAACAN8JoAAAAAFDPrCJIoL1ToDJJJJdRo0YFp2GtntasWVOj17WTYVYNzU7Ad+rUKRiMs/ZwNTF8+HA99thjuvLKK4P3/e9//6syLrQCgwXhqhOttnTWXivAqrBUpy5ts+pyMi5wctpYeLC2Qivd1PSzrU+hFc/++9//BqtzhAbTrJ1ZaJu4mop0XWwqrJ1ot27dgrfffffdOk/TlkGAteKtjoWBLLRXnfrefseNGxe8vnTpUidM2FQ1RrtPCwlcd911FdZfW06rVq1SY7H1KZLwWOh6Z1XeQln42aqYBSoCRbKftwpPgf2FPTc0QB0qNEBmITR7jrVHrPxYYPkG9v+Vx1plntGjR1e7P7XvXQsLNycWtgoEdiys86c//Smq38EWEgwNkbmZNWtWROtdTfZRdqxjYbK6sGqfv/vd75w2m7X9vrFgklVas+/4xMRE5z4LqH3yySfN8timJkaMGBG8bsG9SIRWj6y874nmcWhNvyNC9yN1Ob6LxjJqbE3lmBUAAABAwyOMBgAAAABRbNF5yimnOCfyI7nYifNA60M7UfvPf/6zVq9vlUkmTJgQvG0VeWrD5j3cNHr27Bm8vnDhwrDTKigocEJ60RDa/q66tnN2crexwkwnnXRS8LpV96pt66HQz8Uqp0Qr5BdJVZlAAMmCH4H2S1bVLNIWnfW1LjYlViUnwKpi1bXFVGhlI9tHLF++POx4OxEeSRCsvrdfm95hhx1W4b03VaEByeLi4gZ9bWvl165du7Drc0PNn60noa0s3ViFPwubuq2PgbZyhx9+ePB25Tazbp588sngdWuFGNrGLZQFtC3cY6xKorVDDFQ/qjwfoQG1wNjAsps4caJnQMoeszaSgTDdo48+quaka9euFapYWtjKKvU11neweeSRRyKabk32Ufa9bvuppvJ9Y/MeGiCrzXRqslztM50/f76asmOOOSZ43bZRC6WGY9XknnvuOdfnB1jIPxAcs7ar1bXItNaqkajpPjj0+M72IbUNtUZjGTW2pnLMCgAAAKDhEUYDAAAAgHpkbfneeuut4O3Qk8CRCB1fuVVnJFUf3Nr7hLb9sRNk1Z2sq24aoSGCADvxb9U/vNx9991hH6+LXr16Ba+HhiYqKy0tdSpsNVblmxtvvDF4ctmqpFhbr9qw5R4IXVjQyQJf1vYsElY9JpL2aJEKDZtZRTSrzBJYb7p06aLJkyd7Prc+18Wm5Ec/+lGwUpSFx6pr8xZq8+bNVe4bOHBghapjth5Z6zc3FsawNnORiMb2e+uttwav//GPf9T777+vurz3aMnIyAher66NcaQi3b/Z9he63rutz9GYPy8333xz2BDPT37yk2AVM6v8Fxq2DAhtN2pB20CbXjcWsPjrX/8avH311Vd7jrXtaNKkSc51W+d//vOfh227GQioVR7rFlwLsOpVtk0F3HHHHTUKSzT1cGwggGahtECw5uSTT46oemJNgleBQJC1abb2ll6shbntb2q6jwoXcrTvv9tuu63ettFw3zeRTsNCk6EtUWvzvRXpsY0F1UKrhzZV1kL9yCOPDN62SpHhgl533XWXtm7d6ly3UOp5551XZYzdb9X4Asv83//+d9h9T6Rh05rug6+44gonmBtomxm6T2nsZdTYmtIxKwAAAICGRRgNAAAAAOqRnQgLVG6xE0NuJ+4jDaNZlYfQ9jsPP/yw07LvL3/5S4WTnKFyc3OdQEigPZmdzD/hhBOCj9vz7KS0BWbmzZvnOg07WfT22287AZQAO3ntdoLJqmMZC1ece+65VU4a2UnSW265xTkZHmhZVd9sGQdOhFubNQvjWLWIykGX7373u3rjjTc8q/BEW79+/ZzlHnD77bc7JxoDLV0rsyonF198sdPKrzJbFwInPi34YZ9FuOCPrUu/+MUvnNBA6DpVV9///veDLeDs9X//+98HH7OToqGVXSqrz3WxKbFt4qc//WnwtoXR7HP0qlZmIUlbdj/4wQ88W2zZZxfa5srGVj6ZayekzzjjDKeCUEJCQrXzGY3t104yByqz2H7Qtk0LXQbCTJXZ61olPXuObQsNZciQIcHrVtHPTnjX1TnnnOO83xdeeMHz/VrIxbaZwOtZJcw+ffqEnb9IK/nUhq0ntr6cdtppVb5TLCxqQTVrkxtw//33u1YYs++tQKtNe2/2nWPtoiuzqjgnnnhiMFxh67utd+GEhs4s6GSsAp+1Pq7Mwq+hoSi3abixfVCgipV9h1oAzkIrXuuFBcNtuVjlNts26pOFruw9BC71wVqYvvjii8HvPttXWODlhz/8YdjWlvY5WUVECyRWN/1AKz4LAp599tn6+uuvK4yx+y2oeOGFFzrHJZG0bw4N1lj1J7cWo1Yp0vYfFoCrbh9lLZQttGXVAG2/68aq1F577bWe3ze2LI444ginAq5XSMb+KMHaowa2KTsWtAp8dakoZX+c8OCDD1aZb/tuP/744/XFF1802rFNTfz6178OhrVnzpzpHJcFwlQBtt3deeedzvsNuOeee4LHPOHWEwslWhvYyuyPRGw5RbpN1XQfbBWJQ//AwAK33/ve9zy/9+247oYbbnBt5R2NZdTYmsoxKwAAAICG5V6jHgAAAABQ5xaddgIpkhOuoSyYYCdpAuEcm17oSUxr2fN///d/zslSC5IMHjzYORFsJ42tBeWcOXMqBCHsxFygIkpo2zULDdnF2sWNGDFCnTt3dk7k2gkvO1G0evXqCiEqO2lWmZ3U+9WvfuWEMIydWLITR1OmTHHmyQJgVinLXi87O9uZbzt5Vt+skoSd4A5UkrOTc88884xGjx6tDh06aM2aNc582Mm71q1bO+GBcNV4oumXv/ylc/I80PLQTq7//e9/d9qqWrspC3rYcrOWW4ET2W4VNuxztxCPney0wJCd+D/uuOOcz9rety1/e792UtzWmUjaNtaGVQ+x4EmgfV5oC9RIWnTW17rY1NhJYVvvAvsD+2mV4+z92fpqJ2UtTGYVVOzzCVTKCq3GEsrCFjfddJP+8Ic/BEOvtqztflvHrXKLtee0Cle2Hp1++ulOZbJworH92gl0O3Fv66JVobF10PZBP/vZz5zqbhYGsQCUhTgsBPPVV18Fg0m2v2wo1lItJSXF2XZs+Vu4yQJL1q4xEFaw4IJdImVhmzfffNO5WEDTtlFbX9PS0pyAk33WFnQJVLWzZTVt2jTXadmysBCmseVn1y0sFRq2sc/CAhB1YWEkq7j03nvvOZ+9LQP7jCwga2Gy0KCszdNll13mOh37TG1/ZGEw2+fYumPrpgXULEBtLPQW2nLO1lt7TiDM6iVcBbTKbPuxEEloZbbKbUTd2Bjbno499lhnf2PbpoWWLHhkbVVtn2TrhS2PZcuWOfvbwOcYrupaU2LHFRbqsH2DrYu2bVq7TLv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" + "
" ] }, "metadata": { "image/png": { - "height": 431, - "width": 1233 + "height": 923, + "width": 3023 } }, "output_type": "display_data" @@ -655,9 +683,9 @@ "source": [ "The three power curves fan out because each conditions on a single assumed world. Power at $\\mu + \\sigma$ assumes an effect of $0.9$ and saturates almost immediately; power at $\\mu - \\sigma$ is pinned near the test's five-percent floor, because $\\mu - \\sigma$ lands at essentially zero effect, where even large samples gain almost no detection. Power at the prior mean is the curve a frequentist planner would actually quote. The assurance curve is the prior-weighted integral across the entire fan, and it tells a different story than any single power calculation.\n", "\n", - "The story is in the crossing. Between $N=400$ and $N=800$ the assurance curve drops below power-at-the-mean, having sat above it at smaller samples. At small samples assurance is buoyed by the optimistic tail of the prior: it counts the plausible draws above $0.4$, effects of $0.8$ or $1.0$ that even a hundred observations have a real chance of detecting, which the single-point power calculation never sees. The frequentist who quotes power at the mean is being pessimistic about small experiments in a way the prior is not.\n", + "The story is in the crossing. Between $N=400$ and $N=800$ the assurance curve drops below power-at-the-mean, having sat above it at smaller samples. At small samples assurance is buoyed by the optimistic tail of the prior: it counts the plausible draws above $0.4$, effects of $0.8$ or $1.0$ that even a hundred observations have a real chance of detecting, which the single-point power calculation never sees. Quoting power at the mean understates what small experiments can do, because it ignores the optimistic tail of the prior that assurance counts.\n", "\n", - "At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one: the prior assigns positive probability to a zero or negative effect. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\\delta > 0) \\approx 0.79$. No sample size buys more assurance than $P(\\delta > 0)$. That ceiling, not any point on a power curve, is what a planner should know before committing to more data.\n", + "At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one: the prior assigns positive probability to a zero or negative effect. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\\delta > 0) \\approx 0.79$. No sample size buys more assurance than $P(\\delta > 0)$. A planner should know that ceiling before committing to more data, and no point on a power curve reveals it.\n", "\n", ":::{admonition} The minimum detectable effect leads a double life\n", ":class: note\n", @@ -685,8 +713,9 @@ "outputs": [], "source": [ "BASELINE_RATE = 0.10\n", - "RATE_LIFT_PRIOR = EffectPrior(mu=0.01, sigma=0.012)\n", + "RATE_LIFT_PRIOR = EffectPrior(mu=0.005, sigma=0.012)\n", "BETA_PRIOR_KAPPA = 200 # prior concentration, in units of pseudo-observations\n", + "prior_prob_lift_positive = 1.0 - stats.norm.cdf(0.0, RATE_LIFT_PRIOR.mu, RATE_LIFT_PRIOR.sigma)\n", "\n", "\n", "def beta_prior_params(p_mean, kappa):\n", @@ -750,15 +779,15 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 431, - "width": 1233 + "height": 923, + "width": 3023 } }, "output_type": "display_data" @@ -836,11 +865,11 @@ " label=r\"Power at $\\mu - \\sigma$\",\n", ")\n", "ax.axhline(\n", - " prior_prob_positive,\n", + " prior_prob_lift_positive,\n", " color=\"C0\",\n", " linestyle=\"-.\",\n", " alpha=0.6,\n", - " label=rf\"Assurance ceiling $P(\\delta>0)$ = {prior_prob_positive:.2f}\",\n", + " label=rf\"Assurance ceiling $P(\\text{{lift}}>0)$ = {prior_prob_lift_positive:.2f}\",\n", ")\n", "\n", "ax.set_xscale(\"log\")\n", @@ -856,7 +885,7 @@ "id": "45306fae", "metadata": {}, "source": [ - "The conversion-rate version runs the same loop, and the loop discloses something the Gaussian case conceals. The lift is bounded above: how large a positive effect can even be depends on how much room sits above the baseline conversion rate, a constraint the Normal likelihood never faces. The Beta prior on the per-arm rates is parameterised here by a concentration $\\kappa$ — the number of pseudo-observations at the baseline rate that the prior is equivalent to. Setting $\\kappa = 200$ is saying: I am as confident about the baseline as if I had watched 200 trials at that rate. This is a different handle on prior calibration than the Normal's mean and standard deviation pair, and it makes the ceiling claim more precise: the assurance curve asymptotes toward the prior probability that the lift is positive, and what that probability is depends not only on the prior's location and scale but on the scale on which the lift is defined. At a 10\\% baseline with a planning prior mean of 1\\%, most of the prior mass sits in a narrow positive strip; the ceiling is lower than the Gaussian case not because the machinery differs but because the planning prior encodes a more cautious belief about how large a conversion lift can plausibly be. The generative model produced the synthetic experiments; the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that this matches a full PyMC fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably." + "The conversion-rate version runs the same loop, and the loop discloses something the Gaussian case conceals. The lift is bounded above: how large a positive effect can even be depends on how much room sits above the baseline conversion rate, a constraint the Normal likelihood never faces. The Beta prior on the per-arm rates is parameterised here by a concentration $\\kappa$, the number of pseudo-observations at the baseline rate that the prior is equivalent to. Setting $\\kappa = 200$ is saying: I am as confident about the baseline as if I had watched 200 trials at that rate. This is a different handle on prior calibration than the Normal's mean and standard deviation pair, and it makes the ceiling claim more precise: the assurance curve asymptotes toward the prior probability that the lift is positive, and what that probability is depends not only on the prior's location and scale but on the scale on which the lift is defined. At a 10\\% baseline with a planning prior mean of 0.5\\%, the prior splits its mass roughly two to one in favour of a positive lift, and the assurance curve asymptotes there, near 0.66. The same machinery produces a lower ceiling than the Gaussian case because the planning prior commits to a more cautious belief about how large a conversion lift can plausibly be. The generative model produced the synthetic experiments; the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that this matches a full PyMC fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably." ] }, { @@ -882,15 +911,25 @@ }, { "data": { - "image/png": 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", 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" + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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wIxA+Wnbv3q0FhroFx483jp9LLrkk4kFLlUnzjz/+EKN+/PHHiMv11K5dW/r37x/WPFxTOvO6gXsu83Eejy2xfh0EuI0ak+vevbvceuut8vLLL2v38WoMSVV8UH8b1Zi1OmbVuN7+/fu17E9qXO+VV16RSy+9VKpUqWJKO8aOHatlqIK99MqB6WXz6t27t9SoUSPg97/99pvk5+dHpZRmNEqeq7FYt36cbu/evfLggw8G/P7222+XLl26iJPpVW4IJRDILHXr1tWdJjMzM6xlun37vLKNdjy/JnAMVkmwbMkAYJKFCxdGPK+XAsfef//9iEt6/vPiLNR0sOXl5Vr6YvWvGdQbUAMHDpQzzzxTu3CrVauWZGdnazc26mLo66+/1rKqmZHR4LHHHpOrrroq5AdkKqAo0v5VN+hq8LdNmzZB36ZQH7XdnTp1kvPOO+//vlMDQ+o4UAFn6qHcrFmzPD2QfN999xl+cKWC+F566SVp167d/3xXvXp17dO+fXu57bbbtH3uhhtuiDg1sQoIVG/4q0FDWMNL5yKEj/3DXJUrV9YCTM444wypX7++tu+qY0MF3KoyZmvWrDG8DvXAZebMmYaDd6Plnnvu0c4ZZlJ/r1Sfq3KCKjOsevhw7AGEyoaqgtU2bdokixcv1jIvLliwwJRzIsePd44f9VD06quvjjjTjbrWVQH50SpTqYI1ExLCG+LimtJ5uOcyH+dx75zHAZinefPmWqYodW3Ts2dPSUpK0p3n2LieGj84VrpQjVOocQN1naSyEavrdSPnhu+++04uv/zyiJeB8GzZskX3pQa9bF4qe5N6uWH8+PHH/b6goEB++eUXLcjQisCxihUrSosWLSJaNtxFjXEH2p9Vyb4nn3xSnE6VtA/GrEDdUISyLr32em37vLKN4VBZK6dPnx72fKtXr9b+fqSkpFjSLniYH54ycuRIVQ8p4k+srdcJ69+0aZN/7dq1x/1MmTLFULuefvrpgMsO5XPkyBF/LLjlllsi7qO8vDzL29e4cWNDv6Oa36jJkyf7ExMTDbXj2Oeyyy4Leb0ff/yxKeusW7euf+LEiSGtc/ny5f4uXbqYst577rkn5G1dtWpVROuoU6eO/9ChQ34zZWZm+j/66CP/Oeec409ISPi/dalzndvPSTNmzDD8u48aNcpfXl4eVp/v27fPf9JJJ0W8TnV8btiwwR+Nv2Fmf+666y5L2xsJL52LYnnfiBb2D3N+X/X35tFHH/UXFBQE7e+3337bn5KSYnh9jz32mD8WTZs2zdTj6uyzz/bPnTs37Hbs37/fP27cOH+rVq3+a3kdO3YMazkcP946flasWBHxOtPS0vy5ubn+SGVlZfmTk5MjXv/69evDWh/XlM68bnDLPVcgXCfH7nVyrJzHoy2a46+MPcfuOE+01a9fX9vWpKQk/5AhQ/xz5syxZD05OTn+/v37G/o9LrroopDWFetjf07x9ddf6/bH4sWLdZfzww8/BF3GoEGDIm5j586dgy67d+/efqdQx5eZf5ud9nEyNdYZrO3ffvttWMsbP368bn+oacykxvLj4+ODrvO0007z2+Wvv/7S7YPRo0eHvDy3b59XtjFcb7zxRsTnnAULFljaNngTGccAiwUr+VehQgVDy1ZZUFq3bi1ut3Xr1ojmq1q1qqSmpoqbqVSpY8aM0TIDqJJ4ZrjoootsLY2psnDNmDEjaNruf0bhq8xfV1xxhUycONHQut99910ZOXKk9tawnu3bt0ec/UBlrzKTeqtZlT9UH1UaSL25+M4773jinGS0FOEDDzyg/eaRlD+aPHmy9ibhwYMHw55fZY1TZUrffvttcRK1P6jsdv369dMy2Kg3Y9W5U2WvUW/UqDfmly9fLr///rvMnTvXsWURvHQusotb9g2F/cM49Zay+q179OihO+2NN96o7T9q3zGSHVNl14xFZpXMVX2u/r5feOGFEc2vjtm7775b7rrrLpk0aZI88sgjEWVB4fjx1vGjsjV369ZNy1gXrry8PC0LhspaFolvvvlGyzYViVNPPVVatmwZ1jxcUzrzusEt91xOwnncW+dxeFOsj/NEm8q6qq6Zhw8fbmmJLJWNTGUv79q1q5alMBJTp06V4uJi3Qxo7BPmWLp0adDvExMTtWoFes466yzt91fXRcejSpuG8rv+k7qeWrVqlaGMaHA/VbVDlaEMRI05qGyqTqdKuupdW6lrNrtUqlRJd5pwMtG7ffu8so3hUhmMjTw3V9cUgJniTF0aAFgg0vJwKsWum6gbSDVgrQYXPvjgA7nmmmu0bVTl78wKGktLS5OLL744pGmnTZumDf4bodqvSi+GGqjx9zTfqpRNnz59DK1f3bCrEp+hyMnJiWgdTZs2FSuphzL33nuvrFu3Tu644w5xM/XA6bfffot4fnUh/dxzzxm6kI+0fJOiHjZZnd44VBkZGdqApSqToLbp/PPP18qg1axZUyvzpFI1q2kuuOACLdhApUxWZRFefvllx6W499q5yGpu2jcU9g/j1OD1999/H9LD0mNU+aYRI0YYWm+kD1OiSbVZPVg2qlGjRjJnzpyIg8b+zufzadd2K1askNdffz2sAFWOH28eP8OGDYtKqUkj815//fVhTc81pXOvG7jnMhfncW+exwGER113q79LVgaN/T1I7aWXXjI0drBw4UJT24TIy0Cqa55Qgr3U764C7IMF9kyZMiWiUmV6L16cdNJJYS8X7nL//fdrZbWPRwU0qnGCWHk2FsqxZpdQ1hXOS7Zu3z6vbKOdgWORPjcHgiFwDIDj7dixw/Y/unZSb1Wrh3p6H3Uho7IeqSw36oGOCkIpLCw0tS3qoYsKHrP64c4x48aNi/h3Uv3x3nvvhf021j998cUXpl3YRvsCzuy37J3m448/NhQk+dprr2nHkhGXX3552Bkt/v5WzYQJEyTaVCaQtWvXysCBA8OaT2WNUG/h3nbbbeIkXjsXWclt+4bC/mHco48+KqeffnrY891zzz1a0ECkDh06pGVWjSWffPKJ4WWorBAqw2Uob66HG+iqjlH1NnuoOH68efxcddVVId8P/JMKwlZBQeHatWuXlvUzEioYMty/W1xTOve6gXsuc3Ee9+Z5HEB41FirndQ5xcg69bJgwTx6fR1ONi+9jE7ffvutmB3YphA45m3qpY1gL6qOHj1aGjZsKLEglPsEI9de4QplXeHc27h9+7yyjXY+w440WzcQDKUqATiayrClgi3cHDjmFCkpKfLQQw+FNG15ebn2UNMIVVIm3IcM/9S8eXMt1bKRt/UWLVqkPazSy1AXaXp/dXOmMoHVq1cvwhbiGCPlAFXZpe7duxvuzLi4OK00oSoRGwlVwumWW26RaLn11lu1N8mMBtA5hRfPRVZx276hsH8Y17hxY63Eb6QlvtQgdSQl747Zs2dPTAVFq1J7RqmMPR07dhSrqDeKQ8Hx493jR5WGUIHy48ePD3teVXZCBcnfeeedYc33+eefa/tcJP71r39p9zHh4JrSudcN3HOZh/O4d8/jAJxNjSu1bds2omB7RWUShvV2796t+xuFE5SlsrKqFw8DZQhTY1vqWlq98GNW4Ji6rmrTpo04harK0blz52g3wzNUwoEbbrgh4EvYav8N974tmtwedOT27fPKNoZLBZKrdpSWloY9LxnHYAUCxwA4WqA0uqEIt+SY1z3xxBNSt27dkG9MVVCf0YcNZlBBOEaCNRRV/lCvLE+kA7YHDx7Uylq88MILMmDAAFsvft1EleJUJXAipUq7mqVv374RB46pcjF5eXkRZ/IwomfPnqZkXXMSL56LrODGfUNh/zBOPSyN9CG+0qFDB0MPTNVAvVpGLFBZd7Zu3WpoGSqrrBrYdQKOH28fPyoLcSSBY4rKihzuAwg7y1RyTens6wbuuczDedzb53EAzmakLCYZRuwRSma3cALH1MsZqpTxzz//HHD8eObMmdqYo1mBY+r+0knj0CNHjtQ+sMeoUaNk48aNx/1OBSi+8847YQUqxgI7xzVVELAeI5VTvLh9XtnGf7ZBvXASyfMNI8/OgUAoVQnA0VSARaRSU1NNbYubqcH/e++9N+Tp58+fb2h9Kmimf//+YgaV6cdo2u1QtqdJkyYRL3/nzp1alir1BsGVV16plcVTpXzUWwGRZlfwmjlz5hiaP5yBl1D2uUiVlJRomaXspjJxqAfAodwQxRIvnovM5tZ9Q2H/MEYNMKusQ0Y0atTI0PzZ2dkSK2bPnm14GZFmNbECx4+3jx91bxBpdoKFCxcGfEBxPKtWrYo4c4b6uxtOiSCFa0pnXzdwz2UezuPePo8DcLakpCRDmbBgPb2gLHUtFG6maDPLVarxZL1r6HCvk+Eey5cvlxdffDHg96pqQqxlf0tMTNSdJpKsTUbG+M0817t9+7yyjZEIN4O6Gc/OgUCcE24OACb/8Yv0D67XqMF5VT4vnDdMjLw1q5x22mmmBvapdN96N/RGt0el065SpYocOXIk4vUcPnxYK9+jPseoNOVq2S1atPi/T6tWrbQ3i8maZ86DD1WWS5UBMIvR30VtS58+fcROQ4YMkZYtW4rbePFcZDa37hsK+4cYDritWbNmVM+XqrRCrJg3b56h+dUbhiq43Ck4foxxw/GjMmDef//9EWcdU9mMnZZtTOGa0tnXDdxzmYfzuDFuOI8DME9ubq6sXLlS+2zZskV27NihBXCpcb6srCzJycnRHkCrj9UPvVVmKlhPb2xHXQ+FW03goosukptvvlkrSXk8anz+lVdeCSnjzvr163Wfmxh9uRGxSe1f6l4u0LmoQYMGEVfSiKZYDDoKpc1e2T6vbGMkCByDkxA4BsDRCByzVuPGjeX333+XWrVqhTVfOFkEjifcN7L0qNTbRoSyPeqmXQX7TJo0ScxUVFSklcxRn3+qV6+edOnSRU4++WTp3bu3dO/e3VEpxu0uAWZkgM9JqbfXrFlj+zpvvPFGcSMvnovM5tZ9Q2H/MKZbt26GfwNVjsPqQRynUAP3RpxyyimO+hvP8WOMG46fwYMHy8MPPyzFxcVhz6uCwUIJHFNlH7744ouIB1cHDRoU9nxcUzr7uoF7LvNwHjfGDedxAJFT2ZxmzZolkydPlr/++kuWLVvmmIoBBQUF0W6CJ+gFjkWSzUsFJPfq1UtmzJhx3O937dqlZe8N5W9QKC8tEjjmTS+99JIsXrw44Peq5LzRa5RoCCXzUyT3rk7JVuX27fPKNkaCwDE4iftq0gBwlfz8/IjnJeOY6N7gqrJKKsNVuLZv3y5GqGxaZjK6PLWfHTp0SHc6o6UqwrVnzx5tkGrEiBHaA2X1xvJll10m33zzjefePja6z3l5W9Tx4db09F49F5nFzfuGwv5hTKdOnQz/BuG+gX28oJJYocpPG6ECxJ2E48cYNxw/6sHWhRdeGNG8mzdvDikL38yZMyPe19Q1ceXKlcOej2tK5183cM9lDs7jxrjhPA4gsmxeKvi9YcOG2sujqtSbCtBxStCYQuCY9TIzM2Xnzp2WBGWZVa5SL3BMZckxe8wLzqcyIo4aNSpo1ruLL75YYpGqGKGXjU+9PG4XlWnSzGtBt2+fV7YxEpFWQzHy7BwIhMAxAI5mZKCMQbbjUxdnd9xxh8ydO1fq168fUbrjffv2idESJGYyY3nqrS49AwcO1LKARcvRo0e1oDH1oEy1Y/jw4bJ//35xOzVAp0oAuIXe4JPZ1NuMbuTlc5FZ3LpvKOwfxrVp08bwMuxI6e6Uv1Mq2NtJGRCN4Pgxzi3HjypxEilVrtJpZSq5poyN6wbuuYzjPG6cW87jAEKjXs588sknpUmTJlrQhdFreyvZWcbLq0LJ5hVpMP2AAQNsCRxr27atJCcnh9U2xD6VHThQMEl6erqWbSxWqWoilSpVMhwIZJZQ1lWtWrWQl+f27fPKNtr5HNtJQe1wDwLHADhapNHWCm9g/a+ePXvK/Pnz5dVXX4345lGVDzV6UaJ3gRgulTLWaNrYUN5mUOt46qmnxAmysrLkueeek+bNm8vzzz/v6gtFtc+phx9uoQIA7dS1a1dxIy+fi8zi1n1DYf8wrkqVKoaXYUdKd7fsb9WrVxen4Pgxzi3Hz9lnny2NGjWKaN4JEyYEfbCpSlCoFyIi0apVq4iy9HFNGRvXDdxzGcd53Di3nMcBhFbat3v37vLYY4+RvQOWB46pa+tg2crU/rhq1Srd5ajyqVa0D7Fr/PjxMnXq1IDfjxkzRsumGMv0gnjsHHMPZV3hBh25ffu8so12Pce2IxsavIfAMQCORuCYcXFxcXL++efLlClTtNKURgf8zQjIq1ixouFlmL3MUFO7Dh06VPftMDupIJMHH3xQzjvvPO0BgRu5Le2u3dtzwgkniBt5/VxkBrfuGwr7h3FmBFaqtwm9wIzj1o7BqFBx/BjnluNH3Udce+21Ec2rSi//9ttvAb//5Zdf5PDhw7ZmQuOaMnauG7jnMobzuHFuOY8D0A++6dGjh6xYsYKuwv9ZunRp0N5QmemM3L/plaucOHGibln4I0eOWFJKE7FJVSS57777gu4PqgJNrNN74c7OyiyhVMEI9wVBt2+fV7bRrnEKAsdgBQLHADgagWORBy7069dPXn/9dS29+o8//ihnnHGGKb+Jyg5gVIUKFcRsKSkphuYvKioKedqPP/5YTjvtNHES9WDuwgsvdGXK+nB+m1hgdzZEJ2WxMRPnIuPcum8o7B/GqTIGZpTH9gIz9jcz+tssHD/Guen4ue6667QAskgEK0UZSinLQKXfhgwZEtG8XFPG1nUD91yR4zxunJvO4wCOb+fOnXLWWWdFHMgO72YcM5rNy2i5ylAyohE45i0qKExVJwkUyP7OO++4IqC9fv36Qb9XfWDGdbBZQUd67fXa9nllG+16VmTk2TkQCIFjABzNSNS0294oP0Zd5KuLgqpVq0pGRoacfPLJ2ptKKuvUBx98IIsWLdIusH799Ve57bbbpHbt2qauP9ISl1b/NkazbYUTzKb2S5Ul4ZZbbhEn+fPPP+WJJ54QtzFjn3OSSOvWR6py5criRpyLjHPrvqGwfxgXaaDI37lhYNKu/c3uMsbBcPwY56bjR5XTOfPMMyOad9KkScctwZydna292BKJ/v37S61atSKal2vK2Lpu4J4rcpzHjXPTeRzA8cdlrrzySi1DKvB3OTk5smnTJkuDstq2bauVXg9EZcBTWcUizYim/oZ16tTJUBsRO3744Qf5+uuvA35/++23S+fOncUNmjZtqjuNSqJgh1DWE0p7vbR9XtnGcFGqEk5C4BgAR6tTp07E88bKzX/jxo21AYtQPyqjlAqSUm/EqZvIOXPmaG8iPffcc1opGXUjYOUApRmR7Oom3GxGlxnudqnB+DfffFOmTZsmXbp0EacYO3asbN26VdyEtyeiX2bFiTgXGefWfUNh/0Cs7W9OynTA8YN/uv766yN+WeS77747bvmdwsJCW9uicE0Ze9cN3HNFhvM4AAT3ySefaOOpwPGCsvRe+DSacSyUcpXBso7pZRxr0aIFJcw8JFhp0wYNGsiYMWPELVSZWD16gZ9m0VuPSvoQ7v2T27fPK9sYjrKysojHA408OwcCSQj4DQA4QN26dSUpKSmi9KS7d++2pE1epwahExISDJVENDurhorKN1qiMdI36Pv06SMLFy7Usn29/fbbMnny5IgfhJlBHSvjxo2TV199VdxCZRtQwZDqQhrhU8erG3EuMs6t+4bC/gG7/04ZvTZy0gsPHD/4p4suukhq1qwpBw8eDLtzVEnKa665JuQSlsE0bNhQ+vXrF/EPxDVl7F43cM8VHs7jABDc008/bUoXqaCMCy+8UE499VQtg5QqaaWuN/QCeIcOHSofffQRP5MD6WXzMqsMpAoce/bZZ4MGjj3wwAMRtdGpZSpVlQyVHcutVBUYp1HPCU477TTTl5uZmRnS7/3666/rZpMeOXJkyOtt3rx5SMFAkWbMDkewrIChttVr2+eVbQy3XGakz7xUQhLAbO59WgTAFXw+n1YeJZIocwLHrKHSXauBkO3bt0e8jI0bN2olNs2ilmeUehBlxOmnn659VOaz3377TStlOX36dN0LUCt88cUXWvCYW0pjqH2uXr16snPnzmg3BQ7CuQjsH3DS9aq6jjCS8VMFoZ977rniBJxf8U/qRR4V/PXSSy+F3TlTp07VBkOPvQ2rykH89ddfEXWyyq5spHwc15Sxj3uu0HAeB4DAZs2aJevXrzfURdWrV9cy/g8ZMsQ1Y28ILZtX7dq1tRfdjeratasWeLhr167jfj9//nztulmNh/6dmv7AgQMxGTim7pcXL14c7WZ4itpX9PYXq2zbtk37BNO+ffuwlhlKCdZVq1aJ1VSf6r1UFUm5WLdvn1e20a5n2KFkbwPCRalKAI4XaeQ0gWPWUcF8RqxcudK0tpixPJVtLNKMY/9UsWJFGThwoLz//vtawKN6UPbzzz/LM888I4MHD5aePXtqgwxWUllL3HYjbuRCWAUphlMO1o4PzMG5COwfcAqjAzYzZ84UJ+H8in8aNmxYRJ2i3p798ssv/+sFh/Ly8ogCYa677jrDPwzXlO7APZc+zuMAcHxqjM4I9TLt3LlzteuSSIPGzK7EAPsCx8wKylIvH1188cUBv1djh8cr+a7XPicHjgFGqfNvrVq1gk4TyjFiVCjPXSIpaev27fPKNtr1DJuMY7ACgWMAHK9p06YRzZeVlSX5+fmmtwcibdq0sTztdziWLVtmaP7WrVuLVVSQmMogMnz4cC0N/ezZs7VgstzcXC3g7fvvv5fnn39eG3Dq0KGDoSwKf7dgwQJxEyO/kXpLhWAtd+JcBPYPOEXbtm0Nza8ePuXl5YlTcH7F8fbxSDMGq3KVRstUqlIVZgyMck3pTtxz/S/O4wBgzQsb7777rrRo0cLQMiIp/w3rFRYWyrp162wLylLlKoNR5SojCaiwI9gBiBa9Y3D58uVSUlJiaRtCee7SuXPniJbt9u3zyjaGKlDWyVCQcQxWIHAMgOOdcMIJEc+rd7OHyHTv3t1Q182YMUMOHz5sWvdPnjw5qtsTibS0NC0d80UXXST333+/lqFsxYoVWlDZG2+8YfgNcTPKdzqJkd9IlQ+NpNwtnI9zEdg/4BRGS3CroLH33ntPnILzK47n+uuvj/htXlUSau3atRG/QBLpuv+Ja0pv8fI9F+dxADB/rLhly5aGy8urFxud/PfDy9Q1QmlpqW1BWX369NHKngYbP8/MzAwrcEwFElStWtW0NgJOo46bYFQiiXnz5lnahj///FM3O3KkQaZu3z6vbKPV1yQq+YRKQgGYjcAxIIoiTWd9THFxsXhBly5dHFMSEf/Ro0cPQ12h3hg43ltTkVAPnzZs2BDV7TFTzZo15dZbb9W2y8jFXyRpbp18TjL6QP7rr782rS1wDs5FYP+AU/Tq1cvwMsaNG6f7oMIunF9xPFdccYU2gBpp1rFIs43VqFFDC/wxA9eUiPY9l104jwNw+jhPNKiH0YcOHYp4/t69extug8qkcuDAAYkW9onA7C4DqX6L/v37B/xe3Rv+82Vpu0ppAk511lln6U7zxx9/WLZ+VUVGL6ipb9++kpCQENHy3b59XtnGcAKWI9GqVauIx2aAYAgcA6IoOTnZ0PyqFKMXdOrUKeI/0qpEHawpVRNpCdFjXn31VSkrKzPclldeecXQ/GrfOvvss8VpqlWrJi+88IKhC2A3nZOM7nOffPIJ5SpdiHMR2D/gFOrNbqNv+23fvl2eeOIJcQLOrwiUvUkFj0Xi888/1z6RGDx4sCQlJZnyo3BNiWjfc9mF8zgAp4/zRIPKSG9ErVq1DLfhq6++kmhinwhMLyirSpUqhsfDwy1XOXHixP8qcaoXtE7gGNxOZf1TLxYF880331i2/kmTJukGVYcSGOXV7fPKNlr9/NpIshUgGALHgChKT083NL9XgqJSUlK0Qc9IkHHMOno3tqH8Nu+8846hZSxatEg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+ "text/plain": [ + "
" ] }, "metadata": { "image/png": { - "height": 262, - "width": 519 + "height": 559, + "width": 1223 } }, "output_type": "display_data" @@ -927,11 +966,17 @@ " progressbar=False,\n", " )\n", "\n", - "az.plot_dist(idata_bern_demo, var_names=[\"lift\"])\n", - "ax = plt.gcf().axes[0]\n", - "ax.axvline(0.0, color=\"C2\", linestyle=\"--\", label=\"ref = 0.0\")\n", - "ax.legend()\n", - "plt.title(r\"Posterior on conversion lift at $N=4000$\");" + "pc = az.plot_dist(\n", + " idata_bern_demo,\n", + " var_names=[\"lift\"],\n", + " visuals={\"title\": {\"text\": r\"Posterior on conversion lift at $N=4000$\"}},\n", + ")\n", + "az.add_lines(\n", + " pc,\n", + " values=0.0,\n", + " visuals={\"ref_line\": {\"color\": \"C1\", \"label\": \"ref = 0.0\"}},\n", + ")\n", + "pc.get_viz(\"plot\").legend()" ] }, { @@ -939,7 +984,7 @@ "id": "005827fd", "metadata": {}, "source": [ - "The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The decision rule is the same; the ceiling is lower because the planning prior carries a smaller probability that the lift is positive, and that probability — not the sample size — is what sets the asymptote.\n", + "The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The decision rule is the same, and the ceiling is lower because the planning prior carries a smaller probability that the lift is positive. That prior probability sets the asymptote that no sample size can exceed.\n", "\n", "## The cost of an under-informed prior\n", "\n", @@ -988,15 +1033,15 @@ }, { "data": { - "image/png": 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The generative model already carries the design prior and the inference model the analysis prior, so the loop runs the mismatch with one extra argument." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "142452a1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "data": { + "image/png": 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p5ZZbrqMP/A/qd8oky1133bX2+7/4xS+WV7/61aWpjTbaqJpkWcf2229fvvSlL9V67wknnFA++tGPlrrOPvvsKQZhZhJRJqfllYkN47HxxhuXb37zm+P6t5kk8ac//al6jYWLZyLTcwWLN5FJNJk4s8kmm1S/Vzcme379618v3/jGN2q/P0HyU5IJXylLf/rTn054IZBMLEpoZybFJICwH2SyV8qg3/zmN+XSSy+tJnq1JRPC1lhjjeq3T8hUtyaE/fGPfyxnnXVW+fWvf/2cv+V4ZaL9ZpttVi3wkAluw+TEE0+svWhFwgeOOOKI2vvO5LGtttpqwtfQ7373u2riYV2vetWrqrKvju9+97vVuTze86au97znPeW9733vZP8uk5lzvf7whz+sQk+nFPgyJVkQYocddqgm6GWiXq9lwmwWWPnVr35Vfa9MbGsiE/8SVLrppptWbd7UQZ3WyTZBJv8df/zxVb0z3jbBpNLuy2+9xRZbVJN3B1HT49tJmWiZc/f5pJxNeduJfaQdlzbjSSedVE3InYjcD6Q9sscee1QLxvSD1A1nnnlmdW+U+nyiZdyzZSLsWHmQwKtRlzLlxz/+cTn22GOrNu1EZIJxjmPuAZ+rjmtS/zW5P5uoYW0vpi2f7/Tzn/+8CpRKoEPbEmCUPoaEa2VSfeqalFVN5Z4097W97mOYnJRFv/jFL6r2V/pCJirHaJdddqnq4yn1xXz2s5+t7svqqvO5utFuTR2WeioBxYPebu2knP8JfEg/Tcqk1O8Jm09buRNybMfazGkLdCOUppPtobQfcp6dfPLJVYDeILaH8h2OPvrocsopp1TBJhP9PXfaaafyute9rlqIp5/7cXvJdTY611lCilKmjgXpJCQywVdjC7m2LUFIaRO9/OUvL5tvvnm1EFanNb2mjzrqqKqPekoS8pTfLIFndeS3/uAHP1g6JdtOeVm3v/qXv/xl7TDkQR3Pi7RHEzxbx1prrVXVU23KNZk2du7L0t+boKs2pR85/W1j9y5jIVn9sFhuv0vgYQLg0vectmnK0YSQPVtC1xKel/IkZUaO8yC3KdNPkv7/U089ddx9bOmzfPOb3zww192UyvwzzjijKhtzLbZVXyY0coMNNihbbrllednLXtbxEOtO9inkXjb9LEceeWQVLDmesj/lecZcJ0rZ2NwotAXHIwu5pAxJmTbRe8xcv6997WvL61//+ikuipOFBd/1rnfV/nyf+MQnaj+3Nmh1wUQk8DNjhunfTXszdXLT83O99darzs28EtbeT1Ke5t4m3znfN/1hTaSNn/beWD/9qC7qNDnq+/6q7/vFMPdHdrKuGLWxu0nlvjDtq4xjJLA77ayU3W3fz49JX0j6RHK/mbK9yVhWmzJ284Mf/KB6jmWidXXKje222666V8xzDFNqj+Tf1JV+ube+9a1997xtjlXK2Dz/lGeXxyNlyH777VfaMAzPpk3UsD4PAQAAAAA8v2nH8W8AAAAAAIA+lYf0M+GqrjzkvNRSS5UVV1yxmmhZRwINexE8nwegv/3tb1cTFzp9jDOBKK9MDhibjJeQwlVWWaUKi25rctowfieay+SKL3/5yxMODh3vpNeERfztb38r3ZTvkkkbeWXSRcL2EhyXoI9BklClb33rW9XEu7oh/ZmsmElkeSU04jOf+UxZcsklSy9kAuT3vve9anLJk08+2ZF9JCQ5r0zoy0TmhEtkIvmUJh42naiX/SQE6+qrr25125l8k1cmVyU4IJO0Eqg/DNZdd93a7/39739fnTu5ruuoMwk311DCDLKIRd3rOKFQdSc1rr766qWXEvyy//77V5M568qCGWl/JGRszz33rCbqdTr0ZUrhx5mMedxxx417UuF45JwcK2e/8pWvVBMyd9999ypYbJBk0m7K6EMPPbRRIMoVV1xR3v/+91fbSaDtoNW9PPe1/LWvfa1qO9YNpU17JvckeWWC6Mc//vEy33zzdf2wp2xPwFUCndsOaEio0OGHH169MvE397J165BBlt/6+9//flX+J3ypbjBJFl7KK5OJsyhdL86XJoa5vZg2Tur23Kt04l7y2YEHeSWEcNKQkdQxCTJ64xvfWIZJwlEScNLknEl9nFcCGz72sY9V4XvPlmCXTv923ZS+h7RbmwQZ9Eu7tZP139vf/vaqX7BpkFidY5uFTfPKAnFZnCv9NAmmGSTp3/rqV79a9VfXXaym1+2h3BcdfPDBjdp0+T1T/6S9k0CZvNL3i+ts1K6z1Bdp5910002th4E/Xzhk7mPyOuCAA6o2YMrULMQ5qPVWFp3K4i91F5xMm3uvvfbqyILOadM3CYBLaF2d0Hnjee1JP2DOrSyy2KkQ4EiZdeutt1avc8455z9/vtBCC1UBfAmo3mabbcqg6eTCEwkkSwhcwhDHMwaWkLa8EtyW8i/3hAnPS5/BIJV/KcdT1zUZ+xtEqeMPO+ywqo7vRFBkwjRTH+SVwMsEWL/hDW+o6phBknGw3McnxLuTlI3NaQv+/2vvoIMOqp4Lqtt+zzbSH5P6On2c6bt49uLyGYtr0o+Vsref9LouyOIZ2XedBYaf73ulHZRX+jd33HHHKiS/E89qTETGKNIWzAK5TcPmJ5X7wLFn49LXkrD99JEktHhUqe/7q77vF/oju2dYxu4yjpC2Qa6RTi2WOzmpkzN+klf6v1Oep88t95zTTz996baM4/3f//1f9Xumf6qO3Hvl/cccc0zZeeedy957711mn332//o3OcZNzpfJLRzXS2mTZuwiz9x2sh9oSkbt2bRhfh4CAAAAABi/wXlyCwAAAAAA+B8J42syAW+rrbaq/rv11lvX3kYeHG77geTnkklmmaT+7ne/u+MB7VOSY55JRgn/bGPS2zB+J9qZXPnOd76z7Lvvvh2bbNSL0PnJTbo47bTTymtf+9ryvve9r9xxxx1lEJx77rlVGZoJ1m1NNr744ouroJtf/vKXpZuy8EgWnEjAQwJzOhU6/2yZPJTAkgSitSkTiDN5PgsaJNS+k3VU9pWJ2Tl2OX8Tqj/oFlhggbLEEkvU/k2bBKBfcsklXX1fJASsbtDCqquu2rNQlBzr1A+ZRNnkmD97mwkZSGBE3RDiOrKvQw45pAp3yqTMNif2TS4kOZPpUn4n/CgTQQdBQp4S/pmQi7YmXl5zzTXlda97XRXszeD72c9+VrbccssqlKOtyd2/+MUvqsmwWdyjm7Kw2rbbblstkNB26Pzk6o+0t3MvlmtiVCRkL9d/ysG2yvu053MOnnnmmWUQDHt78dRTT61CdBJq3qvg8rTxcz3XDSftR5mY/6Uvfalqf7V1zmQxjIQdZVG4fgvYakvKmSxMkYU+moTO90O7tdNyX5K+hm6Hzk8uhCRhXwlrSRmZEI5BkDC21EVNwvR63R5KP13aQW216fJbpi7INtu6/gad62y0rrPcS994441dDZ2fnNzXvPe9762C27t9f9WmBATXDY5OWGmTcPjnkoDruu2BfJ/tt99+Qu8xnteeXJsJNc+9S8YmexE2FhmTynh76l/+LeOGH/jAB6pxu9zv1x0DSzn8wQ9+sCr/Lr/88oE4vAlOzDhdm2N//S5laBZA33TTTavAw06Ezj/b7bffXgXxZZ+59+h1XT0e+YwZx0j/TidDaJWN7dEWLOU3v/lNtahKFg5to/2evrFvfvOb1XjVMN9j9rIuSLv5c5/7XNVvnYVjO9m3+8ADD1QLjqQtlvDXuoG5TWSf+Z6pD1IXdfJZtfyWadek/Z/Fz3KsR4n6vr/q+36iP7J7hmnsbixsupuh85OT8f2Mr+XZ9071Oz1XWzN9mt/97ndbqUPTVsuzO+l7zfOjw9zfkD7GAw88sOv9QKP2bNqwPw8BAAAAAEyM4HkAAAAAABhgCfqoa8455yzrrbde9b/zgPNUU03Vk88x0YmRr3nNa4bq4fph/E60M9HnjW98Y9cnxfRaQsgTbNbPgZWZKJHJL29729taD0yPTKrJBIxMOOu0BJDtv//+Zccddyy//e1vyzDIxJVdd921mliW4Ipun78JczvxxBPLoFt33XVrv7dJCHzd8K0m+2zy3nXWWaf0QkLTMin++OOP78j2E3b55je/uSshnhdeeGF13SSoopuTCjPxM2EKCcfIhNB+lrCfTLzsRNhCFhr59Kc/XQ499NDWt013JChjv/32q0LaOxEKleCNLE7zu9/9rnRaQm3TBnrXu95VrrvuutJNuRfLPVnKhUEIumoaepnAt04seJZ6Y88996wCYvr5OA5zezEBD5/61KfKPvvsMzBB0YPiwQcfrILTE4jZifM7E/933nnnjoYc9ELCeRK80al+w262W0dRgk4SAplyq5/7LdO2z+IGeXUivKNb7aG0+3ffffeO9DXlXmKnnXbq6/62UeU66/59Ry9l4cCE7xx88ME9D+aqY9FFFy0bbrhh7fd3KtQ7geF1ZYx0wQUXHPe/N57XnvQDvuc97ylf+MIXehY4z3OHFCeYtc3yL2Oe3/jGN1pbuKQTskB7+mFHJWQ0Lrrooqq9n7DEXiyGlrZvAvByX9fPC5OnHz33GxnH6GQdrmwcbt1uCyZQNgv2dSJce+weM4tIDZte1gWnn3569exe+ge6GXiffrUsBpK+yU4Gvz9bwk8zLpPFJbvZl522SO5N8lxS2j2jQH3fX/V9P9Ef2R3G7rqz6HoWm8/Yb56F7LSf/exnVXsh+23bPffcU/WT/uAHPyjDJs+6ve51r6vaW902as+mDfPzEAAAAABAPYLnAQAAAABgQOXh4LoBrbH55puXaaedtvrfCyywQFlttdVqbysByZmA00mZ8PXud7+7I4GKvTKM34nm7r///iqAIRMtR1FCwhJY2Y8huJkM8oEPfKD6bJ0M08xk14985CONArGfTyY/ZSJvJp0Py6TJX/7ylz0PxMvkpPx2++67b8frxWELnr/ttttqB4skrKFu2GST86XJcarr8ssvrybidXrifwKJUxZ3qqzLdhOukQmT3Z7k9uz2dCaE/vjHPy79Jscoi4N87nOf63jARBZUOeWUUzq6D9qXci/BMT/96U87engTOpWJ2pkI3Mkw9IR8ZBJqr+Q6O+CAA6rFhToRWtsPzj///CrQNgHanZSwsk9+8pN9GT4/zO3FHO9s90c/+lFr2+Tf7rvvvioYrJP3Z2Nt2l122aX87W9/G4pDn+OVdmvaW4PcbuXfYZAJgjz55JP77nCkT3WPPfbo+KKonW4P/d///V/V7u/kefzoo49WixV1awFZJsZ11p37jn6Q6/zb3/52NS7UzTDJtqStUlcWPm27Dyj99eecc07t92dRrvEyntfu75ZF584666wWt0obMvaVvqYHHnig9QOacaivf/3rZa+99qrOgX6TALj0VXfiu/fz7/2mN72pp/3zY7L4TBYt68dFaFJ3Z2y40/dDysbR0K224EEHHVSNL3X6HjNleoJXh0Wv6oJc/wkF3XvvvatnhXrlqquuqhY+70Yb7Zhjjimvfe1ry7XXXlt6JWH3afd89atfLcNMfd9f9X0/0R/ZHcbuuiuLPKfvqpOLFWccNuVFJ9tyGT/+7Gc/W425D4uMFeZ56G7ff47is2nD/DwEAAAAAFCf4HkAAAAAABhQTUNitt566+f8/xNx7733ViF2nXLLLbdUk+wGMYhjlL4T7Uwqfe9739vxQOFBkBDco446qvSLTIJImF23Jk9nfwkE68TE5kwsyUTe6667rgyLww47rLp2MnGlHxx//PFVeE4m3Q+itddeu0w99dS1g9HrBBkkdLhJaM3vf//7Cb8vkw3rLvIx88wzl5VWWql0UxYcestb3tK10IG07Q4//PCOhMmlPE24Rj8EhKYtloDkb3zjG6WffPrTn64WB+mWHIMsSsJgSH2XkNULLriga/tLiEwn7l2y6EEm+uaesh+ce+65VehWQkeGyZVXXlm1lR577LGu7O8nP/lJ+fznP1/6ybC3F/P9LCLSvpwvCSD6y1/+UrohfQEpgwY97DD3nKmnOr3QRafbrfz3PVdC0BJY0S9yP5f7o4suumig20OpM7/2ta+Vbv6OZ555Zlf2x8S4zjp739FvEpaekKp+6BeZiPXWW6+88IUvrL3YatrAbd+/1Q0Tm2OOOcomm2wyrn9rPK9duVe88MILW94qTSV0NeNynS6XsuhgFhrpp7I+4XP5TIM6nlOnPN5vv/268ntPRO6Dcx/Zb30bCfDuRvtZ2ThaOtkWTEDpd77zndKte5gPfvCD5Ve/+lUZdL2qCzLmm/GRBOX2g9yTve997yvHHXdcx+qgLLz3mc98pi/aArkGv/Wtb1X1Ys7nYaK+78/6vl/oj+weY3e9WUgm9xWdWPDs9NNPr+qwbt1HffnLX64Waxl0GSvMWGu3nwcZxWfThv15CAAAAACgPsHzAAAAAAAwgPIg9Mknn1z7/fPPP39ZY401/uvPtthiizLNNNPU3uaJJ55YOuXjH/94eeSRR8owGcbvRDsTRhLQxr/tv//+tcKkO+HDH/5w1ydN//3vf289fOySSy4pb3vb27oWXN0NmQx8wAEH9MUkoUn95je/qSbzZBGBQZPgpeWXX752OEidYM6cm03UeX9C8utOIl9rrbXKtNNOW7rlmmuuqSbidXsy1v/93/+VO++8s9WJdLkuEjDUb77+9a9X5Uk/OPjgg8uPfvSjru4zEw+/8IUvdHWf1L+O3vOe93S9jXTttdeWo48+utVt5p72Qx/6UEcmfjfxpz/9qey+++61Awz7TUL03/GOd3R9gnHOlx/+8IelHwx7ezFBmN0KLR41++67b7niiiu6us+bbrqpWqhwUMOOrrrqqqrM6dZCF51qt/K/ck5+5CMf6YvFitJ2SCBcQmQGuT2UPsAsONXt4LH8jrlW6T+us87cd/Sr9I0kGHSQTDXVVOWNb3xjo7ChNts4Z5xxRu33brPNNmX66acf1781nldarft+/OMft7hF2gom62a/aBaOymI4/XCPnvvx3Jffc889ZVR86lOfKj/96U9Lv/Z7pq3aL+MHWXjr0EMP7fh+lI2jqRNtwV//+tflK1/5SummtO322Wefcv3115dB1au6YGxBvSaLs3fqN037t8kzic9VB/Xj/V7qxQTBDhP1ff/V9/1Cf2T3GLvrnTxH8cUvfrHVbf75z3+unh1N/3o35Rme3/3ud2WQ5R7vhhtu6Oo+R/HZtGF/HgIAAAAAaEbwPAAAAAAADKBMPPvrX/9a+/1bbrllmXrq/x4mmHvuucvaa69de5sJZH7wwQdL2y644IJy0UUXlWEyjN+J5nJO/OAHP3AoJ5EJB/0SRnraaaf1ZL8JYbnjjjtamwSV0PluB1d3UoKZv/rVr5Z+dd5555VPfOITZRCts846td9bZ4J80+D5Oot2NNlnk+NTx1lnnVUeeuih0m0pL775zW+2tr1MxMyksn6V8uSkk07q9cfoWZ1zzjnnVAsy0N+yME3uJ3rh29/+dmvh5TnfEujcbxNfJ2037bnnngM/ATbHN5O577777p7s//Of/3zptVFoLyZ0PhPoaVcWTmgSZtpEyvkzzzyzDKKU771YaLHtditTDkVLvdJrqdd6tXBjW+2hLAj4wQ9+sCeLTOR6OfDAA7u+X8bHddbufUe/Szu128FLTW233XZl1llnrfXeu+66q2r/tiFjBml31LXDDjuM698Zz2vXQQcd1PIWaerss8/uye9y6qmnliOOOKL0WoLZRmlBngTc/eQnPyn9LO3jD3zgAz0PYs5Cjt0a31M2jq4224JpZ2UMrhf97ekH+tKXvlQGVS/qgrRls+B4v9ZBOY/222+/cuGFF7a2zQSh9nMddNxxx1ULWw4D9X1/1vf9QH9kdxm76/0YX1v1WPpr99prr548v5pnFbq9eG2bTjjhhKrfodtG7dm0UXgeAgAAAABoRvA8AAAAAAAMoKYPHW+99daT/fOtttqq9jbzYP3pp59e2paHoofNMH4nmvviF7/Yt8GbvXTrrbeWY445poyqTCA6+uijG2/ngQceKO9973uHKrTpiiuu6Isw0+dz/PHHV69Bs+6669Z+70QD3RMaeMstt5QmrrzyyvLYY491LXi+yfEZNCeffHI16bqp733ve+VnP/tZ6Xef/OQny/XXX19G1fe///1efwT6WNoTmRzcVBZRS9Bqv4e6n3/++QMfYnziiSf2dFJ1r3/jUWgv3nfffeXnP/95659p1N15553lK1/5Sk8/Qz8swDaq7Vae22WXXVZ+8YtfjOxhaqs9lFC+BAOOah3Nc3OdtXOdDYIsHtTrNsdEzTLLLGX77bev/f5jjz22lc9x7rnn1l7sZoUVVijLL7/8uP6t8bz2XHvtteX3v/99i1ukDUceeWR5+umne3IwDz744PKnP/2p9EruHbLYyaj47W9/OzD9XKkfEz6fPo9eybnRjftLZeNoa7Mt+KlPfaoKFO6VQe3H6lVd8OUvf7nnC2yM5/x8//vfX+65557G20o/0mGHHVYGIbC9zbD9XlDf92d93y/0R3aPsbv+OefbuN9NoHfTZ9pGsZ0VX/jCF7q+z1F7Nm0UnocAAAAAAJqbtoVtAAAAAAAAXZRQ1TPPPLP2+xdddNGy0korTfbvNttss/LpT3+6mkBWNxB/5513Lm15/PHHG4XkzTTTTGWTTTYpa6+9dll66aXLggsuWGadddYy44wzlqeeeqp6KD9hQffee28VKnbTTTeVG264oQqvvfnmmzsy0X4YvxPteOihh/ruUE4//fRVAMuLX/zissQSS1Sveeedt8w999xVwEz+ftpppy2PPvpoVTbl3EuQaCbbZKJsJmU++OCDjT/H4YcfXt74xjeW6aabroyiU045peyzzz5lmmmmqb2Nj33sY+W2224rw+Lhhx8ue++9d+36alJrrLFG2Wijjar/LrDAAmXOOeeszudMor788svL2WefXc4555xGC0N87nOfK2uuuWZZbLHFyqDI8cg1V+cYT3SifJMA+DH5nAkuGm8gfMqt1I115BxZdtlly6hI2+WMM84ob3jDG2pv46qrrioHHnhg48+SOiftoJe+9KVl5ZVXLvPMM0+ZbbbZqjo0oY2ZTJ+FkLK/unL9J0ghE95Gsd751a9+VdXnL3jBC3r9UejjIPO0y+rKPUvq8Dbavmmn5h52rbXWKossskhVPqc+yL1QgstSf+feuUmwakInUuasttpqZRDbS22UvYNqVNqL2W+T75j95TpaddVVq/6i+eefv+pfyL1e+hfyPRO8ku+aRcHSr5BQtNS1udaGeWG4YVq0a1S00W7tdzPPPHPVR5M6MH00iy++eNUmnmuuuaq/y7WbvoPc7+SV0Lmxa/eiiy4qF198cfXnTaV+TNkxqpq2h/7whz80XliWznGdDcd1Nh5TTz11WXLJJavxshe+8IXV/55vvvmqMjX3xClTx9pEaRck5Pz222+v+r8T6JOFqjIG01Tac2lfDVJfU36bH/zgB7Xavxkf+/vf/14d6yZS59e1ww47jOvfGc9rV9OFa3KNbLzxxmWVVVap+gDSBsqYafoL81uljZN7l7FFTtP+ufrqq8uf//znVsapaF/uZTN2lcVGmox/1dVGu3hQpB/uQx/60EA9J5D+/gTxfe1rXxvqcXtlY+8MU1sw28iLwagL0pd89NFHN95O+nC32mqr6tmxl7zkJdW5m2fH0heUczXn6GmnnVY9M1ZX2lYf+chHqud16sp1su+++5ampppqqvKyl72sbLjhhtV4Ta7X2Wefvbo20/7L8wYZD0rfV125v0l9meM2iOOk6vt6x2xU6I/sLmN3/35GPc/0jLWz8oxz6qo55pijzDDDDFU7K/eEqYvT1rrjjjuqsYzcw5933nnVPX1T11xzTfn1r39djfE22cYPf/jDxp9lVHW7nB21Z9NG5XkIAAAAAKA5wfMAAAAAADBgzjrrrOqB4bq23nrrKf5dJk7lYem6kxIT+JqH/hN81Nakl0zkrOOVr3xl+cQnPjHFyWCZwJ4JDJl4t/DCC/9PGH+O8WWXXVZ+97vfVZMZrrvuutKGYfxODJeElm266aZlgw02qMIHc049n4TQ55VA+kwYil133bUK+UxoegLJEvRRVyYw/PKXv6wmzvabTCrJ8VpnnXWqkMZMkEpwzl/+8pcqIOn6669vvI98/wR5Z9JwHQnzaRpc8Gz5nvnOCfleYYUVqskmeSXoZSzgJROaMwErdUMm3LQ5meib3/xmtf0mEtaXMjVBNc+W8z4TlXM+v+Y1r6nCaxOAku9TRybJJbzyW9/6VhkU+S1zbOqEwo+F+6Q86VbwfOQ6GW/wfM7LuhOvcu5ncnu/yLm85ZZbVpM1Uw6l7M0k/gSI59pP8FJTmahfN8Azk86aLGw0aTsok+7Sxnm2TFDNK+XRHnvsUU3wyz4TrlBHQj6OOeaY8uY3v7n0k4RIvPzlL6/q6Ez0S72b8vbGG28sJ598cvnjH//YeB/5nTJh8NWvfnUrn5nuyjmxxRZbVKEbKQ9yX/C3v/2tWmgjIV5N687IthKuk8nidfz4xz+u6tUmMhH14x//eHUtPFsmqud7pw5KufGe97ynCqlK2G7doPzPfvaz1YTfhBENku9///tVO7INmVyd+i+TjRPwl1CXtPnS7k2QX+qJNgIA2jQq7cW6QTpp6+Xa2G677abYrkm9k1fa/vmeWeRhUikL0r+QV4KM6ta7/SYhAT//+c9b217Ko7TVEhSeayfHNNdOjl/6+NIWTlkzCvq93dqvlltuuSrgIvXeiiuuOK5QztSFeWXxwCxcGbvvvntVdv/kJz+pAsNy39bkOknbc0qLm/bSILSHEnzSJJxjUvl+qZ/XX3/96vumbyblcX7f1BHpT7vvvvta2dcwc50N33U2JWnXjJWp6623XtW3+3zG2kTpd8jnSbv4ta99bfV3uc/4xje+0SjcMH70ox+VT33qU2VQZAwwY4kZZ5qo1P/pN3/7299ee/9pM9Qdx0w7PveK42E8r111r5Nce1/4whfKK17xiue8v8kr/3appZb6rz7i1LlZoDvXaxZLvuCCCywy1UdyD52+omFrw/ebr3/9643a/5NKYGQWoco1mb763HOkbM99bsKNc2+X/uUmz7OMyb15rt2646ODQNnYXcPYFkw9d/DBB5c2F+LKPWbaemnr53vnHjPXePoAc30PSx9gLyS88/Of/3yjbaQvd5dddinvfOc7q9/n2VIu55U+7Xe84x3l2GOPLfvvv3+17zrS5k/fQp6JqeNLX/pS4+dEcj4mAH+sj+vZ7fv0hSyzzDJVeyLPpaSfvu59ac711JvZxqBR3/Nc9Ed21yiO3Y21IdLOSrmd/sPnk3H9vPJdFlpooSrweuw5lYxBZMw0dVDTdlaT4PmvfvWrrY3hpc7Kscm9VO6rUl+njsy9WoK9M77V1n3bKBrFZ9NG5XkIAAAAAKA5wfMAAAAAADBgEsjQxHMFz0eCnesGNsRJJ51U9tprr9KGuoHNeUD/gAMOaBQKm7CUDTfcsHp9+MMfriZlZCLDqaeeWv785z/X3u4wficG33TTTVdNmthpp52qsPm2JKAyE4I233zz8oEPfKBR2XLaaaf1VfB8vttb3/rWakJvJkE9O6wqE6kywSSBbglFqTuRd0wmhNUJVsjkp6aTlyeViU8JBdphhx2muCjBWMBdQmET8LLbbrtVoRPnnntuFdqaUMGnn3669me49dZby1FHHdXgW5QqECCTZsazsEK85CUvKT/84Q/L+973vmpiXh353oMWkJHwhLqh8HnfeIPnExjf7cmbTUIgxhtu32mZQJ/yJefns6UsT5mZ4Ld99tmncehFgvpTjmUC60QlEP2KK66ove8EPX/mM5/5T5DHeOS7J9w14Zp1J9llgt72228/xQV/ui119Ac/+MHJBqEkIOWNb3xjFV673377NZ48nDqnn4PnE26x8sorT/HvDzrooOpY1JFA4IR1N2lT9UImoaet9frXv/5/wmhzLWTBgne9613VBM7/+7//azw5OvVZnQDIBx98sHzta19rtO9MxM59Udoa45Fw4yOOOKKaAFv3Xjr3SrnP7efrYnKTdo8++uhWtpXzJxOms+DF5CR0dO+9967aOglDSDBLr41Se7HugnJZUGGbbbYpTaQcyCvHKuVKFro744wzqtcghxy3Ndl9nnnmqc6h3A8/WwIJIiFR6ZvJYhoJwRxWg9Ju7ScJacl9d66vZZddttU2QwIs0gf07ne/uwrUrSv9if0UPD8o7aHcGzU9zyPfMb9l6p0pne8p51M3py2U8LG6i6EOK9fZ8F5nU+rjSr2bPtvxtu/GI+22BHzl++Y6qyvtp7QbBmmxqxzPOsHzcdxxx5W3ve1ttce70s/8yCOP1HpvwmYTaDYexvPaVffeJddXFrqoK+dZwujzShhp6sO0vXPdJSC77rk07BLsmkWjJg0eTr/OWCBejl/69dtYTCf1Rtq9bZbP/H+550x/RVP5fVLPZ+xzcn2g6bd+0YteVJWzWSw1Y5MZ220qgcVNn4/pZ8rG7hjmtmD6ExNY2VT2nXoy/byzzDLLZP/NtttuW9Wjhx56aPn2t7/dONRzFH33u99tFA6ae7IE4Obea7zPtOT+LX2Recaj7vhhxmSyz4mOgaWtkBDdJt773vdWfVjjvXfImGnC9nO/kdDiOrIoTsZds+DWoFDf81z0R3bfKI3dZdwnZWYWp0p/a1sShp0FftKH1CRMPGO2+d5ZpGSiEq7d5FnXZz+zn+cVJrdoTGScbN99962C8rNQhL6KiRu1Z9NG6XkIAAAAAKA5wfMAAAAAADBAEuDWJIgqk8QT9vRcNt544yqspm5I8imnnFL23HPPRgHpY+o+jP2mN72plf1PKhMy3vKWt1SvG264ofz0pz8tJ5xwwoS3M4zfic5LqEMmQueVMO+55567Ckd5/PHHywMPPFBuuummakLxb3/722ry5niv30wa2HXXXatJl/POO2/HPn8mJydkNKFpf/nLX2ptI2VfJjL3Q/BEvk9Cu1ZZZZVxBQXnN8sEkyZhS5kIVkcmjt1yyy2lDfkumQRV5zfIpOaNNtqoet18883VBPyE1NUJJcmE7CYT2TPpJ5P1Jlqm5nc/5JBDqvM436GOTMQfpIkzCVivG5SQsug1r3nN8/67THCvO+ny2TKBbLzlRJOw+34Ins9k04R3Pt93Tf2REO2UQU3akLnm/vSnP5U111xzQu/LNX7YYYeVJlLuTGRi36Rh05mgt+OOO1b15USlfk2wQIIYeikhDgmRSGDP88m/yYIP+c4Jne52ndMtCTGaXAD/pH/f5HgnIGmQ5Do/8sgjq3P++SbKJpwji8h89KMfbbTPnCPjKeOfLddUk4UR1lhjjapNOdG2SNohCbu67bbbai+okjo8E37bvifrlAR7NV2EIt7//vePuxxMO2/11VevFh2qG+jSllFqL95xxx0T3kfu/RI63aaEzyZoK68sgpIw0AT8pK9hkCT8/Oyzz268nSxGljbYeIIs0jeTf3vwwQdXoV3DZlDarf0iwSw5BumrmX322Tu2nyyMkPMtv0/CQ+vIdZ7rvR8MUnuoaRBIZAHE73znO+O6N007KP1+6VPMeZX7nFHnOhv+62xM2nJZuCoLJadu7pTs5z3veU/Vjkj7p4603dOv1eaisJ2WciV9EHX6nhOKmEXv6vaxNQmvTFt9vIzntefRRx+tFXCXhQebhM5PTtqlY4t0f+pTn6oWL8wCwvxb2qBZvHxy5W/akHklVC3hxAkKS3sw13QTaY9mfHrnnXfuq58hz1SkrEuAbRbjS0Bg+h0TAJhg9SxOkf6PhK3lv00WXO6k1N9Nw6HTfsr92nMtBjqpHKuEJSaU7otf/GKjfWdByNwj9sO4zKT9PzkvUo7kesj3zdh9zoGHHnqouiYyHp5xqAsuuGCK5Z+ysbNGpS3YxsISGRfJ2H3O6fHUo/m+KR/TD/zwww+XYdZmXZBrvkmfQPpfs/DyeEPnJ5XnWhIe/9a3vrXW8xnp6/75z38+4T7lpn2d6SPL+TZRaa9kYZvtttuu+p0mKvVmngdK+OygUN/3b33fD/RHdt8ojN3lXj0L1mTbnZRnhe69995qDK2OXLO5Rusc2zbaWWkrZsx5PM88jS0ElLZH/pv25TDL87QpY7Pg3cILL1yVsemTyLNOGcdIOyvh//n9sojyc93XjuKzaaP0PAQAAAAA0NzULWwDAAAAAADokoS6NwkuzsPC43kweDwTCp8riOHiiy8ubchkvToWWWSR0kkJo8yk+/POO6+suOKKZdS/E52TCZGZzHjOOeeUz33uc2WzzTarJkZnslEmAM8666zVxItMwMiky0woSGBLgjVXWGGF591+Ji1kYlEnQ+cnnYTcJGwok0oSHtdrYxOvxxM6PyZhCAn5aqJuYH8mxDaVCSb77rtvdS62Efy/xBJLlK985SvVYhcvfvGLJ/TeTNY8+eSTa+97oYUWqkIP64bG5prbf//9a+8/k84HKXxzpZVWqgJF6hhvsG8m4taZ4D45mcD1xz/+cVz/LgEOdaTMzeS3XnrZy15WvvSlL437ehwLInjBC17Q9XIoE1czGbCuzTffvJpQWVfqzDqBBJNOJH3yySdLr6SsSp0+ntD5SReaSpndRNrzddusdFcm3h5++OHPG/44qVe/+tVl22237Xp5kGvpmGOOqb3P3KdmQnndtkjKwi9/+ctVW66OLPSUe6VB0aS9NOniZxOd4Jy6JpO6E6TdK6PWXqxTXqc908lFFMYWnUqw0KAFOObcaRoWmPM/9+bjCZ2fVAI6+i1ocZTarf0g3z8LH6T92snQ+TE5R7N4aF0JlOiH4JNBag+lzG66uEXCZxLiOdHgzdwbJXQtofWjzHU2/NfZpDLekfZIJ4NGn72/LOpcV/r1B0nak69//etrv79uMGv69X71q1/Vem8WREhw4HgZz2tPvx7LXLMJnzv66KOr0KxRl7HC448/ftyLfiQk7KSTTprwOM/k/OhHPyr9IsGiCW/N4skf/OAHy/rrr1+No6f9nPvd3K+kTtx0002rQOuMdf3mN7+p2tbzzTdf6ScJhG666HzaTwnRG2/o/KTe/OY3Nx4fjfwe/SD3thmPz7jHN77xjSqUMM9VpC8p7eyZZpqpOgeyMOPrXve6qt2eRePTV7bxxhv/z/aUjZ01Cm3B3JM3WbxvrE2XcfuJPiOWMeTcY9btb+93nagLTjzxxEaLxaa/Pn2uTfrpEojareDqjA+nr7uuLF6SY99k8bQEyDZ5PrONxX27QX3f3/V9r+mP7N1xH+axuyxwmnu4TofOj8nCKbkGu9nOSv/TGWecUZpKXTbRMPOMM+Z5z6ZjZf0q3y99ML/85S+rujqL6uQ5pyzwkfM0fdH5N1lE6p3vfGf5wQ9+UIXP5zmoPHM6OaP2bNqoPQ8BAAAAADQneB4AAAAAAAZI0/C4rbfeurWA+ueSSeZt+Ne//lU7LLNbk40SgDjq34nOSADKz3/+87LTTjtVk/rHK5PcEgoxnskMCbbvpnXWWafRPq+++urSa5nQMtFwr7FwhUyOqSsTRvKaiEzQuOqqq0obE7gykbltmZx53HHHTSi06swzzyyPPvpoowlduUaayKIDEwkoeramIR/dlLCANddcs9Z7b7vttnLXXXe1FlA/XuPZXkLnn3jiiVrbT5hOL80xxxzV5OWJlieZoLf99ts32veNN97Y1TZh2iQf+chHSlMJQas7ITPncCYw9kqC+l71qlfVel9+87qyGERCtul/WUBomWWWmfD7EpjQ7fIgoQvjqRem5B3veEfj8K5MoN1uu+2Gvg5P4NBll13WaBuZtF031GXuueeuFizqlVFrL9bpY7jjjjtaW/jn+TSpj3oh9+BNffazn61935vQhE6Ha3bLoLVb+0ECHrp9zWSBwyYhcX/+859Lrw1Se+jXv/51ozoqcn+QYLs6Eoi12267lVHmOhv+66yXfd8J3GmyqHM/9H1PVMYh6i5amXCpifZ3RwI9s0htHWljZAGP8TKe155+P5aDeO/StlzLWWRkoouupuxLyGZCEpu45ppryrXXXlt6LSHpuSfPeOZEpM/qXe96V+NnLNqWhTqaLjCaBdLqjIuOed/73jehccAphdI9+OCDpZfSr5jxjg996EPVeT9eqXc22GCDyYbyKRs7axTagmeddVZ56qmnShNZLGrLLbes9d6EzQ7jPWan6oImY6bpc3/3u99d2vhudZ9Zyfj6RAJC832b9EF//OMfbxzAnHN7SgG1zyf9N6effnoZBOr7/q7ve01/ZG8M+9hd6qVuynOzW2yxRVfHMbK4T9N7kDXWWKN2/+hSSy1V3v/+95dhk7Zn6teJ/p555ivPrk4pHH7Unk0btechAAAAAIDmBM8DAAAAAMCASHjwX/7yl0YBv4svvvi4/u0rXvGKRuHjCepqGqITM8wwQ633HXrooeXJJ58s/WgYvxPt23jjjct3vvOdCU1kGwSZbPeiF72o9vt7HTqR4Iw99tijdujeCius0Gj/f//73yf0788444zSVELH99prr9LJyWEzzjjjuP/9L37xi9r7mnfeecvmm29e2jDehVymFAIwSJoEilx66aWtBc8vuOCCre3z4osvLr04Hm3I9Vh3olqTCV91yqDHH3+8msjcpC7M5O6mUpe+/OUvH7hrNp+77kTSlK1NF0mY6O9N92UiZ52FCWK55ZZrFLyTkL+HH354Qu9pUodnsm8WY+p1HX7uuefWXrikmxLy3zSY4AMf+ECjIOLUOU2ClpoYtfZinT6GlPFZAIr/9o9//KNxiHbCoJu0F/N7fvjDHx6Kn2aQ2q2jLL/R/PPPP7D9NIPWHkpgZhPpP9l7770bbSMhc90OYBx1rrPuXme9Viegv1/K1Lp9F3UX90oIW53FthOsVEdCK7NQ30QYz2tP3WP5hz/8oaeLQo6St7/97VX7qG7oYBshbXWv77akLySviSxQ0e+a9m3PPvvsVXB8E+lfSnhrE6kzzjnnnNIrCS3+4Q9/2GiMe3KUjcOn223B8847rzRddCTlXhNZMDZ9qsOiU3VBwkT/+Mc/1n5/FlBq49mhLHiZvpS6snhUN/rpV1pppbLyyiuXpvI7NlkUZlCe61Df93d932v6I3vD2F37ll122drvvf7668vTTz/d1Wsn/VAf+9jHGm1jxx13LMsvv3wZFglm//KXv1w9+9GmUXw2bdSehwAAAAAAmhuep6EAAAAAAGDInXTSSV17yDeTHzbaaKPa+3rkkUcmNOHsuYKa60ig7K677loFEvSbYfxOtGvRRRctBxxwQOuTLPpFk4nHd9xxR+ml3XffvXYIQNPJ5nHvvfdO6N//5je/KU3tu+++VYBxP0jd0iQwfJtttmntuzQJdL755pvLTTfdVAbFOuusU/u9zxcqn/Cwa665ZlzbGu+iD5dffvnzLtQy3rD7to9HUwkHzMTCXkzEjHvuuWdC//6iiy6qQuLqqhsgOTlrrbVW7ff2KtAmv3WTQMhu1zl03zvf+c5qwnJdSy+9dFfPkSaTfbMwWt3w4mdbbbXVageqN22LdMuFF17Y6P0LLLBAFZ7d1Bvf+MbSbaPYXqzbx/DJT36yfOUrX6nC1vm3nDsTDZ7oxHmf6y/X4SAbtHbrqBvkfppBaw81raM32WSTRgsFRBabTWAd3eU6G51700H+rXvR/jn22GMn9O8fe+yx2v006RvK2MtEGM9rT8Kr6wa4pr7P4tyDthDFIEnA2m677dZoG5tttllZffXVGy+k1ysJpc0CPcMk97dNF27IcalbFj67b2+8iwt3KvixrvQjHnLIIY0//+QoG4dPN9uCWXi0aV91yu427jF32GGHMgw6WRekDdtksdh+GTMd7zjPDTfcUG655ZaB/765xpqMdXeD+r7/6/te0x/ZG8bu2tfk2ZkEk0903Oh3v/tdaWKNNdZoHBqffpSEtQ+DVVddtXz84x9vNJ4zJaP2bNooPg8BAAAAADQneB4AAAAAAAZAAlRPO+202u/PA9uZJNepoPpOBOXHwgsvXPu9l112Wdlpp52qQJ5PfOIT5bjjjqv+rNfhU8P4nWhPrtUvfvGLZeaZZx7aw1o36DP+9re/lV6Zdtppy5ZbbtloG00nKU4kWCUhPFdffXWj/a277rplhRVWKP3ij3/84/MGindqcs+zLbLIIo0m4QzSIiLLLbdcmXPOOWu999JLL33ekPinnnpqXEENCcbLdfh8MpnsqquumuLf/+tf/ypXXHFFqWOppZYq8803X+mVlEFNzruER4znGE7JRMOdfv/73zeqD9dcc83SlsUXX7z2e+++++5y++23l27LZL+mgVFNCPPq/8nq66+/fs/uSyZ6jvz1r3+trqV+qMOziFCTMJ1BqMOvvPLKxoFDbUz6TluyrQUDxmsU24t1r+UE0hx++OHlpS99aRUWevDBB5czzzyzWhTo0UcfLaOo6f1T2qxtnEN1+vD6TRvt1ibv144ZjX6aNtpD3eynyUIfd955Z6P9Ne2XGrPFFlu0sh3Gz3VWRqZMb/JbP/HEEwO5KNCLXvSist5669V6bwIpM8Y1kYVW6wZJ1QlENZ7XnvRJ1r0XT0DcgQceWN1jZnHgb3zjG+Xss8+uzp9cNzSXvvcmiy6PabL4VPzpT3+qAtS6be65567G3YdNrpGm9WjT50Umvc9t2gZNn08vvOtd76rGyTpB2Th8utkWTKh30zKzrWu86XhWP+h0XdBkzDTj9E0Xm25rzDRj8ONpfzX5vm330zf5vnmuoOl4S6ep7/u/vu8l/ZG9Y+yuv9pZEx3LSD/EjTfe2BftrM0337zxd++1GWecsXoeuu6ChM9n1J5NG8XnIQAAAACA5urPKAYAAAAAALrm3HPPbRQ4sfrqq5cFFlhgQu9J+FhC4h544IFa+7zwwgurB/abhPq1EXickMOf/OQn1WvMLLPMUpZYYomy2GKLVQ97L7nkkuWFL3xhWXrppctMM81UOmkYvxPtyXXX5mSGNmTC8l/+8pfqlXMvIVm5tlM25JW/z2SGsVcn9TJ4J5Mu5pprrkbbqBvePenEpvHKBNhMhG3i9a9/feknTUMlVlpppdY+SyYepY6se04m+DxhLoMg33XttdeugkjrTHTOMZrStfN8wfSTtmNSzy2//PJV+MzzueSSS8rKK6882b/L++uGqSbcqJeahgyOnbf33ntvrfdPNMipyTWbNsyss85a2tK0/M012zSkeyLSlmvaZuxmnUP3bbrppo0nGCestVvnSN0FPzpRh49dH7fddlut9zb9Lp2WsjqhQ0284hWvaOWz5Bx92cte1mgRvYkaxfZi2kfjbVNNThYBStspr0k/exbbSX009kp/Q/oX0ufQqWCAXkvofhMbbLBBa8dmo402KkcccUQZVG21W+teP8MeQJq+mLF+mtRnd9xxR/n73/9eHnzwwervcr+T/oD00Yxnoa9B7adpoz3UzTZz0zImoZTpN2zDiiuuWJXzOW+YPNfZYF5ndaS8TJDUtddeW7WjU6bedddd5f7776/Og4ceeug/ZWrTvtbxuO+++xr3QfdCFjL67W9/W+u9WWA5fX/jUad/MmabbbYqqGuijOe1K/cuTRZhSRvv/PPPr15j0v7OQjKT3rfkvwln7WZf3qBra3GbLJw+/fTT126Pp+2a8ni11VYr3fTmN7+5cX3Vj5r2kaR/fo011mjt82y44Yble9/7Xu3333zzzVXd3M1FDrOvt7zlLR3dh7Kxtwa5Ldj0HjPldVvjnlm4O6GTdfvc+0Gn64Im4woveclLWv0sTb5n6vice8/Xj96kDpp55pmrc6qfxojbDGhtm/p+MOr7XtEf2TvG7v59f5f2VdpZN910U9XOSp9A2jsZy0g7K/XKWFvrmWee6ZuxjIy9NB1befnLX17aKqNyj3zRRReVQbXddttVz7J3yqg9mzaKz0MAAAAAAM0JngcAAAAAgAFw0kknNXr/1ltvPeH3JFAkwSI//elPa+3z6aefLieffHJ529veVupadtllq0DETOxsU4Kyr7rqquo1qQQUJEQtD1evuuqq1eSxNie0Det3oj077bRTzw9nrt3LLrus/OpXv6omrVx99dXVn/WDxx57rGf7bmPSRYKzm5hIsH8mbTWdGNJviyAkxLyJ9dZbrwzLd+m2BA/UCXbKxMCUJ2lPTM7FF188ru2MnYv573iD5/fYY48p/l1d66yzTumVaaaZprz4xS9upRyqGzw/0VCNJud53ps206hes22ESHSzzqH7Bq1d0vQaeu1rX1v6Rb/X4ZnA3/T6XW655Vr7PCnLuxk8P4rtxbRPjj766Fb3nTZcFhvL69nttQT/5HddZZVVqj6GLFDUdCGLfpGF1poGibSln9pBvWy31g0q6EYYXDfl+yTM99e//nXVT9NPdZF+mu710yRId8YZZyxtljOC5/8/19lw3HeMV8rRX/ziF+V3v/td+f3vf99XC6/1slxtunhV3bDR9Dnut99+VTj88x2b1IV1bLXVVrXKUON57cq9Q8ad2pSxq9tvv716PXvxg5xTaaPnviUhbelbblomDaMEmmWRsTYkrC3Hu0kgXsIxuxk8n+chdthhhzKMmi5MmAUc2lx4ro373HyntheofC7bbrttq23wyVE2dt+wtAWb9mOl7G+6yNSz+5QHNXi+03VB7mma/F7nnntuX/UVXn/99c9bFjfpu/rnP//Zaj9rU/3UDzc56vvBqO97RX9k74zq2F3uzdPOuvDCC6tFs/M8c7+YSJuvaTsrxz6L5LXZzhrk4PlOPw89as+mjeLzEAAAAABAc4LnAQAAAACgzz3wwAPlnHPOqf3+aaedtmyxxRa1A+vrBs9H0+D5hFVtttlm5dhjjy3dkICCm2++uXqdcsop1Z9lEsCGG25YXvnKV5Y11lijCmNuYhi/E+3IpJMEtPRKAt1++MMflp/85Cd9GzzVy4nfbQRwZqJX00lkE5nM1UQWqJhzzjlLP7njjjvKsGj6+/QieL6uTCacXPB8whOuvPLKcW0jdVUkHOiII4543n+fsPvUf5MLRRlv2P3k6u8s3tIrSy65ZCsTnrs1aTohCv1alwxC+dNGoMJMM83UtTqHwWyXdPMcGaY6PJP5n3rqqape6NfP18Rcc81VvdrSVnjdKJ5r420vvvSlL63C/R5++OHSDQn+SShXXt/73veq9lYWTEl7L30MbQYpdNtdd93V6P1tBhPMPvvsVQDkoN03tNlubVpPDYP8/gmnOfHEE1tfwLIt+mme6Vod3fZCoqmjzzvvvDLqXGfD1R/6XJ544olqfOQHP/hBtdBqvxrU4Pm0CV//+teXL3/5yxN+76OPPlpOPfXU6v3P5Te/+U3VFq3jNa95Ta33Gc9r1+abb17233//ri10/NBDD1V9wWP9wQmVTQBfxsy33HLLVu99B1nCxdveXpNAvKbhqROVe+q55567DKOm95Ntnxu55uadd95y9913N+r36Wbw/Pbbb9/xfSgbu2MY24JN7zHbDtjMvcNZZ51VBlGn64I777yzGlMYFuPpgx/FfvpeUd8PRn3fK/oje2eUxu5Sx/3yl78sRx11VLn88sv79jmTbraz2r6XanPR+G7LsVhhhRU6tv1RfDZNOwsAAAAAqON/Z1kDAAAAAAB95Wc/+1k1EbKuddZZp/bk+bXXXrvMM888tfd9/fXXlz/96U+liTe+8Y09DUbPJMAf//jH1edIGMExxxzT6PcY1u9Ec5nwk4UiehES9s1vfrNstNFG5etf/3pfT8bIZJFeedGLXtR4G938ffspNLHNsmtY5Drr18l2k7P44ouXhRZaqNZ7L7nkksn++R/+8Ifyr3/963nf/4IXvOA/52MC6CcXJj+5UKFrrrlmsmVIJjrWkYlwCQAd5DJoLGCpW+f4MIUodLv8GbQ6h+5r4xzpVnkwbHV46pImAVmd1rQtnzq/TYsttljpplFsLybge8cddyy9ktDIK664ohxwwAHVQmrveMc7qoWHBk0CJx555JFG22g7uGP++ecvg2jQ2q39KPczX/jCF6qFKxMS06+h86GfZvxGvY7uN66z0bo3Pf3006sydb/99uvroNEY5L6UhLvXXTjmuOOOe95/c8YZZ9ReaKNJSLHxvPakvTy5BUq7JX3RCaH/zGc+UzbYYIOyzz77TLYPedS0PRbUdAG6pgF/E7XaaquVYdX0WC6yyCKtfZa2ttl07HOii+d0I+BR2dh5w9oWvOeeexrta4EFFmj0/k5vb5jqgm6WXf3QB5/+4m7X56P8+6nvB6O+7xX9kb0zKmN3v/3tb8u2225b9txzz3LZZZf19XNw3WxntT1eqJ01ZaP4bNooPg8BAAAAADQneB4AAAAAAPrcySef3Oj9W2+9de33TjPNNGXzzTfv6efPhPftttuu9IObb765CiRIWPuvf/3r2tsZxu9EO8Hzvfj9X/va15ZDDjmkPProo13f/yBpI3C6m8F5TUMT55xzztJvHn744TIsEnIzaNdcFrKpI+E9k/vtxjupMZP9xxZryXW4zDLLjOt9CRF6tj//+c/ln//8Z6lj3XXXLb0022yztbKdbpVDCfEbJt3+Pm383qMc1jrsEtw4yyyztLKdbhmmOrzfy7imn62Nc2tSs846a+mmUW0vvu1tbytzzDFH6bVMDD/nnHPKG97whvKud72rrxc1e7Y22uaDfv2Maru13/zxj38sr3rVq8qRRx7Z01D3UWkPdfM8G/U6up+4zob3Optc23CvvfYqe++998AE8gxy0E4Wj9xmm21qvTd9dldeeeVzttV+85vf1Nr2DjvsUJownteuXI/90M7L/dapp55att9++7Lvvvv29X1+py288MJ9tb1uL/bXi7HZQRkn7ER7sWnbou74Tt3FiPOsTDcoGztj2NuCWUSxn+4x297eMNUFw9bOeL7vk7J6mAJg+/33U98PTn3fC/oje2uYx+5yT/35z3++7LbbbuX6668vg2CQ21mD3JevndV+uT2qz0MAAAAAAM0IngcAAAAAgD520003lT/84Q+13z/99NOXTTfdtNFn2GqrrRq9/7TTTqseEG7iox/9aFlsscVKv7j99tvL29/+9nLwwQfX3sYwfidK4wCTbrr66qvLTjvtVK699tqu7ndQtTGJp5sTFptOykhQUL8ZtokmTSeqdVvd4PVMLL/88sv/588vueSScb1/zTXXfM7/PyWTC7afXBj9oATPtzWRcOqpu/OYyKCd3/32fQatzqG72pqoPEjtkn7Tz9/n8ccfH+nAoX7+bTpZ/2TRqAQ8jC3W0w/OPvvsKjy7SZ/WIF07nQh+GNQgiUFrt/aT888/v7zxjW+s+ugYvvZQv9XRM888cxlFrrPhvs4m9cADD5Rdd921nHHGGT3Z/6hKPVbXscceO8W/S+h8nbDhBJynTdqU8bz2LLnkkuVDH/pQ6RdPP/10Of7446vFwrNQ8yhq+75jkILFezE22039FpbYxja72U/fzXND2di+UWgLNr0e9GN173oftTHTUfu+vaa+b2aY24KhP7K3hnXs7oknnqgC7I866qgyrPqtnTXIC/xoZ7V/7o3q8xAAAAAAQDOjNzMDAAAAAAAGyEknndTo/S972cvKbLPN1mgbq6++ellwwQVrv/++++6rQiGaBiAfeuihZYEFFij95Nvf/nb52te+Vuu9w/id2gh46JfAuF6YY445uravO++8s+y2227l/vvv79o+B10bgVzdnEzWdMGPfgwgy+S1YTJoE2eaBK8/OwQ+5+cVV1wxrvc+O2h+jTXWGNf7Emz/zDPPPOfnmMhCPquttlrppbauyW4FeLpeez9xtJ8mMNOf5UE3z5FhKxP6+X6j6bGeccYZS5tmmmmm0k2j3F7cZJNNysc//vG+Kv//8Y9/VPed11xzTRkFbR/7QQ1eH8R6qh9cddVVVVhLP9cx/aQf+ywGrY4exGPYlOtsdM6RJ598srzjHe+ofnO6Hya11lpr1Xrvz372sykGTtcNjX35y19e5pprrtKU8bx2JQh4jz32KP3ktttuqxZO+Nvf/lZGTdvl/SAFi+eeo5tjs93WdJxwhhlmaO2ztLXNbvb7dPvcUDa2R1uwN/1Og7oYcjfqglHrsx6179tr6vtmhrktGPoje28Yx+6ymNy5557b+ucaJtpZ/592VvvtDm0tAAAAAKCOwZyZAgAAAAAAIyBhqaecckqjbZx99tlV0EOT13LLLVeFVPcyQD+WXHLJcuyxx447cLZbvvnNb5YLLrig1nuH8Ts18eyA4LoeeuihMoiaLhIxkeO89957V4tC0N1JQd0MDkxQ9rBdR02/U79pa7GNbpl33nnLUkstVeu9CYGf1J/+9Kfy6KOPjiv0ZoUVVnjOIPopycIa119//X8d78suu6zUseqqq7Ye8ter8qNb5dCwXa9ttVHGq42Jx4MaVsvwlQfDWCb0cx0+3XTT9VWISrdDWUb9XHvDG95QvvGNb1TBmP0i4aHve9/7+j5Mu41zZ0pBqXU9/PDDZRC1Vb8MamBZHbk32nPPPfv+Oukng3ieNa2j2z4/+j04rW2us9G4zsYccsgh5fLLL+/6fvm3hHfXbftMLmA+12/dQLMddtihtZ/FeF67PvjBD5bPfOYzHQm2ruvuu+8u73//+7veD9hrbf8GTfvRm4anTkTGH4a5D7Xpfe54xnC6vc1ulhndGreflLKxHaPSFmx6PbTdj9WPzxb0S10wbH3Wz9dWGrXv22vq+8Gr77tJf2R/GKaxux//+Me1FwccpXbWI488Uto0qO2sbpSzo9juGLbv3M/P3gAAAADAMBneJ6QAAAAAAGDAXXTRReWOO+4ow+DXv/51KyHX888/fzn66KPL5z73uep/98vD3p/97GdrPwA9jN+prieeeKLxNp588smBDeqaddZZu7Kfk08+ufz+97/vyr7onZlmmqnR+x944IEybN+J5tZdd91a70vQ/KRl86WXXjqu9yXwfdppp/2vP5t77rnLC1/4wnG9/+KLL/7P/77mmmvKgw8+WLr5vUdZr4P6gf6iDh/dwKG2J9Y/H+daKZtsskkV+rDTTjs1DhVpyy233FKOOOKI0s/aOHfaPt+7ff3QO9/97nfLX//6Vz/BkOu3sJpRK2NcZ6Mj7Y7vfe97vf4YZdTbowsuuGCt9x533HGTHV+sc5+SRTQ32GCD0ibjee3KPcvPfvazssUWW/RN+Hf6rU899dQyStoOF2/ar9DNALVujcsOah99J9qLTbfZzXGHXp0fysZmRqkt2PR6aHvBw0G9x+zGtT5qY6aj9n17TX3fzLC3B/VH9o9hGLvL81YHH3xwGQX9VrYOajurG+XsKLY7PA8BAAAAANTx3zO0AQAAAACAvnHSSSeVYfGvf/2rnH766eUNb3hD420lfOC1r31tedWrXlVt8/jjjy+XXHJJFZbeKzfddFM566yzymabbVbr/cP4neqeJ03deeedZVBNM800XdnPt7/97Va2s8ACC5SNNtqorLnmmmXJJZesQlcyseH5Jjd85CMfKSeeeGIrn4HOTdxpY7GQts0yyyzl/vvv7/XHGGnrrLNOtVhKnfL9iiuuKGuttVb1/1PHjccaa6wx2T9PuXPjjTc+7/uzn7G2x6Qh9BMleH7ihn2SNjDxOpzBONZtT1xvO8j++Wgv/v+Fej7zmc+Ud73rXeVHP/pROeWUU3q+sOFRRx1Vdt99966GCE5E7mNnnnnmRuds7qEWWmih1j7T3Xff3dq26F+PPfZYOfLII1vZ1hJLLFE23njjsvLKK5cXvehFZa655qrKxecLGNpll10a3S8xPqNeR/eS66yM3CIDbYwz5JpNaPn6669flamLLLJI1VbIn0811VRTfN8JJ5xQPvrRj5ZRlnGO173udeWggw6a8HuzYO51111Xll566f/8WYLZ6thuu+06MuZiPK9diy66aDnkkEOqvt7cu5x22mnlH//4R+mlww8/vGy77bZlVPRbIF43Q+OevejtsGnaR992KHUb25xtttnKsI3bT46ysb5Ragum/6+Jtsfc77nnnjKIulEXjNqYaa6VlKFPPfVUrz/KSFDfD2593w36I/vLoI/d5TO30X7IeMV6661XXvayl1X9P4svvnh1rqb+eK5F6S666KKy6667lkFoZ7X9zOWgtrO60dYatXZWeB4CAAAAAKhjuJ+SAgAAAACAAZXgl5///OdlmCRouo3g+TGZ8JDgiLz+9re/lXPOOaecf/755bLLLutJKEEmgzQNaR/G79TtwKOrr766lc8yrC699NJqUYEm5phjjvLBD36wbL/99kM/CW+QNQ08/NOf/lT6zYILLlhuv/32Xn+Mkbb22mvXniieEPgEzz/99NNViNR4JGB+Sn/+k5/8ZFxl3qT7rztha8UVV6z13lGWxUiECgCT1uF0x7zzztvo/W0HHHS77aa9+L+Lhe29995lr732Kn/84x+rPobf/e535corr2wlhGsi0qdx3nnnVaHY/Wq++eYrN998c+33JyT1xS9+cWshybfeemsr26K/pf/3wQcfbLSNhRdeuAq223TTTVv7XLRvnnnmGek6updcZ6Mj9WdCq5tIX8Yee+xR3vrWt3Y13HbY7LjjjuX//u//yuOPPz7h9x577LFlv/32q/73o48+Ws4999xan2GHHXYonTTq43lte+ELX1j97h/+8IerPt3f/OY3VajcNddc0/XQ0muvvbba73LLLVdGQdvh4k23Z/HC/hknbHJ/PCVN73PTzzFKlI0TM2ptwYzDNZF+rLbrTyZv1MZHEhqcftY777yz1x9lJKjveS76I/vToI7d/fSnP228v5122qm8973vbTye3e/trOuvv760STtrykbx2TTPQwAAAAAAdQieBwAAAACAPvSLX/yilRDufpIQ4xtuuKG86EUv6sgD5DvvvHP1iltuuaX8+c9/rh66T8h2JjHn1fbk+UldcMEF1QPsbQVxD+J3mmqqqRrt75FHHilNCZ5/bnVDWiY9L4866qiyxBJL1N7GQw891OgzMD6LLLJIo0N12223VYE5TSdTtSlhepMGiU9EJq0l+IdmErKQMM1Mfpyosd8u5fR4yoGEN6288sqT/bsE2I/H3XffXdWZKbPqnjvZ17TTerRionLMmoQKvPKVrywHHnhgrfcC/Sd1eF25F7n88svLjDPO2OpnGlZN22533XVXVU+3FazUdoDR89FenPK9etpVY22rBIAmSDHtsoQfJEwu/QsJNe5kqEW/B8/nHqpJsN5f/vKX1j5LtpUFmxh+TftplllmmfL973+/zD333LW3oZ9mNEMB2yyz+p3rbHSk76fJGEPuPb7xjW+UjTbaqPY2lKn/Nuecc5att966nHDCCbUC1LPwbfoGE76W8PmJWm211cqSSy5ZumUQx/P6uU9xnXXWqV6Razr3Lbl/SV2YY5tjmXvXToaK5d5lVILnc0z7KVg8fcr0R9Bx2+3FjDnef//9PetjHGTKxvEZtbZg02cB2r7GU1cz5fK4SSDq29/+9vL+979/oA5vyuu6Y8Srrrpq+fGPf9z6ZxpW6nuei/7I/jZIY3djfRtNfOITnyhveMMbRqKdlWcu0y5ta2E17awpG8Vn0zwPAQAAAADUYXY0AAAAAAD0oZNOOqkM6/f6wAc+0PH9LL744tVryy23/K8/v++++6oH+zMRIpMzMpkygfi33357431moYAE6yfwaVS/U9NAi3/84x+lqV/96leNtzHM6gYvj/nsZz/bKHS+rd+Z59fGIh8Jq3n1q1/dN4f7hS98Ye33JoA85WVCj2hm3XXXrRU8/4c//KE8+eST5ZJLLhnXv19ppZWqgKkpTZDNRL/Uf+Mp9zIJs26gyVjIEfWu2bqT+0YpBBFGQZM6PGEsmVy/4oortvqZhtViiy1WBRU888wztbeRMnj11Vdv5fN0uzzXXhyfGWaY4b/CLCa93lJ3p42V8MH0MVx11VXlyiuvbGWhuDptyG5adtllGy1W1fR+e1IWzRodTc6b9MMdcMABjULnQz9NdzTtT7vxxhure+q2Fkbr9uIwveQ6Gx1N6+LddtutUdBoKFP/v1122aVW8Hz68LI4d4KfzjzzzFq/ww477FB6aRDG8wZFQtrWWGON6jWp9PcmfO+vf/1rde+SELzcuyTw/7HHHmu83/wuo6Lt+/ambYx+Wgx51McJ0x/3xBNPTHGcptuLx+f+J2UrysYpGbW2YPqxmn7WtB3aeKYg/Ym5Z2Xych+/6KKL1l7wchDHTNNPX/eazPfN+EbGOXh+6nuei/7IwdLPY3dN21npH2kSOj/Wp9It6VuZeuqpay8QnXrssssuKxtssEHjz5I+jmyLKRu1Z9M8DwEAAAAA1CF4HgAAAAAA+sxdd91VLrroojKMTjnllLL33ntXD+b3QgKH83rJS17yX3+eSZAJxT/66KOrcIa6Mrmj26EO/fSdmk48z7nfRCYCXHPNNY22MeyaTPjNhLQNN9yw0f4zsSbnFJ23/PLLlxlnnLFRyErKj34Knn/25LqJSujM+uuv39rnGeXg+e985zsTfl/qovwG4w2ef3aY0LOttdZa4wqev/jii6twlCbfl3qyeEAWsKgjYVGPPvpomWmmmRx+GAJt1OGC58cfypfFWRK+V9d5553XSvB82v7dDs/WXiyNQ9xy/uQ16eI7CXVMm+r73/9+Offcc2tvv9/vBVdYYYVG77/iiiuq0NCFF1648Wf52c9+1ngb9L/cIzXpC8u9StOguYceeqjcc889jbbB+DT9rR5//PHqfrqNe9SEEzVpKwwS19loadL3nVDDXXfdtfFnSH8G/79ttdpqq5XLL798wofk2GOPrYJf67Q9Z5555v8JfO8X/TSeN+imm266/wT8v/SlL/3Pn2c8JPe0hx9+eLUQal11g2EHUcZV2wx3vfbaa3sWoEa7fSS5ntK//4pXvKKVQ3v22Wc3ev9SSy1VlfFM2aiXjaPWFlxyySWrMbSMpdV1xhlnlPe85z2NP8tpp53WaCHUURkzrXsNZXxkEL9v2vR1JEQ57dqmgdmjQn3Pc9EfORz6Yeyu6QIzb3rTmxq9v43PMBG578hi703av2lntRE8/6tf/aqVBQaG2ag9m+Z5CAAAAACgjt6kugAAAAAAAFN08sknl6effnooj1DClH73u9+VfpNJ7O9///vLD3/4w0YTlv/2t7+VUf5OCTls4rrrrmv0/oRyMGWZJHHffffVPkTPFwI93gCLe++9t/F2eH7TTjttNbGmiUxi/u1vf9s3hzuBPJlQV9eZZ57Z6ucZVQmMqrvQSCY9XnbZZeP6t2uuuWYrZdKll15a7beOueeee2TCmjph1VVXrf3eTJI966yzWv08jI4mi1w1WaiCKcvE7JSpdanDuxue/fOf/7y04fe//33X75G1FzsXXJYFnA477LDyjne8o/Z2Hn744b4OR8jCRm306TWV9uv111/feDv0vzvuuKPR+9tYJCSLnz711FONt8PzW3DBBauw4SbaahONUtvKdTZamvzeiy66aJl//vkb7T/jesO6qHRdu+yyS633pS/vyCOPrBWkusUWWzQeq+q2YRyj7JUsxrvpppuWH/3oR2W77barvZ1ROpYZs2sSRD2pLGj0xz/+sdE2lltuuVY+C/+u25r0x7W5KNqTTz5ZfvGLXzTaxiqrrNLKZxlFo1I2jlpbMGPmTfsFTj311MZ9Arm+jz/++EbbGAVNyrBce3UWcxrUMeJR67doSn3Pc9EfOdy6OXbXpJ2Vz9lGW77bzy+uvfbajd6f551ybJuqu5DLKBm1Z9M8DwEAAAAA1CF4HgAAAAAA+kwbIVX97KSTTir9avnll68dhBH//Oc/yyh/p3nmmac0cffdd5cbb7yx1nuvvPLK8tOf/rTR/odd04C/puEEccYZZzTeBuO30UYbNT5cn/3sZ/smHHLWWWdtNIE+E5Qff/zxVj/TKJphhhlqT9o67rjjxrUARhZOeL59PF8w/aQTIM8999xSxzrrrFOmmmqqWu/l3+GtM80001C2GelvdRfHiIceeqjVz8L/t+GGG9Y+HBdeeGHfBxv1k9RfTeSebLwLxTxfvd9t2oudt+eee5YFFlhgqPpNxsw333yNFx367ne/22ixtYSUfeELX2j0GRgc+mlGT9M6Ov0aTcNqEgp44oknllHhOhstTX7vNvq+E4B1//33N97OMNlss82qNtZEPfPMM+Ub3/hGrX3usMMOZVAN4xhlLxcl/NjHPlb1ZdcxaseyrbG7LGSXe5omfXpLL710K5+F5v1x8ctf/rLxQj5xyimnNK4jm34Xhr9sHMW2YMJmm7j55psbh5kec8wx5aabbmq0jVHQtAwbtDHTpZZaqiyyyCKNnpvMPQHjo77nueiPHA2dHrtr0s6aY445Gj93lbHra6+9tgxSO+vBBx8s3/72txttI4HoeU6C5zZqz6Z5HgIAAAAAqEPwPAAAAAAA9JE//vGP5YYbbijDLBOU+yXEeHLqhumOBfeM8neaffbZy8wzz1ya+MlPfjLh9yRwad99920UZjAK/vWvfzV6f9Pr9oEHHqgmHtM9m2++eSuTtz7xiU+UTskE/IlMwt9iiy0aTer6zne+U3rl6quvLp/5zGfKMFh33XVrBxiMN2Rplllmec5/s9hii4174mTdMIy635N/y8S+DTbYoPbhOP/888vvfve7nh3OTOJMeCyDp0l79LHHHiu33nprq5+H/98uqSvt/IMOOqhnhzLnxIc//OEyKJpOhI8vf/nLjd5/zTXX9GyStvZi50PKVl555drvf+qpp0q/h6M2kf6Jz33uc7WDkQ4//PBy1VVXNfoMDI5e99Pk/swCgYNVR6f/pGlYzY9//ONx35sPA9fZaGnye7cxZvWtb32r8TaGTRaX3Hnnnbs23rbEEkuUNdZYowyyYRyj7JXZZputCj0dxvuWtiXcNWN4TeSYpZ3RRBYerhuIzeRtuummjQ5N+kqb9hGljm3ar5f+3jb6uxjusnEU24K5xps+C3DIIYeUO++8s9Z7Ezj/9a9/vdH+R0VC2FdcccXa7z/hhBOq5zZ6Idd+9n/88cd3rZ813zX77JWLL764HHzwwWVQqO95LvojR0Onx+76oZ3V7QVJXvrSlzZ+DvrII4+snhWsI4tcW6h6fEbx2TTPQwAAAAAAEyV4HgAAAAAA+kivgtm6KeGvv/jFL8b970855ZTy6KOPlm5pEsww3skGw/idxiy55JKliaOPPrp6mH8iE97f8Y53lGuvvbbRfkdB07CGhEc2sf/++1chfHTPQgst1Epw9mmnnVY+9alPtR5cc84555RtttlmQpPZt9xyyzL99NPX3udhhx1Wrr/++tJNCY583/veV7bffvvy61//ugyDTgeyr7nmmuP6d50OklpnnXU6uv1RsN122zV6f8qebraZMlk1k/p22GGH8u53v7tcd911Xds37Zlrrrkavf/MM89s7bPw/6233npl3nnnbXSvfOGFF3b1kCaU9WMf+1jV/hike/XFF1+8LLfcco228Yc//KH2olGPP/54+eQnP9mzhcFGrb2Y87Tbk+GbBExk8n+/t12aBnadfvrp5Utf+tKE33fiiSf2dJENRqufJmV0Fkbr9+DAYbPxxhtXIcxNHHXUUeXKK6+s9d6//vWvIxcK6DobLTPOOGPt995yyy2N+h9yv3DppZfWfv8w22mnncp0003XlX2lP6dtwzye122pv7q9yFLd49nv9y1ty+I23/jGNxpt46c//Wn5y1/+0rjviPbDEueZZ55G28hiVblfrdvfnj6iu+++u9FnSHhxk3q+nykb2zOKbcFFF1208VjpfffdV3bbbbfyj3/8Y0Lvu+uuu8pb3vKW8tBDDzXa/yhpMmaa/tiUp93sx8k+c25vvfXW5aMf/Wi54447JvT+V73qVY32/5WvfKXcc889pZsuuuii6nrYZZddyuWXX14Ghfqe56I/sjeGbeyuSTsrz4qnX7pJ2fyzn/2sdNsss8zSeLHqJ554ouyxxx4TXgg27avdd9+93H777Y32P0pG7dm0UXseAgAAAABoTvA8AAAAAAD0iTxo3ouH5HthIpOTDzjggPLyl7+8HHzwwV15mL7J5LE555xzZL/TmGWWWaY0kYmSe+21V/nlL385ru+VSQOXXHJJo32Oihe84AWNAl5+//vf154IdNxxx5Xjjz++9r6pLxOR2vCjH/2ovPWtby1///vfG2/rj3/8YzWxKotGTHR7CRNuMkk5dW2Oya233lo6KWVZgvV33XXX8upXv7r8/Oc/ryYNDYuXvOQlZdZZZ+3Y9scbktDJ4PmFF164CmygmVe84hVliSWWqP3+m266qSorstBMJz3yyCNVObfFFltUk/rqBjnSHxZYYIFG7//2t79d1VW0KxNf3/CGNzTaxnve856u/DYXX3xxede73lVN2E07tu3Fd7ph2223bbyNz3/+8+O6L3t2G+gDH/hAFVzfK6PWXkzgz5ve9KZqYvrJJ59cBf93OrjiT3/6U6335n60k23INqT9t/766zfezve///3yzne+swrhGk87JAHgCXAapnsGnt/cc8/d6DClDHnwwQdrvfeQQw4pF1xwQaP9U6+OShhYEynn3/a2t004rObee++t6rcEy44S19loabIIWa6tuouQZSGQhBMxeQk8zr1Vp00zzTSNQ6ZGbTyv266++urq3ufNb35zFa7V6XvtBOimb7EXixoOoh/+8Ifl3HPPrfXeG2+8sfEiWlkAbKuttmq0DSbfH/e6172u8aHZb7/9JlxPZrGrz33uc+XUU09tvP/0ewwrZWN7RrUtuPPOOzfeRurL9C2mn2G8C1KkTp9oEPmoyzHLMztNxi0+/OEPd3zB1yxGkDDShFVnf3XbU1kct8miMnUXRagzHpC6Kr9P+up/+9vflkGjvue56I/sjWEbu2t6j5xjUMff/va38v73v79nC+hmMcWmsohKtnPCCSeM699nwYIEc+c+gfEbtWfTRu15CAAAAACgOcHzAAAAAADQJ37zm980Dn7JQ/rXXnttx18HHnhgo8+ZCXETmYSY45IAyE022aR64DnB9XWDlZ7LddddV3784x/Xfv+SSy450t8pVl999dLUQw89VIVKJkTp9NNPr8LOMwEnEwtz/iX8McEUCa6sO8lxFCV4JUHKTSYCZNL0RMNAjjnmmPKJT3yi9n5pJoFmK620UiuHMZNsU2Z94QtfqCZ3TcQDDzxQTaJ64xvfWF772teW8847r/bnSJk57bTT1n5/wh/zOc4///zSthtuuKEKIUoYUSYkXXTRRWVYy5M111yzI9tOwMx465K11lqrdMq6667bsW2PkvyeWbSiiUysTL0/0WDF55Nghixek5DXlJWp49reB70x0fbr5CZ7JqwlweNpx6T+u+qqq6oy/rlenQ6/GAavf/3ry2yzzVb7/Q8//HB5y1ve0kpg1bMlQPA73/lO2Wyzzcouu+xSzj777I4HuHRSJqTPOOOMjbaR9v+ee+5ZvvKVr4wrEOH666+vwswmGlbfCaPYXszE9A996EP/qdPS79KJczjXyd13313rvYsttljVjux3b3/721vZzq9+9asqZDWLMWQifcIY09+R0IL0c6R+SeD8pptuWtU3JtqP5mI5M8wwQ+33px/xS1/60oTek/Ms/brpk6R37aGmEiK/4447jntx15RHCWO+5ZZbyqhxnY2WJuFCkTbaRBfJvOyyy6ogsUcffbTRvodd7rE67WUve1mZb775OrLtYR3P65ULL7ywCtfKPdGXv/zlji0wl0UDEqTVi/JkEGXsL30Al1566YT7U7LQcNNnHDImsMgiizTaBlNuf84yyyyt9BF95CMfGVcfaMbz07/6gx/8oPHPkn6OFVZYoQw7ZWNzo9oWTN9T+tyayvNb6avMtfu9732v/PnPf676AFOXJjA1YyRHHHFEFaC71157VfelTEzK4qaL82ZsJGNXEz1XxxNYnGcWU9anXZ121ESfB5mcPPPVxF/+8peqn75umPLz9adngZR833322ac6xweZ+p7nOz+a0h852mN3TdtZ3/3ud6vx0YnIv8+4c9ohvbLaaqu18lxa7pfzXNI222xTvvnNb1b9IKln087K/VUWMkoYefrP8mxUxhCZmFF8Nm0Un4cAAAAAAOqr35sIAAAAAAC0arxBMVPywhe+sCy33HKlGzbaaKMqxC5hWXUk5Cgh+e985zsn/BB2HnTOa7rppqtCb9dbb70qHDbfve6D1Pk8Z555ZhX49c9//rPWNnI8ll122Qm/b9i+0zrrrFPakomNedGelVdeudHEhZyn733ve6vg8TnnnPM5/20myGRC6imnnFJ7f7Tj4x//eNlpp51ambyW0NEjjzyyHHXUUWWZZZapyqvll1++Oh/mmGOOMtNMM/1noYg777yzmgycicB5JZiiDQnQycSX73//+7W3kfMzE3Be+cpXVpOGXvKSl9TaTurB3//+91UgREJWEyY5KlJPnXPOOa1vd+mll67OpfF40YteVOaaa66OhD0Lnm/Pq1/96mqSZCbV1pXrbNttty277bZbFXqx4IIL1tpOyqZM6MviFwmVFowxnJZaaqmqXZ2gjLpSZ+UcyWu8MpE14RRM2Qte8ILyvve9r3z+85+vfZgS2pzjnPvJBJutvfba1UTiicr5kfZJJhCfddZZAx8q8mypH1/zmtc0DvjKtXD44YdXbfqEGG288cbVYlbzzjtv1Q5KiEFCiM4444yqbG1y3bVplNuLCcBMvZvXPPPMU01gT7sm9+oJ360rC0l9/etfb3RO5X50ECREIscsv1lT6Y857bTTqhc829RTT12VLRMN95zU8ccfX4XC7LvvvtX9+HNJ+ZP+nCYLwdHchhtuWPWnpL+kiZTLCf5MIODWW29dlVvzzz9/1T+TuiAhdAkySv9w6qFR5TobLVl88yc/+Unt96f9l3bfQQcd9LxjbWnjJXgzYeTjWaRp1OW3yatTAeORENROG7bxvF7L/WTC5/LKfWbuXXI8c1znnnvu2ttNHfjFL36xWth62O9d2pbzb9ddd60W/csC4c+1oF3O2ywUvv/++1cLBQ7CAhWjKn1ECXrLAlRtPM+S+9tcqwmQy7Wbvof0H+Wazj1H+j6uuOKKVj577nUS1DlKlI31jWpbMNdJyuy2rpXcP47yPWSnZRwpZWmeo6gr4/PpB8jimRmDTTlf95xPv0Geyfr1r39djb20Le3kLLrZZLHaBJfmWZeMDye4P88H1JH2ShaLuOCCC6oxoSyeM0zU9zwX/ZG9N+hjd2lnNb3XTDsr9yTPF+SeRdHSpvvqV7/akUX/JiqLsjRdOGZMxgTyOuSQQ1rZHqP9bNooPw8BAAAAAEyc4HkAAAAAAOgDedD43HPPbbSNTCzrlplnnrmalPLzn/+89jZOOumkCQfPTyqBdr/97W+rV2Tye8KPE6yQh6oXXXTRKgwvkzVmn332Mv3001dBEE888UR59NFHq4emb7311nL55ZdXD3ffcsstpYkEIcwwwwyNtjEM3ymf8cUvfvHQBTcOi5e97GVVSGcTv/rVr8pmm21Wtttuu7LBBhtU52dConP+3nPPPeWaa66p/k3Kh5yX9F4mgL3+9a9vHDz67FCRa6+9tnr1QhZASBjPXXfd1Wg7YyGQOY/XX3/9suKKK1bB5wnGHStnExgwFqya/d12223/+e4p6/olYLXbOhXMnrpnItZYY43yi1/8ovXP0eZCKqMuQX+f/OQny+te97pqkmhduRYT4nHooYdWv8/qq69eXbOLL754db3ONtts1b5yvT7yyCNVyFTaRjfddFN1vaZ+uv7666vyi+GW9msmRKZNTP/JxOzcCza9X8gk3bwWW2yxqk2a8iCBQAlbTXkwthhO2qO5304dniCRSevwuoF+g+Jtb3tbOeGEE1r5nilTs/hQXoNCe7FU92e53vKK9CfkWkk4zxJLLFEWWWSRKtAxr1wzafuO1aUJGrrjjjuqujMLNKSPoen9Xe5HB2nxrle96lUj29ane3JdNAmej2OPPbYKHUvgbgIgswhP2sdj97FZaGVsQZ0m7XHaDat597vf3cq2etk3MyhcZ6MjoV1ZlKrJfX9CDbOA0yabbFI233zzqu2UtlICxdO2yphLQhkTaJ02MuOXIKROhQcnZPEVr3hFV3+OYRjP6ye5X0+w3FhgcMKsc/3lWObeZaGFFqqOZe75x+5dIu2dBNHl/en7S8BVwlObtuMH6d6lbQkQP+yww8oPf/jDavG5jJuMLW6TwNaUfTlvMwbYVjmY3zjjjnROAuTSN5B6rqlcX91aPD79iIO2qEablI0TM8ptwfRhpW+gaf8C3XnW7mMf+1jjPoG0f77yla9Uobw591ddddXqfE0bdNZZZ63GSCJjJKm/0/bMGEnO8fQhXH311Y3bn+OVxRLTZs7YbZP2yTHHHFO98l0TWjzWzz02Rpy2dtqGaWuPPdeR7zjWb5K2YrYzzNT3PBf9kf1jEMfuVltttTLLLLM0KsvTdspCZ9lfnnNP4H2+e/oy/vGPf1TP4uX5+Sx23q06arzPpG2zzTbl1FNP7fVH4XmM4rNpnocAAAAAAMZL8DwAAAAAAPSBhN42nQS/1VZbtfZ5xru/JsHzN998c/nDH/5QVllllVY+Tx7k/v3vf1+9euHVr35169sc1O+0/fbbC57vUwmJaDoRaGwi61FHHVW9GAwf/vCHyxVXXFEFzg2DTJjOROpddtmllTBIQW0Tt8wyy1RhP5kU2fakvYmGKrUdPD/23Wh3AYz3v//95ctf/nLjbT399NP/FewFk5PAOcHz/Wmaaaap6vDca2RyfFMJ6mtzcZ1hkmC4LPZ24IEHllGkvfi/EriTcOq8ui2LleV+dFAk4OMd73hH+frXv97rj8KQe+UrX1kOOeSQqo3b9PpOEEZe9L+EGCYY7vzzz+/1RxkJrrPRscACC1QBhBdddFGj7aSvMSFXedGeLbfcsuoXarsvMbbddtsqaLKXBnU8r5/DlvPqhSzikwC8UZdxxFNOOaV6ddree+9dhbbROQmrPPjgg8trX/vaKkRvEGSByX322afXH6OvKBuf26i3BT/zmc9UC9I1DZ+lO30CWVgjIepN5XztVX/veGUBoS984QtV6HUbetnm7nfqe56L/sj+NQhjdwmHz6I8WfC8iQRxJ1w+r0Hy0Y9+tFrorhN9WrRr1J5N8zwEAAAAADBenswCAAAAAIA+cNJJJzV6//LLL19e+MIXlm56+ctfXmaeeeaefu9+scQSS5RNN920DJMm3ykTWjMxpRcSLjLjjDP2ZN+DINfszjvv3POwUXozyfRrX/taFUA6LFZdddWy77779vpjjLR11lmn9W0mSL6T/75X34tSdt99964vlMTo2mabbbQ5+thiiy1WTfbVLuy8N7/5zeXFL35xGVXai/0jC0YlmGKQvOtd7yprr712T/a98MILlw033LAn+6a7FllkkbLZZpv19LCrj3vjU5/6VBUKQue5zkav76GXlKnP3T+90047deS4Z0xqlA3jGGUvvfWtb+31Rxgp6667btliiy16/TFGwrLLLlu1QQdBnjPIwpWD1o8wzAalbBzltmAWUfz4xz/ek31PNdVU5fWvf31P9j2oPvKRj0x4MfZBlrp+jz326PXHGAnqe56L/kiajN3ttttuVZ0/iu2sueeeuxxwwAE9WzAtC9YwfqP2bJrnIQAAAACA8RA8DwAAAAAAPXbDDTeUK6+8stE2evGgdMK9N9poo0bbOP3008sTTzxRBl2CjxN4PkyafKeEm7/jHe8o3ZbJLZ/73OeqyR5M2dve9rYy55xz9uQQrbTSSgIkemihhRYqRx55ZJl33nnLsMgk9g984AO9/hgjq+2A9oQkTfT8zOTp2WefvdXPIXi+cxI0/YpXvKKDe4B/W3DBBcvWW2/tcPSx3Evuv//+PZugPUrhjgcddFDjReOaBBb0mvZifwT+9jr4q46UTwcffHBZfPHFu7rfWWaZpXz7298uc801V1f3S+/svffeVXndCxtvvHFZbbXVerLvUbfooouWT3/60z3Zd/pQ3/jGN5ZR4jobHVm4pVf9Oi94wQuqvnemLIvitj2WljGHZZZZZqQP+zCOUfbKKqusUl71qleVUTLttNP2bN9ZhOezn/1sz/Y/il796leXD37wg6WfpQ/r0EMPLUsuuWSvPwoDWDaOelswi/H0oj/2Pe95j2dAJij9QN/5zneqtuyoSP3zute9rtcfYySo75kS/ZE0GbtL30vKl15In8dee+1Ver1o2oc//OGu7zfH/C1veUvX9zvoRu3ZNM9DAAAAAADPx8w9AAAAAADosRNPPLHxNnoRPB9bbrllo/c/8MAD5ZxzzimDbLfddqsmsA6TNr5TwouWW2650k377LNP2W677bq6z0E0xxxzlE996lNd32/CpL/xjW/0LEyNf0tQww9+8IMq4HtYZBL/Rz/6UcG1PZpY16Y11lijViDo6quv3tpnmGaaacpaa63V2vb43wmhX/va1wSC0xVZmCThvfSvbbbZphx44IFlhhlm6PVHGWpp9x1wwAFVHddNL33pS6s2Wj/QXuyd3P999atfLTPNNFMZRFnY7rvf/W7XFu9KvZWAvVEPTx3FcroXoSlLL710Fb6REHJ645WvfGVPQmPe9773lc0226yMEtfZaEmI8Wyzzdb14OYsWLPYYot1db+DZr755mu9/EnA6igbxjHKXo6dZeG2UWsbpf+sV/drKa8Tfkl37bHHHuVDH/pQX57ruQ4PO+ywsvLKK/f6ozDAZeOotwX322+/6l6zW9785jdXwfPUW4Al/Y5rr732yBy+T37yk8Jzu0R9z5Toj6TJ2F3uIxZaaKGuH8Q859oP9whp97z1rW/t6rP5FmurZxSfTfM8BAAAAADwXATPAwAAAABADz399NPllFNOabSNVVZZpSyyyCKlFzbYYIPGkzbbCN7vlYScZ0LFMGnrO+Xh/UMOOaSaLNlpmeicCbSZOMf4bLHFFtVkg26ZZ555ypFHHlnmn3/+ru2T5w7bOu6446oyfFhkctd3vvOdMvvss/f6o4yUtD/aDIZZc801uxZYPyUrrrhi1wMpRnESa4JK9t57bwtG0FELLLBA+dznPjdQoTijKIuoHXPMMdXvRedsvPHGVahLt66H1M2ZzN3tsPvnor3YfekX+PrXv15e8pKXlEGW9u6Pf/zjji/elTC3I4444j9t2/QZ1tVP1x7js/vuu3c1GC6L0uV860a/Hc8t/bDbbrttV+vDd73rXWUUuc5GRwI/s/BSAkC7IfvJ/tZff/2u7G/QZdHitsw444wjFSA1CmOUvZJxhcMPP7wsvPDCZdS8+MUvLvvvv3/X+2nTHkmfEL1rF6XfZuaZZ+6bnyD33Ln3bnO8h9EsG0e9LZj+36985Stl55137vi+3vGOd/xn4dEm/Vij3JeV6yz9M294wxvKKMj5+eEPf7h88YtfrMaL6Sz1PVOiP3K0NRm7yzhW3tvN+4i0NV7zmteUfrHPPvt0ZSHh9PkceOCBVVvzmWeeabStUW1njeKzaZ6HAAAAAACmZDR6SQEAAAAAoE9deOGF5W9/+1ujbfRyUnYezk6AXRPnnXde+cc//lEGbTJcJsRnMtywPJTeie+UCeIJgp5ppplKp2Tbmci76667dmwfw+oDH/hAedOb3tTx/WQy+lFHHVVe9KIXdXxfTGwS86GHHlo++9nPDk3IdoL0TzvttLLRRhuVfjUsdcak1l133da2VTdQpG5gfae/D88fSJEgmaWXXrpvD9UwXrOjJvdKCZ/vVsgM9WRi+6mnntpXk8aHsTzYaaedype+9KWOXw8JWTrssMPKLLPMUvqN9mL3zDvvvOX73/9+efnLX16GZcGltFte8YpXdGT7WfzohBNOqBaXHPPggw82CmFl8CTwc/PNN+/4fpZffvmqn2a++ebr+L4YX59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" + ] + }, + "metadata": { + "image/png": { + "height": 923, + "width": 3023 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "OPTIMISTIC_DESIGN = EffectPrior(mu=0.6, sigma=0.4)\n", + "SCEPTICAL_ANALYSIS = EffectPrior(mu=0.0, sigma=0.3)\n", + "\n", + "design_analysis_cases = {\n", + " \"Matched (design = analysis)\": (OPTIMISTIC_DESIGN, OPTIMISTIC_DESIGN),\n", + " \"Optimistic design, sceptical analysis\": (OPTIMISTIC_DESIGN, SCEPTICAL_ANALYSIS),\n", + " \"Sceptical design, optimistic analysis\": (SCEPTICAL_ANALYSIS, OPTIMISTIC_DESIGN),\n", + "}\n", + "\n", + "design_analysis_curves = {}\n", + "for name, (design_prior, analysis_prior) in design_analysis_cases.items():\n", + " design_analysis_curves[name] = assurance_curve(\n", + " gaussian_assurance_at_N,\n", + " N_grid=N_GRID,\n", + " n_sims=N_SIMS,\n", + " rng=np.random.default_rng(RANDOM_SEED),\n", + " prior=design_prior,\n", + " analysis_prior=analysis_prior,\n", + " sigma_obs=SIGMA_OBS,\n", + " threshold=DECISION_THRESHOLD,\n", + " )\n", + "\n", + "fig, ax = plt.subplots(figsize=(15, 4.5))\n", + "for name, df in design_analysis_curves.items():\n", + " ax.plot(df[\"N\"], df[\"assurance\"], marker=\"o\", linewidth=2, label=name)\n", + "ax.set_xscale(\"log\")\n", + "ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{int(x):,}\"))\n", + "ax.set_xlabel(\"Sample size per arm\")\n", + "ax.set_ylabel(\"Assurance\")\n", + "ax.set_title(\"Assurance when the design and analysis priors agree or disagree\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "e0ff8d8a", + "metadata": {}, + "source": [ + "A sceptical analysis prior lowers assurance at every sample size, because it discounts the same evidence the optimistic design prior expects to see. The reverse pairing recovers most of the matched curve once the data accumulate, since an optimistic analysis prior and a growing dataset agree. The gap between the curves is the price of a mismatch, and it narrows as $N$ grows. Small experiments pay the most for an analysis prior that disagrees with the design belief, which matches the guidance observation that trial characteristics are most sensitive to the analysis prior when the sample size is small {cite:p}`fda2026bayesian`." + ] + }, { "cell_type": "markdown", "id": "eb131822", @@ -1048,12 +1194,12 @@ "\n", "$$n_{\\text{ESS}} = \\frac{2\\sigma_{\\text{obs}}^2}{\\sigma_0^2}$$\n", "\n", - "The simulation-based approach recovers the same number without the closed form. For each $N$ in a grid, draw synthetic datasets from the prior predictive and compute the posterior variance under a flat reference prior; the $N^*$ where the average posterior variance crosses the prior variance is the ESS. The machinery is the same prior-predictive loop already running in the assurance calculation — only the inner step changes. In the conjugate case the analytical posterior fills that step. In non-conjugate models — hierarchical likelihoods, logistic regression — the same loop runs with MCMC posteriors and the ESS estimate generalises without modification. Verifying that analytical and simulation estimates agree for the Gaussian case validates the approach for those more complex settings." + "The simulation-based approach recovers the same number without the closed form. For each $N$ in a grid, draw synthetic datasets from the prior predictive and compute the posterior variance under a flat reference prior; the $N^*$ where the average posterior variance crosses the prior variance is the ESS. The machinery is the same prior-predictive loop already running in the assurance calculation; only the inner step changes. In the conjugate case the analytical posterior fills that step. In non-conjugate models such as hierarchical likelihoods or logistic regression, the same loop runs with MCMC posteriors and the ESS estimate generalises without modification. Verifying that analytical and simulation estimates agree for the Gaussian case validates the approach for those more complex settings." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "8518b730", "metadata": {}, "outputs": [ @@ -1120,7 +1266,7 @@ "Sceptical 0.1 0.5 128.0" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1148,10 +1294,204 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "c1190134", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n", + "Sampling: [delta, y_A, y_B]\n" + ] + }, + { + "data": { + "text/html": [ + "
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prior_sigmaanalytical_ESSsimulation_ESS
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" + ], + "text/plain": [ + " prior_sigma analytical_ESS simulation_ESS\n", + "Informed 0.3 355.6 357.1\n", + "Default 0.5 128.0 128.1\n", + "Flat 1.5 14.2 14.3\n", + "Sceptical 0.5 128.0 128.1" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def simulation_based_ess(prior, sigma_obs, N_grid, n_sims, rng):\n", " \"\"\"\n", @@ -1214,10 +1554,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "839ea69e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 964, + "width": 3967 + } + }, + "output_type": "display_data" + } + ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(20, 5))\n", "\n", @@ -1290,11 +1646,11 @@ "id": "95945bca", "metadata": {}, "source": [ - "The left panel is a calibration check. The simulation curve — posterior variance under a flat prior as a function of N — crosses the prior variance at the point the analytical formula predicts. The two estimates agree to within interpolation noise. For non-conjugate models where no closed form for the posterior variance exists, the same loop with MCMC posteriors in place of `gaussian_posterior_delta` gives the correct ESS. The conjugate case validates the simulation approach before the closed form disappears.\n", + "The left panel is a calibration check. The simulation curve, posterior variance under a flat prior as a function of N, crosses the prior variance at the point the analytical formula predicts. The two estimates agree to within interpolation noise. For non-conjugate models where no closed form for the posterior variance exists, the same loop with MCMC posteriors in place of `gaussian_posterior_delta` gives the correct ESS. The conjugate case validates the simulation approach before the closed form disappears.\n", "\n", "The right panel reads the ESS against the assurance curves. The dotted vertical lines mark each prior's ESS on the sample-size axis. An ESS of 356 (Informed) means the prior carries more information than 350 subjects per arm before the first participant is enrolled; the curve climbs steeply early because the prior is already doing most of the work. An ESS of 14 (Flat) means the prior contributes almost nothing, and every observation earns its full weight. The ESS converts the abstract question \"how informative is this prior?\" into a number on the same axis as the assurance curve: experiments whose planned N falls well below the ESS are prior-dominated; those well above it are data-dominated.\n", "\n", - "This is the quantitative form of the trade-off the sensitivity analysis in {ref}`sensitivity_confounding` interrogates from the other side. There, the question is not how much information the prior commits but what happens when that commitment meets an identification gap — the tipping point at which the conclusion turns is the price the prior's ESS is staking." + "This is the quantitative form of the trade-off the sensitivity analysis in {ref}`sensitivity_confounding` interrogates from the other side. There, the question is what happens when that committed information meets an identification gap, and the tipping point at which the conclusion turns measures how much that committed ESS has put at risk." ] }, { @@ -1302,15 +1658,15 @@ "id": "fda04dc5", "metadata": {}, "source": [ - "The three curves disagree most where they should: at small sample sizes where the data has not yet overwhelmed the prior. The informed prior reaches high assurance fastest because it is willing to commit to a narrow effect range; the sceptical prior assumes a smaller effect and so needs more data to detect it; the flat prior carries enough probability mass over zero that even moderate datasets do not push the posterior reliably above the decision threshold. The point is not to advocate for a specific prior. It is to make the dependence of the assurance number on the prior commitment visible, so that the prior can be the subject of negotiation rather than a hidden choice.\n", + "The three curves disagree most where they should: at small sample sizes where the data has not yet overwhelmed the prior. The informed prior reaches high assurance fastest because it is willing to commit to a narrow effect range; the sceptical prior assumes a smaller effect and so needs more data to detect it; the flat prior carries enough probability mass over zero that even moderate datasets do not push the posterior reliably above the decision threshold. The point is to make the dependence of the assurance number on the prior commitment visible, so the prior becomes a subject of negotiation rather than a hidden choice.\n", "\n", "## Planning as a posterior over future posteriors\n", "\n", "The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty.\n", "\n", - "Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. It makes explicit the assumption power leaves buried: a believed distribution of effects. Naming that assumption, and giving it a posterior, is the move this sequence repeats.\n", + "Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability that the inference about to be drawn will be useful? The operation that produces assurance is the same one that will produce the posterior at the end of the experiment. Only the data are still hypothetical. Assurance makes explicit the assumption power leaves buried, a believed distribution of effects, and gives that assumption a posterior to argue over.\n", "\n", - "In each case here, the Gaussian and the Bernoulli, the inference machinery was the same. What changed was the likelihood — and with it, the ceiling that the likelihood's geometry sets on how much assurance any sample size can buy. The same is true across this notebook and its successors. Planning, interpretation, synthesis: three problems, one machinery. The problem changes; its proper characterisation is always a posterior. We have just seen the first of the three.\n", + "The Gaussian case and the Bernoulli case ran the same inference machinery. What changed was the likelihood, and with it the ceiling its geometry sets on how much assurance any sample size can buy. The next notebook, {ref}`sensitivity_confounding`, keeps the machinery and changes the question: not what an experiment will probably say, but what it did say once clean identification is itself in doubt.\n", "\n", "## Authors\n", "\n", @@ -1327,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "b30e957b", "metadata": { "execution": { @@ -1337,7 +1693,32 @@ "shell.execute_reply": "2026-06-01T17:16:23.604855Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Fri, 12 Jun 2026\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.13.13\n", + "IPython version : 9.14.0\n", + "\n", + "pytensor: 3.0.3\n", + "xarray : 2026.4.0\n", + "\n", + "arviz : 1.1.0\n", + "matplotlib: 3.10.9\n", + "numpy : 2.3.5\n", + "pandas : 2.3.3\n", + "pymc : 6.0.1\n", + "scipy : 1.17.1\n", + "\n", + "Watermark: 2.6.0\n", + "\n" + ] + } + ], "source": [ "%load_ext watermark\n", "%watermark -n -u -v -iv -w -p pytensor,xarray" @@ -1377,4 +1758,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/causal_inference/assurance_planning_simulation.myst.md b/examples/causal_inference/assurance_planning_simulation.myst.md index cf0623638..47b8ed9d0 100644 --- a/examples/causal_inference/assurance_planning_simulation.myst.md +++ b/examples/causal_inference/assurance_planning_simulation.myst.md @@ -20,18 +20,26 @@ kernelspec: :author: Nathaniel Forde ::: +:::{figure} experimentation_triptych.jpeg +:name: experimentation-triptych +:width: 100% +:align: center + +The experimentation lifecycle as a Bosch triptych. *Left, Bayesian Assurance (this notebook):* before any data arrive, the planner reads possible effects from the prior and asks what the experiment will likely conclude. *Centre, Sensitivity Analysis:* a single experiment is wracked by the biases it cannot rule out, and the model is contorted to see which commitments its conclusion can survive. *Right, Meta-Analysis:* many experiments are pooled through a hierarchy of levels into a synthesis that becomes the next plan's prior. Three panels, one posterior machinery. +::: + Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records, orientated at a new angle in pursuit of theory, anomalous findings and the next question. ## The question planners are actually asking When we experiment, we implicitly assume the experiment will be useful. Our questions are "operationalised" as power heuristics: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold. -An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (revenue per visitor) or binary (conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery serves all three; what changes is the problem put to it. +An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (like revenue per visitor) or binary (like conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery serves all three; what changes is the problem put to it. :::{admonition} Where this lands in regulatory practice :class: note -The assurance focus here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\Pr(\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference, which is exactly the split the generative and inference models below are built on. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical. +The assurance focus here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\Pr(\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference. The generative and inference models below are distinct objects for this reason, and a later section runs the assurance loop with the two priors deliberately mismatched. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical. ::: ```{code-cell} ipython3 @@ -45,7 +53,6 @@ import matplotlib.ticker as mticker import numpy as np import pandas as pd import pymc as pm -import seaborn as sns from scipy import stats @@ -55,6 +62,7 @@ warnings.filterwarnings("ignore", category=UserWarning) ```{code-cell} ipython3 %config InlineBackend.figure_format = 'retina' +az.style.use("arviz-variat") rng = np.random.default_rng(42) RANDOM_SEED = 42 ``` @@ -75,7 +83,7 @@ class EffectPrior: return rng.normal(self.mu, self.sigma, size=size) ``` -## Power and assurance: two questions, different focus. +## Power and assurance: two questions, different focus Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null: @@ -165,11 +173,17 @@ with gaussian_two_arm_model(y_A_demo, y_B_demo, EFFECT_PRIOR): ``` ```{code-cell} ipython3 -pc = az.plot_dist(idata_gauss_demo, var_names=["delta"]) -ax = plt.gcf().axes[0] -ax.axvline(0.0, color="C2", linestyle="--", label="ref = 0.0") -ax.legend() -ax.set_title(r"Posterior on $\delta$ at $N=200$ (one simulated dataset)"); +pc = az.plot_dist( + idata_gauss_demo, + var_names=["delta"], + visuals={"title": {"text": r"Posterior on $\delta$ at $N=200$ (one simulated dataset)"}}, +) +az.add_lines( + pc, + values=0.0, + visuals={"ref_line": {"color": "C1", "label": "ref = 0.0"}}, +) +pc.get_viz("plot").legend() ``` The posterior concentrates above zero; under the decision rule the team would conclude the new flow lifts revenue. That is a single draw of the experiment. Assurance asks how often this conclusion would arrive if we re-ran the experiment many times under our actual prior. @@ -208,7 +222,11 @@ pd.DataFrame( The two posteriors agree to within MCMC noise. The closed form will carry the decision step; the generative model will carry the data step. ```{code-cell} ipython3 -def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng): +def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng, analysis_prior=None): + # `prior` is the design prior: it generates the synthetic worlds. `analysis_prior` + # is used to form the posterior; it defaults to the design prior so a single + # argument reproduces the matched-prior case. + analysis_prior = prior if analysis_prior is None else analysis_prior seed = int(rng.integers(2**31 - 1)) pp = pm.sample_prior_predictive( n_sims, model=gaussian_generative_model(N, prior), random_seed=seed @@ -216,7 +234,7 @@ def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng): y_A = pp.prior["y_A"].values.reshape(n_sims, N) y_B = pp.prior["y_B"].values.reshape(n_sims, N) d_hat = y_B.mean(axis=1) - y_A.mean(axis=1) - post_mu, post_sd = gaussian_posterior_delta(d_hat, N, sigma_obs, prior) + post_mu, post_sd = gaussian_posterior_delta(d_hat, N, sigma_obs, analysis_prior) prob_positive = 1.0 - stats.norm.cdf(0.0, loc=post_mu, scale=post_sd) return float((prob_positive > threshold).mean()) @@ -236,7 +254,7 @@ assurance_df = assurance_curve( assurance_df ``` -For comparison we compute frequentist power at a single fixed effect size. The convention is to set the effect to the prior mean — which is itself an arbitrary choice, and a revealing one. +For comparison we compute frequentist power at a single fixed effect size. The convention is to set the effect to the prior mean, which is itself an arbitrary choice, and a revealing one. ```{code-cell} ipython3 def gaussian_power(N, delta_alt, sigma_obs, alpha=0.05): @@ -297,9 +315,9 @@ ax.legend(loc="lower right", fontsize=9); The three power curves fan out because each conditions on a single assumed world. Power at $\mu + \sigma$ assumes an effect of $0.9$ and saturates almost immediately; power at $\mu - \sigma$ is pinned near the test's five-percent floor, because $\mu - \sigma$ lands at essentially zero effect, where even large samples gain almost no detection. Power at the prior mean is the curve a frequentist planner would actually quote. The assurance curve is the prior-weighted integral across the entire fan, and it tells a different story than any single power calculation. -The story is in the crossing. Between $N=400$ and $N=800$ the assurance curve drops below power-at-the-mean, having sat above it at smaller samples. At small samples assurance is buoyed by the optimistic tail of the prior: it counts the plausible draws above $0.4$, effects of $0.8$ or $1.0$ that even a hundred observations have a real chance of detecting, which the single-point power calculation never sees. The frequentist who quotes power at the mean is being pessimistic about small experiments in a way the prior is not. +The story is in the crossing. Between $N=400$ and $N=800$ the assurance curve drops below power-at-the-mean, having sat above it at smaller samples. At small samples assurance is buoyed by the optimistic tail of the prior: it counts the plausible draws above $0.4$, effects of $0.8$ or $1.0$ that even a hundred observations have a real chance of detecting, which the single-point power calculation never sees. Quoting power at the mean understates what small experiments can do, because it ignores the optimistic tail of the prior that assurance counts. -At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one: the prior assigns positive probability to a zero or negative effect. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\delta > 0) \approx 0.79$. No sample size buys more assurance than $P(\delta > 0)$. That ceiling, not any point on a power curve, is what a planner should know before committing to more data. +At large samples the relationship inverts, and the reason is the ceiling drawn above. Power at a fixed positive effect climbs to one: with enough data an effect of $0.4$ is certain to be detected. Assurance cannot reach one: the prior assigns positive probability to a zero or negative effect. Its asymptote is exactly the prior probability that the effect is beneficial, $P(\delta > 0) \approx 0.79$. No sample size buys more assurance than $P(\delta > 0)$. A planner should know that ceiling before committing to more data, and no point on a power curve reveals it. :::{admonition} The minimum detectable effect leads a double life :class: note @@ -313,8 +331,9 @@ The generative model fixes the baseline conversion rate, places the planning pri ```{code-cell} ipython3 BASELINE_RATE = 0.10 -RATE_LIFT_PRIOR = EffectPrior(mu=0.01, sigma=0.012) +RATE_LIFT_PRIOR = EffectPrior(mu=0.005, sigma=0.012) BETA_PRIOR_KAPPA = 200 # prior concentration, in units of pseudo-observations +prior_prob_lift_positive = 1.0 - stats.norm.cdf(0.0, RATE_LIFT_PRIOR.mu, RATE_LIFT_PRIOR.sigma) def beta_prior_params(p_mean, kappa): @@ -422,11 +441,11 @@ ax.plot( label=r"Power at $\mu - \sigma$", ) ax.axhline( - prior_prob_positive, + prior_prob_lift_positive, color="C0", linestyle="-.", alpha=0.6, - label=rf"Assurance ceiling $P(\delta>0)$ = {prior_prob_positive:.2f}", + label=rf"Assurance ceiling $P(\text{{lift}}>0)$ = {prior_prob_lift_positive:.2f}", ) ax.set_xscale("log") @@ -437,7 +456,7 @@ ax.set_title("Assurance vs frequentist power, Bernoulli outcome") ax.legend(loc="lower right"); ``` -The conversion-rate version runs the same loop, and the loop discloses something the Gaussian case conceals. The lift is bounded above: how large a positive effect can even be depends on how much room sits above the baseline conversion rate, a constraint the Normal likelihood never faces. The Beta prior on the per-arm rates is parameterised here by a concentration $\kappa$ — the number of pseudo-observations at the baseline rate that the prior is equivalent to. Setting $\kappa = 200$ is saying: I am as confident about the baseline as if I had watched 200 trials at that rate. This is a different handle on prior calibration than the Normal's mean and standard deviation pair, and it makes the ceiling claim more precise: the assurance curve asymptotes toward the prior probability that the lift is positive, and what that probability is depends not only on the prior's location and scale but on the scale on which the lift is defined. At a 10\% baseline with a planning prior mean of 1\%, most of the prior mass sits in a narrow positive strip; the ceiling is lower than the Gaussian case not because the machinery differs but because the planning prior encodes a more cautious belief about how large a conversion lift can plausibly be. The generative model produced the synthetic experiments; the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that this matches a full PyMC fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably. +The conversion-rate version runs the same loop, and the loop discloses something the Gaussian case conceals. The lift is bounded above: how large a positive effect can even be depends on how much room sits above the baseline conversion rate, a constraint the Normal likelihood never faces. The Beta prior on the per-arm rates is parameterised here by a concentration $\kappa$, the number of pseudo-observations at the baseline rate that the prior is equivalent to. Setting $\kappa = 200$ is saying: I am as confident about the baseline as if I had watched 200 trials at that rate. This is a different handle on prior calibration than the Normal's mean and standard deviation pair, and it makes the ceiling claim more precise: the assurance curve asymptotes toward the prior probability that the lift is positive, and what that probability is depends not only on the prior's location and scale but on the scale on which the lift is defined. At a 10\% baseline with a planning prior mean of 0.5\%, the prior splits its mass roughly two to one in favour of a positive lift, and the assurance curve asymptotes there, near 0.66. The same machinery produces a lower ceiling than the Gaussian case because the planning prior commits to a more cautious belief about how large a conversion lift can plausibly be. The generative model produced the synthetic experiments; the conjugate Beta posterior supplied the decision, with no MCMC in the loop. We confirm below that this matches a full PyMC fit on a single dataset, so nothing in the assurance machinery depends on the closed form being available; it depends only on a posterior we can compute reliably. ```{code-cell} ipython3 def bernoulli_two_arm_model(n_A, n_B, N, baseline_rate, kappa): @@ -470,14 +489,20 @@ with bernoulli_two_arm_model( progressbar=False, ) -az.plot_dist(idata_bern_demo, var_names=["lift"]) -ax = plt.gcf().axes[0] -ax.axvline(0.0, color="C2", linestyle="--", label="ref = 0.0") -ax.legend() -plt.title(r"Posterior on conversion lift at $N=4000$"); +pc = az.plot_dist( + idata_bern_demo, + var_names=["lift"], + visuals={"title": {"text": r"Posterior on conversion lift at $N=4000$"}}, +) +az.add_lines( + pc, + values=0.0, + visuals={"ref_line": {"color": "C1", "label": "ref = 0.0"}}, +) +pc.get_viz("plot").legend() ``` -The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The decision rule is the same; the ceiling is lower because the planning prior carries a smaller probability that the lift is positive, and that probability — not the sample size — is what sets the asymptote. +The PyMC posterior on lift agrees with the conjugate Beta posterior, as it must under this setup. The decision rule is the same, and the ceiling is lower because the planning prior carries a smaller probability that the lift is positive. That prior probability sets the asymptote that no sample size can exceed. ## The cost of an under-informed prior @@ -516,6 +541,48 @@ ax.set_title("Assurance under three prior commitments") ax.legend(); ``` +## Design prior and analysis prior + +The assurance loop carries two priors with distinct jobs. The *design prior* generates the synthetic worlds the experiment will face. The *analysis prior* reads each dataset and forms the posterior the decision rule consults. Every curve so far set the two equal. Separating them measures a sensitivity the FDA guidance asks for directly: how the operating characteristics respond when the inference commitment disagrees with the planning belief {cite:p}`fda2026bayesian`. The generative model already carries the design prior and the inference model the analysis prior, so the loop runs the mismatch with one extra argument. + +```{code-cell} ipython3 +OPTIMISTIC_DESIGN = EffectPrior(mu=0.6, sigma=0.4) +SCEPTICAL_ANALYSIS = EffectPrior(mu=0.0, sigma=0.3) + +design_analysis_cases = { + "Matched (design = analysis)": (OPTIMISTIC_DESIGN, OPTIMISTIC_DESIGN), + "Optimistic design, sceptical analysis": (OPTIMISTIC_DESIGN, SCEPTICAL_ANALYSIS), + "Sceptical design, optimistic analysis": (SCEPTICAL_ANALYSIS, OPTIMISTIC_DESIGN), +} + +design_analysis_curves = {} +for name, (design_prior, analysis_prior) in design_analysis_cases.items(): + design_analysis_curves[name] = assurance_curve( + gaussian_assurance_at_N, + N_grid=N_GRID, + n_sims=N_SIMS, + rng=np.random.default_rng(RANDOM_SEED), + prior=design_prior, + analysis_prior=analysis_prior, + sigma_obs=SIGMA_OBS, + threshold=DECISION_THRESHOLD, + ) + +fig, ax = plt.subplots(figsize=(15, 4.5)) +for name, df in design_analysis_curves.items(): + ax.plot(df["N"], df["assurance"], marker="o", linewidth=2, label=name) +ax.set_xscale("log") +ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f"{int(x):,}")) +ax.set_xlabel("Sample size per arm") +ax.set_ylabel("Assurance") +ax.set_title("Assurance when the design and analysis priors agree or disagree") +ax.legend(); +``` + +A sceptical analysis prior lowers assurance at every sample size, because it discounts the same evidence the optimistic design prior expects to see. The reverse pairing recovers most of the matched curve once the data accumulate, since an optimistic analysis prior and a growing dataset agree. The gap between the curves is the price of a mismatch, and it narrows as $N$ grows. Small experiments pay the most for an analysis prior that disagrees with the design belief, which matches the guidance observation that trial characteristics are most sensitive to the analysis prior when the sample size is small {cite:p}`fda2026bayesian`. + ++++ + ## The information content of a prior: effective sample size The three curves above differ because the three priors carry different amounts of information. The *prior effective sample size* (ESS) makes this precise: it is the number of actual experimental observations the prior is equivalent to in terms of information content. A prior with ESS of 128 is, before the first participant is enrolled, already as informative as 128 observed subjects per arm. @@ -524,7 +591,7 @@ For the two-arm Gaussian trial the ESS follows from equating information in the $$n_{\text{ESS}} = \frac{2\sigma_{\text{obs}}^2}{\sigma_0^2}$$ -The simulation-based approach recovers the same number without the closed form. For each $N$ in a grid, draw synthetic datasets from the prior predictive and compute the posterior variance under a flat reference prior; the $N^*$ where the average posterior variance crosses the prior variance is the ESS. The machinery is the same prior-predictive loop already running in the assurance calculation — only the inner step changes. In the conjugate case the analytical posterior fills that step. In non-conjugate models — hierarchical likelihoods, logistic regression — the same loop runs with MCMC posteriors and the ESS estimate generalises without modification. Verifying that analytical and simulation estimates agree for the Gaussian case validates the approach for those more complex settings. +The simulation-based approach recovers the same number without the closed form. For each $N$ in a grid, draw synthetic datasets from the prior predictive and compute the posterior variance under a flat reference prior; the $N^*$ where the average posterior variance crosses the prior variance is the ESS. The machinery is the same prior-predictive loop already running in the assurance calculation; only the inner step changes. In the conjugate case the analytical posterior fills that step. In non-conjugate models such as hierarchical likelihoods or logistic regression, the same loop runs with MCMC posteriors and the ESS estimate generalises without modification. Verifying that analytical and simulation estimates agree for the Gaussian case validates the approach for those more complex settings. ```{code-cell} ipython3 def analytical_ess(prior, sigma_obs): @@ -674,23 +741,23 @@ axes[1].legend() plt.tight_layout() ``` -The left panel is a calibration check. The simulation curve — posterior variance under a flat prior as a function of N — crosses the prior variance at the point the analytical formula predicts. The two estimates agree to within interpolation noise. For non-conjugate models where no closed form for the posterior variance exists, the same loop with MCMC posteriors in place of `gaussian_posterior_delta` gives the correct ESS. The conjugate case validates the simulation approach before the closed form disappears. +The left panel is a calibration check. The simulation curve, posterior variance under a flat prior as a function of N, crosses the prior variance at the point the analytical formula predicts. The two estimates agree to within interpolation noise. For non-conjugate models where no closed form for the posterior variance exists, the same loop with MCMC posteriors in place of `gaussian_posterior_delta` gives the correct ESS. The conjugate case validates the simulation approach before the closed form disappears. The right panel reads the ESS against the assurance curves. The dotted vertical lines mark each prior's ESS on the sample-size axis. An ESS of 356 (Informed) means the prior carries more information than 350 subjects per arm before the first participant is enrolled; the curve climbs steeply early because the prior is already doing most of the work. An ESS of 14 (Flat) means the prior contributes almost nothing, and every observation earns its full weight. The ESS converts the abstract question "how informative is this prior?" into a number on the same axis as the assurance curve: experiments whose planned N falls well below the ESS are prior-dominated; those well above it are data-dominated. -This is the quantitative form of the trade-off the sensitivity analysis in {ref}`sensitivity_confounding` interrogates from the other side. There, the question is not how much information the prior commits but what happens when that commitment meets an identification gap — the tipping point at which the conclusion turns is the price the prior's ESS is staking. +This is the quantitative form of the trade-off the sensitivity analysis in {ref}`sensitivity_confounding` interrogates from the other side. There, the question is what happens when that committed information meets an identification gap, and the tipping point at which the conclusion turns measures how much that committed ESS has put at risk. +++ -The three curves disagree most where they should: at small sample sizes where the data has not yet overwhelmed the prior. The informed prior reaches high assurance fastest because it is willing to commit to a narrow effect range; the sceptical prior assumes a smaller effect and so needs more data to detect it; the flat prior carries enough probability mass over zero that even moderate datasets do not push the posterior reliably above the decision threshold. The point is not to advocate for a specific prior. It is to make the dependence of the assurance number on the prior commitment visible, so that the prior can be the subject of negotiation rather than a hidden choice. +The three curves disagree most where they should: at small sample sizes where the data has not yet overwhelmed the prior. The informed prior reaches high assurance fastest because it is willing to commit to a narrow effect range; the sceptical prior assumes a smaller effect and so needs more data to detect it; the flat prior carries enough probability mass over zero that even moderate datasets do not push the posterior reliably above the decision threshold. The point is to make the dependence of the assurance number on the prior commitment visible, so the prior becomes a subject of negotiation rather than a hidden choice. ## Planning as a posterior over future posteriors The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty. -Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability the inference about to be drawn will be useful? The decision-theoretic operation that produces assurance is the same operation that produces the posterior at the end of the experiment. Only the data are still hypothetical. It makes explicit the assumption power leaves buried: a believed distribution of effects. Naming that assumption, and giving it a posterior, is the move this sequence repeats. +Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability that the inference about to be drawn will be useful? The operation that produces assurance is the same one that will produce the posterior at the end of the experiment. Only the data are still hypothetical. Assurance makes explicit the assumption power leaves buried, a believed distribution of effects, and gives that assumption a posterior to argue over. -In each case here, the Gaussian and the Bernoulli, the inference machinery was the same. What changed was the likelihood — and with it, the ceiling that the likelihood's geometry sets on how much assurance any sample size can buy. The same is true across this notebook and its successors. Planning, interpretation, synthesis: three problems, one machinery. The problem changes; its proper characterisation is always a posterior. We have just seen the first of the three. +The Gaussian case and the Bernoulli case ran the same inference machinery. What changed was the likelihood, and with it the ceiling its geometry sets on how much assurance any sample size can buy. 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Forde\n", ":::\n", "\n", + ":::{figure} experimentation_triptych.jpeg\n", + ":name: experimentation-triptych\n", + ":width: 100%\n", + ":align: center\n", + "\n", + "The experimentation lifecycle as a Bosch triptych. *Left, Bayesian Assurance:* before any data arrive, the planner reads possible effects from the prior and asks what the experiment will likely conclude. *Centre, Sensitivity Analysis:* a single experiment is wracked by the biases it cannot rule out, and the model is contorted to see which commitments its conclusion can survive. *Right, Meta-Analysis (this notebook):* many experiments are pooled through a hierarchy of levels into a synthesis that becomes the next plan's prior. Three panels, one posterior machinery.\n", + ":::\n", + "\n", "## The replication-as-evidence problem\n", "\n", - "Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question — \"did it work?\" — and gets, depending on which colleague answers, two incompatible defaults: vote-counting (\"four out of eight worked, so it's a wash\"), or pool-everything (\"the combined estimate is positive, so it works\"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that estimates between-market differences rather than assuming them away.\n", + "Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question, \"did it work?\", and gets two incompatible defaults depending on which colleague answers: vote-counting (\"four out of eight worked, so it's a wash\"), or pool-everything (\"the combined estimate is positive, so it works\"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that estimates between-market differences rather than assuming them away.\n", "\n", "Each experiment speaks about one market. The hierarchy is what lets us hear all of them at once.\n", "\n", @@ -50,7 +58,6 @@ "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", - "import seaborn as sns\n", "\n", "from scipy import stats\n", "\n", @@ -694,7 +701,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -703,7 +710,7 @@ }, { "data": { - "image/png": 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dLJaZIUOG2AP9HnjggbikFRnTGeJHjBiR6cz+XgeInax8+fL24Mz77rtPNm+OfLIEAAAAAMGti7z22msZfkZXGY42QOy4EiVKyIABA6Rjx472itmnMnLkSLuuU6xYMfGzHTt2yLRp0zL8jK7a7TRA7EQa/DVz5kx7QpjT+fbbbz0NEsuTJ4/UqlXrf8Fg+m+5cuU8Oz4AAADCjSAxAAAAAAAM99q8NbL+QGqmn7usfFG5sEyRuKQJiWftPyhp437L8DNZGtSOS5DYcReUKSwV8+WUR6YtkbX7D0e0z9I9B+X2X+bLoBY1pVK+XDFPIwAAgAaM6cA9HXj3wgsvyNdff53pRXnnnXekWrVq9gpSSKw///wzw1nYdSU5/X3jRWeL1w0AAABAuP3222+ydOnS0/69SpUq0rlzZ1fnaNy4sVx55ZUyZsyYU/5dV8XWFbr8vtr19OnTTxvopnLnzi133323Z+fr2bOn/fuc7pyLFy+2J4zR9gCnrr32WilVqpQdEFapUiV7JTMAAADAiWRHewEAAAAAgFCYunmXfLlqS6afK5snh/Q8o2Jc0gRkRAO9hraqK+eWjLyzdcuhI3Lnrwtk1tY9XFwAABA3GtjTv39/eyb4nDlzZvr5p556SlatWhWXtOH0/vjjjwwvT/v27SP6PQEAAAAgGpmtaKyrT+sq1G51795dsmbNetq/f/7553LkyBHxs0WLFmX495YtW3o62UbVqlWldu3aGa4Cp4FibnTp0kWuuOIKqVy5MgFiAAAAcIUgMQAAAAAADLUn9ag89+eKTD+XJSlJnm1cVXKlZIlLuoDM5MmaIv3Ori531iwT8cXaf/SYdJu8SH5cu40LDAAA4qpNmzYybNgwyZs3b4afO3DggDz99NNxSxdObd68eZn+ngAAAADgpZ07d8rUqVNP+/dy5cpJq1atPDlX8eLF5dJLLz3t3/fs2SO///67+Nm6desy/HuzZs08P2fz5s0z/PvatWs9PycAAADgBEFiAAAAAAAYSGc17Dd7lew4fDTTz95du6zULJgnLukCIpWclCR31CorLzSpJtmzRNbElWZZ8vTM5TJi6UYuNAAAiKt69erJm2++meFs7WrGjBkyfvx4SaTDhw/bAxQ3bdpk/5uamiomWbNmzWn/lpycLOXLl49reoDjjh49ag/Y3bx5s2zfvt0OLNW6PcJBf8v9+/fLli1bZOvWrbJ37145duxYopMFeELz8r59++zn17Zt2+z/zfMLAP7phx9+kLS0tNNeliuvvNKuj3jlqquuyvDvY8eOFT/Td0lGYlFvq1ChQoZ/1/IbAAAA4Afu1x8GAAAAAACBM37ddpm4YUemn6tfOK/cUq1UXNIEOHFBmcJSKnd2eWjqYtkWQdCjem3+GtmdelTurVNOkpKSuPAAACAumjRpIj179pQXX3wxw8+9/PLL0rp167iUU1asWCF//PGH/PXXX/b/1tnYNUjhZAULFrRnrtdgt0aNGsm5554rOXPmFD/S9Ot30oC7lStX2kFfOlhPA2pUvnz5pFSpUvaKYHfeeee/9tfgm9PR65CSQveqF1avXi2//vqrzJ8/X5YvX24HDehvpIEE+fPnl8KFC0vt2rXlrLPOsn+rPHnyuM4Xv/zyi8yZM0cWLlxoB0HqwNJDhw5Jjhw5pEiRIlKxYkU588wz5YILLpDKlStLIh05csReSWLmzJn26naajzVw6OSgiuzZs0vp0qWlSpUqdtrPPvtsqV69uvjZ4sWL7d9Cfwf97TUY9fj9mTt3bilWrJg9APeMM86wnzVVq1aVMNFnjP6uy5Yts59Rum3YsOGUQX9ZsmSx86Y+f2vWrCkNGjSQFi1a2M8xP9PgYs2/U6ZMkaVLl9qrauhzWIOQs2XLZq+sqfm2WrVqdr49//zzff+dEJmNGzfKrFmz7Hv7eP7W5+3Bgwf/9VkNnNf7XQfxa/4+/gzLlSuXkZfbbfklEvqM0XeKrg60YMEC+zy7du2yfx99n+gzuGTJkvY7RZ/Bem/qbwQgPjJbuSujlb+c1k+LFi1ql8NPRd/jWjbX8ogfZRZsrHU3rxUqVCjDvxPgDwAAAL+gFwMAAAAAAMPsOHxEBsxZlenncmZJll5nVZEsBNHA53Sluw9a1ZUHpy6RJbv/O3gmM8OWbpTdR9LkkQaVJCWZQDEAABAfHTt2tFcK06CsjIJndIDgeeedF5M06GDjzz77TL788ktZsmRJRPvoAGLd5s6dKx9//LE9gPviiy+WO+64IybBNK1atbKDJk6lb9++cvXVV//rv2vwz7Bhw+zrqysunc6OHTvs7XSDnjWIIWyD/jIKGpo4caKUKVMm6mOuX7/eDqaK5rjp6ekybtw4ef/99+3B6RkF0eim+XPMmDHy7LPPyhVXXCHdunXLdGDmyTRQ4a233pIJEyacdlU8vSd00wH5GrimgZqNGzeWHj162EE58aQD9j/44AP7OmW2OoLS73Q8EOP4KoQaVHXttdfK9ddfbwfAee3111+XN95445R/0+umz4hTBb199dVX9ndbter07RG7d++2Nw0s0u/Tv39/qVWrltx11132M8fr4NlHH33Ufhaeyv333y9du3Z1fQ59Hukga32uT58+3c6TkdJnjq4sppsGlukzTgNVNVDspptuitl7wulzWD8/dOhQGT169CmDgpQGZuqmQY+zZ8+WUaNG2cFCl1xyidxzzz0JD9BEdPS31KBPzeOavzXYPJp7Q/OMbhpUqO8GDSLUQPmbb77ZDhqLlQ4dOtjBWNHkb7+XXzKix9Wyn55Hy5mnovesbhosooFk+v7VZ26zZs3soDQN4Au6jH53P9BVnTKbTALhpfdpRvlTg6t1QgMv6apkeo9//fXXp/y7Bqpqmb1+/friR5kFmMdi0pXMAtMIegcAAIBfECQGAAAAAIBBtBOr/+xVsvdo5oMbu9evIKXzeD+gDIiFYjmzyzvn1ZYnpi+VqZt3R7TPN6u3yt4jafJM46qSPUsyPwwAAIg5HYj38MMPyw033JDh54YPH+754H8deKiD94cMGSJ79uxxdSwdRKyDhzXoQwc863fS1Z8SQQMNdEDtd99958nxdIB6WlraaQdK6t9YTSx6ulLdE088YQeFOAlC+PTTT+3AqT59+tjBQpHkUc0Xn3/+uR2cFi0dpHvjjTfagZ0PPfRQzH9zXU1LA6J0kK6T9J5IV6h64YUX5N1337VXL7zmmmskkXRVoSeffDLD4LCM6Ipj3bt3twPQ+vXrZ6+kE4S2F10RSJ9LP/30kx345hV9Bmkwo24axKj3Vd26dT07vtM0aYDPm2++edpgzMzeT99884388MMPdkCgBujp+xL+pL+3Bj1q/v75559PGxDohAaU6nF1a9mypTz22GP2yoJh5HX55XQ0eO/pp58+bXBYZs8yDQDU7cILL5TevXvbqw4B8J4GY2X0PI1VoGZGQWLHnyF+DRLTlYczK197HXyuE6fEe/UyAAAAwAla1gAAAAAAMMjEDTvk1407M/1cixIF5YoK0c9MCyRSrpQs8tLZNeTKipHnXb0fHpy6WA6nBXNVCAAAEDw6qF9XgcnI5MmT7UFtXlm8eLG9OoGukOQ2QOxEGszyxRdfSNu2bR0F/7il57zyyis9HWCd0cA+/b66mhqio6t4aaCS2zyigTYaLKTBKBnRlZo0eFFXKHITcKWD4z/88EM7YEUDF2Llt99+k0svvdRe0cptgNiJdMWZxx9/XDp37pzpgNZYGTx4sL1yi9MAsZMD93SFtIxWofMLvd6dOnWygxS9DBA7md5TGsyowb+J/q6vvPKKowCxk4PFNNCsS5curo+F2Fm0aJH9G40dO9bTALGTaSCkll0yCl4IqliUX05FV9LU+9NJgNip3uWa5r///tuTtAH4p8zurXr16sXkkmUWaO7ne7527doZ/l1XCfZaZs/TmjVren5OAAAAwAmCxAAAAAAAMMSu1KPy0uzMB2blz5Yij59ZSZKSkuKSLsBLKclJ8miDStKldtmI95mxdY88MHWxHCRQDAAAxMl1112X4d81UERX6fDCt99+K9dff729ulCsbNu2zQ4E0VVg4kVXtbj11lvtQBgvFShQIMO/x/M7hoGuNte1a1d7NTDPVsfu399eye50AZFeBSUd98svv9ir2cTCsGHD5J577olpEJfeK+3bt5eVK1dKPD3zzDMyYMAATwPftm/fLrfffnvcv4ufaWDVSy+9JM8++2zcz71lyxb7/TJz5kzPg4M0INTLvINg0iA0Xa00kYGQQSm/nPyu1FW/Xn31Vc+fwbfccovMmTPHs2MC+P8rp2akVq1aMblUFStWlFy5cmUYGOxXZ511VoZ/nzp1akye4RmtbBbW1S8B4P+xdx9QslVl/rDf6u6bc845k6MiCIooEgwgKgoqiBkwjBnHNM6MMuaIYiKpIChBMUcEBEFyujnnnFOnb+2aj/kr3j6n7+2q6q7u51mrli7O23V27zp16lTf/TsvANVHSAwAAAC6iC88vCg2723IrfvQkZNjSM/uFRkTlEMKOF44c2x84pipUdvKrOOD67bGu+96KrbX579HAADa6gUveEFuGCkFU9rqRz/6UXzgAx+I3bt3RyWCCu9///vjd7/7Xdn3lRYrpuBRObo7pYWSWVJ3qmXLlpV8v51RWkT50Y9+tCxBj49//OP/smg1vS4XXHBBSbvwPS11zEldoUrpmmuuif/+7/+uSBBm+fLlxbkpR0eFfUldpX74wx+W5blTZ653v/vdOk09ww9+8IP4+te/HpWSulK++c1vLtsx9cc//jG+/e1vl+W5qT4pCJm6LVa7cl6/PHO+rr/++rIF9y6++OJYsWJFWZ4fuqq5c+dmbp88eXJZ9ltTU5P5/Wfp0qUlu9lDqY0fPz6zw1oKnZeyo2u66UpWZ7XTTjutZPsCAIC2qmvzMwAAAAAd3h0rNsbvluffofaUMUPilLFDKjImKLczJgyL/qkz3r1zYk9Tc279oxu2xbvueiq+csKs6Nfdn80AgPLp1q1bPOc5z8nsSvXggw+2aR9pMfV//Md/tCpgf8QRR8Szn/3sOPbYY2PUqFExaNCg6NevX2zbtq0YuJk/f37ccccdxUdW54uGhoZiKO2GG26ImTNnRjmkwFvqMLNjx459bk93b3/+859fXDCY7uY+dOjQ4u+YfpfUfSjN6x/+8IcWn/+4444rBoJakhZ2v+UtbykGMtJz03K3kfe9733F8OAzpWPrxBNPjBNOOKF4vKV5rK2tLf5M6gCWwiH33HPPPn/2aWlbOr7TIvj0+qbj4tJLL93nQtD0fkudBtJxkY6PdFz07du32L1r1apVxa59aZ9bt27NfDn/53/+J174whcW3x9t9fvf/z4+/elPt6r28MMPjxe/+MVx9NFHFxfDpvlrbGwsvhfTYtXUJSF1DMwLx61du7Z47P70pz8tPke5/PKXv4wrr7xyn9sGDBgQxx9/fPG1HzFiRPG1SN0r0tjT+NLrngKy6f/nLaT+zne+U3zNq02a+9QNZMaMGcXz5JQpU6J///7F/54eabF2Ol89fc56/PHHi5260iN1B8qSQmJHHXVUcY7LKY0jnetbWtCeFrI/73nPKx676TVOj/T5kF7n9Dulz5L0WucFZdLvc/rpp8eECRPK9JtQaun8ePDBB//f8Z3Ouf94fKdQbDq20/k2XVs89thjxWMhHeetCQcfeuihMXXq1Kp84cp9/fKPHTy/973v5dal507XfSeddFLMmjUrhg8fHj179iyOL4WuH3nkkeJn1eLFi//lZ9PnT7oxwDnnnNPK3x7Ik3UTimHDhkWfPn3KNonp+rKl8FP6zE+h0I567k2dGdP5aF/S+Sxdk37oQx8q2U0QWrq5Qzqnpk6LAADQUVjtAgAAAJ3clr318T8PL8ytG9C9Lt53xMSKjAkq5bmjBsVXTzwo3vfX2bG9vjG3/omN2+PSO5+Mr544KwZ071aRMQIAXVMKSWSFxFJIYt26dcVFgfvr4YcfLi6mzlJXVxcvfelLi51gWlr0N3jw4OIjbU93Rk8LnK+66qr47ne/G9u3b9/nz6Q7zacuPz/72c+iR48eUWopGLJy5cp/+e9pofOHP/zhOOSQQ1r82RSGe8UrXhEf+9jHigvT9yWF9/KkINPZZ58d//7v/14M76RFgfyzT3ziE8UQ1j9KQYFLLrkkzj///GJw65nScZZCeq997WuL4YF0DD/wwAMtTu1DDz0Uv/nNb4rH5uWXXx6zZ8/+p+0peHbuuecWu7ak4/iZnu6acOaZZxbDZZ/97GeLAaqWpIX66dhP4Zi2LgJuzWLVFLRIx1gKhz1Tmr8xY8YUHylUkBbHpuDi1772tWKXl5akrk/pffKNb3wjymHNmjXF99czpfDBu971ruL7L70uLb0WKRCUgkNXX311fPOb38z8XVIIIi3GzevK2BGk3z91kHzRi15UDOTu6/j/R08Hq1JoJP3c0508rr322mKHyBQSbGkhd5r/9NnSvXv5OsSnLnH7WkyePtfSsZhCcC1J59h0Dkjvg8985jOZoZcUBv3KV75SXJRNx5XCBaecckoxRJtCiinomCUFkdK1TQpIps/QJJ2/03s6XTu0JJ0bUnfKFESvRuW+fnn6PNGaGwSkz40PfvCDxddgX1Jo7NRTTy2+n1MnnvQZ+8ywWAqtZYW5gdZL36uyOl6l671yGjt2bG5H2o4aEjvjjDOK18Dp+29LnXuf9axnxcknn9ym/aTPqHRTiZakc3S5ur0BAMCBEBIDAACATu4rjy6JDbvz/9H+fYdPjCE9y7eICNrLEUP7x7eed3C8+66nWvVemL15R1zylyfjayceFIN6CIoBAOWRumHkSYvw00Le/ZFCFamDU1aHlpEjR8aXvvSl4mLu/ZEWdr/jHe8oLsZLHYlS4GRf0kLiK664Iv7t3/4tSu2ZC6xT2CItiN6fbhYpvJY6S7W0SDIFHe6+++7M50ghvhSGmzZtWjGIlMItOov9P88MbKVjLQWT9hXW2pe0EDUFhdLxdtddd7VYlxaFpm5kP/7xj/8llPPtb3+7uNC9NVLQKHX2Su+NrADVTTfdVOxe1atXrzhQn/zkJ1sMWT7tNa95TTEMkRcmeloKBF100UXF7k1vf/vbi0GBlqTOMClElI7ZUtvXOeGss84qvkfT+aO1v8tb3/rWYnDiTW96U4tdd9K57sYbbyzWduTzfPodUhAmLzjTmiBOOiZe9rKXFYOK++ru8/RC7jQv5exm8cyAWHptU4hkf46pcePGFT8nUoe+73//+y3WpSDo6tWri+9NOpYU+EtB8+c+97ltfq7Udexzn/tc8fhOganUWbKlcHDqNtjWxf7todzXL0l6vqxwbQrppvBxCmq2RgrBp7lOAe50/nlmp9WswFpHdN1117Xp52+++ea47LLLWtw+Z86c6IxSF8is46qaOh2WsxtXW+wrQPqPyv0dI+/5Uyexjiqd11LoPIW00s1KnikF69/73vcWr/MP5No3BfDTTSK+8IUvtFiTuuNmnRsAAKA9CIkBAABAJ/bX1ZviF0vW5dadOGpQnDquvP/YCO1p2oA+ceXzDi6Gv9bsannB9NPmbdkZF//lifj6iQcJTwIAZZHuNJ4WCGd1YWgpBJAlhb9SSCCrO1G6E3paKHmgJkyYUOzk8YY3vCHmzZu3z5rUcSyFFA6kE1prpflLXWZSB5NSSp2n8kJiT0u//3/9138VFx6mQEhaNJ86P6X/35Y57kxSgODrX//6fgerUlgo/Vxa0Llq1ap91tx///3FMGJTU9M/Bf1SwCyFUPZX6naVFnmnINW+bNmypRhQSEHJA5GeNyv0llxwwQXxkY985ICeP3WFScG58847L/M8kI7X1KGqHN3+/lEKcKXQ6oE48sgj48tf/nIxkNqSW2+9tUOGxHr37l3s+pW6hpXaYYcdVnzu17/+9S0GdVPoKoVAKtHlcMCAAfGtb31rv0PHT0td9VII7Je//OU+tzc0NMTPf/7zzOOAykqBvVtuuSWzY9yBOvHEE4vn73R8P7Mb5dPSNUw1hsTKff1yxx13ZH6+pPNBCmWmLrL7K31+f/7zny9+ZmR13KRzSp1W03u+2j0dJOqoQbyOHBJr6Xzckb5Xp+8M6eYS+7pRSgo5vuc97ylew7/tbW9rsYviM/3973+Pr371q/G3v/0t8zroyiuvjH79+rXpdwAAgFJr2+2qAAAAgA5re31DfObBhbl1/brVxoeOnFyRxUPQnsb17VXsKDaqd+sWQi7cuisu/suTsa4VoTIAgP2VAjB5IZa0cH5/LFu2LK6//voWt48ePbq4mL8U4aXUESotcE5hiH3Zs2dPZneYUvj4xz9e8oDY0+GU/b3TfAopPfLII8X5TWGG1HXjhS98YXFBYlrQft999+V2j+qMUmArHScH2nkr/VxeR7p/7G6QFt6nTmAHEhB7WurSlNXB6w9/+MMBP3fqnJQldbFLXXTaInUzSPtJ55isTng/+clPopzOPPPMAw6IPe2kk04qduFqyYIFC2Lhwvy/e1Ra6qxVjoDYP77G6VzT0nGa3hPpnFNu6e9IqfvTgQbEnpa6b/Tt27fF7S2FNmkfKfxdjoDY01KHzhR4z1q0n653qlk5rl/yPl9SMOJAAmL/+H5Pncra+n4H9n0Tgiz9+/cv67TlBZw2b94c1XBTinQeTN2BW3LbbbcVr09TkD7diCBd06fvb4sWLSpeUz744IPFbrspUJg6W6a6rIBY+jxMXcZa27kYAAAqSScxAAAA6KS++uiSWNuKcMt7DpsYw3q1vHgMOpPRfXoWg2KX/OWJWL5jT2794m274u13PFH8Ge8ToCVNq9a2z+TU1UbNsCH7/WNN6zZENDRGe6gZNXy/f6Zp05aI3fnn7HIoDB0UhYygwL40b9sezdt3RkeeUzqO4cOHZwYc9jcklhbGtdSZLC3uTXdCT/sslXQX9hQC+c///M99br/55puLAZ+ssMqBetaznhWvetWrolxSp6W0YHD27NkH/BxpEXt6pMWGSU1NTUydOjUOP/zwYoAkBYJS2K6zSr9v6lqSFf5ojbSYM70erVmgmjqBzZw5s037mzhxYnGhaeo2sC8HGr6555574oknnmhxe3qffPKTnyzOW1vNmDEjLrroomKQqCWp21/qOFaOG8aMGTOmGCYohdQV4je/+U2L21M3udRBoqtJv3Pq5phCqPvyu9/9rqxBtSTt/3nPe16bnyd9LqXuLqlD2r48/vjjsWvXrgMOm1J9UlfO0047LX7961//y7bm5uZicPCNb3xjVKNyXL889NBD8fDDD7e4PQWnL7nkkjbvJwVT07n9rLPOisbG9vk+CZ3R1q1bM7e39Vo6T97z54XYOorUjfJnP/tZsSNvS50V02dIChunR1v3lb7nDBmy/3+TAwCAShASAwAAgE7ovjWb47bF+QvWjxsxIM6cMKwiY4KOYmTvHvHN5x0cl/7lyViyfXdu/fIdu+PSO5+Mb550cAzuuX9BAaBr2POZ7Lu2l0th5LDo+ZH9X+y39zs3RPPqddEeen31k/v9M/W3/CaaHn4y2kOPyy6Own6GsBruvD8afn1HVMqBzCkdR7r7eJb169e3+rnS4r1f/OIXLW5/+ctfHoceemiUWlro/M1vfnOfY02hnj//+c9x6qmnlny/H/vYx8raDTl1SEuhuxSCWL58eUmeM3Ubmzt3bvFx0003FcefOnKkEFTq7lHuLgGV9vznPz+OOeaYNj9PXV1dsePKT3/608y6oUOHxoUXXhilkLpXtRQSS124UoBz5MiR+/Wct956a+b2FOoaP358lMrb3/72YseEVatW7XN7CjCmBbLHHntslNqll16a25WitVJ3hhSunD9//j63P/roo3HuuedGV/Ta1762xZBYCiWWU58+fYqhzFJJ58CWQmINDQ3FgGUpzidU1/G9r5DY08d3tYbEynH9csstt2RuTx0qSxXYnz59evG1+cEPflCS5wMiduzYkTkNLXVuLuVnepYU1K4WqdtqujZKNxFIN0v461//WvwOVsqO0+k6N91QAgAAOrK234YMAAAA6FB2NjTGpx9suRvB03rX1cZlR00p68JK6KiG9/rfoNikfq27E3nqKPauu56MLXv33ZkDAOBADBgwIHP77t35gfan3X777bFnz7677qXORO9+97ujHHr06FFcLNySO+4ofWgyLc5Li5TLLXVDuvHGG4sdTcoh3cn+gQceiE996lNx0kknxSc+8YkWAz3V6HWve13Jnqs1HZFSYLFUi+CPO+64zO1ZHQD3JXX4S52dWpLG/aY3vSlKKXVdygtRZAVLD1TqqPCSl7ykpM+Z3h8tWbBgQXRVqTvQtGnT9rktheq2b99etn2fc845Je1scthhh2V2Vtzf9xzVL4UCWwpPP/LII1GNynH9kjp6/fa3v21xe9rfC1/4wpLuM4WQa2trS/qc0JWlMHTeDRPKKXUJzNJSp+qOLN0EIYXFUufJk08+uU3Plf79LN2QIAWXb7jhBgExAACqgpAYAAAAdDLfeXJZrNq578Wh/+idh04odlSCrmpIz+7FoNjUAa27G+u8LTvjPXc9Fdvrs//hHgCgtfICLS2FvvYlLYBrydFHHx2jR48u2wtz4okntritHN1sUjihUlLg5aqrropPfvKTMXDgwLLtJ92hPy06fNGLXhRf/OIX9ysg2FHv4n/88ceX7PmmTJmSW3P22WeXbH+jRo3K7KqwdOnS/Xq+hx9+OLNLRFq8Wo5OcimslbWw+O677y75PlPXwlKF9Z42Y8aMFreVqtNftcoKnMybN69s+02hzEr+LqnzHV1LOndNnjx5n9tSp9LU1bHalOP6JXXZ27RpU2aXvnJ0ws0LUwOtlxfCKndILC/0WY0hsdRJLHX8Sh2bW+oOvD839vjxj38c733ve4vdydJnEAAAdHTl/RYBAAAAVNSczTvix/Pz7zx/zLD+cfak4RUZE3Rkg3p0iytOPCjeeddTxfdPnic37Yh/u3t2fOW5s4rd+AAA2tqFK0trg0I7d+4sLoRryemnnx7ldPDBB0fv3r2L43imFStWFBcvDxo0qGT7e/7znx+VlO4en7qlpcDND37wg/jhD38Y69atK8u+0iLMK6+8sth16itf+UpFOqaVw1FHHVXSrtUtBQWeNnTo0JgwYUKU0sSJE4uL7/dl/fr1+/Vcf/vb3zK3v+xlL4tyhRxPOOGEFjv6pbDb6tWrY+TIkSXt/lNqWe+DjRs3FhfvdtUu6ek1bsmSJUuKnYtKLQVmW+pg1hbpOe+9996SvOfoGsf38OHV9bfNcly/ZH2+pPNiqTs7/mP4rBxBYzqeyy+/vPigfFJHwCzl7tyX9/x5nc46kkcffbR4vKZuzaX25JNPFh/pu9p5550X73jHO0raVRUAAEpJJzEAAADoJBqbm+MzDy6Ixubsup61NfGRo6Z02QVU8EwDenSLb5x4UBw8uHX/qPvohm3xgb/Ojt05/4APAJAnBRuy1NS07p/yHn/88cw7vJcjtPHMu9tPmjSpxe2zZ88u2b7SgvDUwaI99OvXr7gYMN2N/mtf+1qceuqp0atXr7Lsa+HChXHuuefGX//616hGRxxxREmfL4UQu3XrVrH9JVmd4/a3g8BTTz3V4rb03bycHVnyOrqlxa6lVI5Q0uDBgzMXVm/bti06g3Qef7pDUgrYpi5peY8s5eq0lF7jcvxNKet13rJlS8n3R2Xt3bu3GBpfs2ZNq4/vrO455Qprl0u5rl/SNWBW2LlcnWRL2S0Uurq8kFZeiKyt8p6/3J3MSqGpqanY5Svd2KMcAbF/lG6M8t3vfjfOOuusYrdgAADoiDr+VTwAAADQKj9dsDqe2pTfCeniQ8bHmL49zSr8g37d6+Krz50V77rzqXhi0/bcufn7uq3x4XvmxmefMyO617oPEwBwYPbs2dOmTmOtCXmkRYdZAa5KBGpWrlxZ0q5l7S2FlVJALD1St7f77rsv7rnnnuKCxPRaZAX29ncB4tve9ra46qqryh70K7VyHHMpKNZSUKTUXcSe3l9W2GF/zJ07t8Vt48ePL2sXgoMOOih3bC94wQtKdh7ICvocqD59+rTpXNrRpO5n6XzxyCOPxPz584sd3VKgq9RhtxTIKYdyfaZkvc7V9hp3Zak7YTq+UzeXFHh++vjeV7fRtr6Pqkm5rl/mzZvX4raZM2dGuYwYMaLYJbZc5xnoSrJuhFCJTl55IbG88bW3NP4PfvCDcfvtt7fq3JW6Oh599NExa9as4rVreqTvb+l7Rupcmj6//v73vxc78WZ9di1btizOP//8+NznPhdnnHFGiX8rAABoGyExAAAA6ATW7NwT33xiaW7doYP7xqumjKzImKDa9O1WF19+7qy45C9PxNwt+YuX7lmzOT5639z49LOnR10ru3wAAOzPoveePVt3c4cUMsjqXFGubjL/qHv37pkLxkulHGGgtkiv0UknnVR8PB0eSp3Tnnjiif97pAXcBxocS893ySWXxM9//vPia1ktUte1SobEBgwYUJb9tWR/Xs/UMXDVqlUtbk8LVMspPX/q+tRS58K8blSlCouWMyS2v6G99rB9+/bi+/hnP/tZsetE6nhRbinEWg7leL/lvc7V8Bp3ZSmwdfPNN8cvfvGLkncnrPTxXS7lun5JIbz2CIk9/fwpJA+0TV6nrnKHxPKuazt6SOyyyy7LDYilmzJcfPHF8ZKXvGSfv0/6LpuuQ1L3xcMOOyxe97rXFbu7/uhHPyp2DduxY0eLr80HPvCB4muYbiACAAAdhZAYAAAAdAJffGRx7GzIXmBUWyjEh4+aHDWFQsXGBdWmf+ooduJB8Y47nohF23bl1t+xclN88v758R/PmlZ8jwEA7I+87gu9evVq1fNkBVDStlNOOaVdX5itW7d26PBRKaUFhmlhYXr8YxgwhULuv//+uOuuu+Khhx7ar+dMCxQ//vGPx7e+9a2oFuUIkdRk3JihHMdF1v5aClzty4YNGzIX306cODHKKc3N0KFDY926dfvcXsoQaf/+/aMcsl6L/X09Ki0FWVI3wO9973sl7xSWp1zdt9rrdd6fwFKpu1bt7/yUa446mvT5/s1vfjN++MMfVrzbW7V1lyvH51S6jsz6fJk8eXLJ9/nM5xcSg/KH4cv9mdZSAGp/v5O2h/T5c9ttt2XWnHnmmfGpT31qvzv3ppsfpGDZ6aefHu95z3uKNwJpKSiWOpnNmDGjw93QBACArktIDAAAAKrcHSs3xp9XbsytO3/6qJg6IPsfHIGIQT26xddPPCjedscTsXxH/p2pf7d8Q/Sqq42PHDW5eId+oOvpcdnF7bPjutoD+rHub3lNRENjVItuZ7844vTnt8u+C0MH7ffP1J14bNQeeXBZxkPn01Jo42nDhg1r1fNUolNYR+n2sb+L+zqCHj16xLOf/ezi49JLL41ly5YVFzNed911xQBYa/zpT3+Ke++9N4477riolt+5klrbda89pMBKex/TaR8tnW/yxtdZXof2kDoJvu9974tFixa1y/7L1a2so7/On/3sZ+OWW25pt/2n8/w73/nO6Ozuvvvu+PCHP9xu1yCV6MZXSuU416cQcnsG6zt6cB+qRV6wOC/E1VZ5z1+uDqJtlb5Tff7zn8+sOffcc4sBsbaYNGlSXH/99XH++ee32C1z165d8aEPfajYeaxUoXcAAGgLITEAAACoYjvqG+PzD+cvNhrdu0e8aebYiowJOoOhvbrHFSf9b1Bs1c78u1P/bPHaGNijLi45xN1CoSuqGTU8qknNsCFRTWoGdcwFSS0p9OtbfEBrrF69OnP7qFGjWvU85V442FZ79+6t2J32q8G4ceOKIYILL7yw2F3oyiuvjMbG/PBuqq2WkBitD0lWIiSWtZC/2rrxVIs///nPxa4TadEwdDY33XRTfOITn2jVZxflu37J6y5U7mumznBNBh1BXghry5YtZd1/XqfT1FGrI7riiisyz4PpBh2pG3Mp9O7du9jV+RWveEWsX79+nzWpW/Rvf/vbOO2000qyTwAAaAu3LgAAAIAq9u0nl8XaXfkLLj905OToeYDdRqCrGtG7R3zjxINiWK/uraq/ds7K+NHclWUfFwDQOaQFbStWrMisGTlyZMU7dZVDc3NzyZ6rtrbzfK9J4aB3v/vdce2118agQfmdC++8884WFyVSvSHJ9g6JdfTzRzV64IEH4l3vepeAGJ3Sr371q/jYxz4mINYBrl/a+/NFJzEojcGDB7epa2C5u1u35ntKpaU5uf3221vcnrp5pc+qurrS9U8YMWJEXHzxxZk111xzTcn2BwAAbaGTGAAAAFSppzZtjxvnr8qtO3XckDhuZMe82yN0dGP69iwGxVJHsU176nPrv/LYkhjYo1ucMWFYRcYHAFSvefPm5YanpkyZ0qrn0smjuh1zzDHFO+FfcMEFmQu+0/Fyxx13xDnnnFPR8VFeTU1NZZ/irHNEWkRL6WzdujXe+973tqpDWwqNHHLIIcVHOt+PHTs2hg4dWlyMnTpWpEd6fbIWOH/ta1+Lr3/9615CKmLZsmXxkY98pFXh7+7du8fhhx8eBx98cEyaNKl4fA8ZMuT/ju+ePXsW3wNZ4akPf/jDccstt5T4t+g6yn1+9/kBpTF69OjM7eW+SUTe848ZMyY6ml//+teZ35tOP/30mDZtWsn3++pXvzq+853vxKpV+/53uQcffDAWL14cEydOLPm+AQBgfwiJAQAAQBVqaGqOzzy4MPKWkvXrVhvvOcw/SEFbTOjXK75+4qx4x1+ejK17G3Lr/+uB+cX33omjs+8CCwB0bQ899FDm9kKhUFxY3RppITbV7aijjoq3ve1txcBHlocfflhIrMr06NEjc/v27dvLPoasfTh/lFYKbK1evTqzJi1avvDCC+PFL35xmzvxlLJTI+S5/PLLi51Qsxx55JHxhje8IU4++eTo1atXmybV8Z0t7/xd7s+Xbdu2lfX56Rg2btyY+76vBimg2qdPn+iIUte/gQMHxubNm/e5ffny5WXdf97zp5BvR3P//fdnbj/11FPLst9u3boVP99+9KMfZY5NSAwAgPYmJAYAAABV6KYFq2PO5h25dZccMiGG9LRgFNpq6oA+8bXnzopL7nwytte3fBf+pLE54t//Nje+8tyD4shh/U0+ALBPd999d+bMjB8/vtXhgaxF2GeffXZxUTcdXwqNXH311ZmLrufOnVvRMdF2qWNOlh078r/bt1VWUKCtIQ7+eSH9DTfckDklF110Ubz//e/P7J60v53LoBLmzJkTv//971vcnjreve997yse46Xi+M6WurG1Z0isEiFn2t9nP/vZTtHR7zOf+Uy84hWviI5q3LhxLYbE1q1bVwzq5V1THqilS5dm3rikI3YSy7rhSupy+JznPKds+z7hhBMyQ2Kpm9irXvWqsu0fAABaQ0gMAAAAqsyanXviyida/oe7px02pF+8fNLwioyJzqcwsH90f+Orcmu6kpmD+sbnj58Z777zydjTlH239rT9fX+dHd963sExfWDHvEstANB+0qLavLufH3vssa1+vsGDB8eSJUv2uW3ZsmX7PT7ar4vA8ccfH7/5zW9arFm5cmVFx0Tbpfdne4cgsvYxZMiQsu+/q/jFL34Re/bsaXH7JZdcEu9617tKuk8hmn+VgtHC0aWXFxL51Kc+VfJOl47vbHnnbyExqB7Tp0+Pxx57rMXtCxYsiEMPPbTk+21qaopFixZl3riko91QIHWZTMG5rHPjgAEDyrb/SZMmZW7P6ygLAACVUFORvQAAAAAl88VHFseuxqbMmtpCIS47anLUFApmngNS6NUzao88OPORarqaI4f2j08fNyNqW/HW2tHQGO++66lYvn13JYYGAFSRX/3qV7Fr167MmpNPPrnVzzd69OgWtwmJVZdjjjkmc3tWlzE6pkGDBkX37t0zF/2W06ZNm4odrloyfLiby5TKHXfc0eK2WbNmxcUXXxylJkRDRzi+n//855c8IJY4vvNDyKmDW0tauoFAqZT7+aErOfjggzO3P/nkk2XZbwqIpS5lWdcvHU36bGhsbMy89m7PG0C01BEOAAAqSUgMAAAAqshfV2+KP69seXHX014/fXRM7t+7ImOCrua5owbFR4+e2qrajXvq4113PRnrd+0t+7gAgOpxww03ZG7v0aNHnHDCCa1+vgkTJrS4be3atYJFVWTYsGGZ27O6FNFxjRs3rsVtTz31VFn3nff8qUMEpfHwww+3uO1Vr3pVZpjjQM2dO7fkzwnPtGXLlli4cGGLE3PeeeeVfNL27t0bixcv9mJkKBQKMXbs2Ba3z549u6zzV+7nh64kLySW1WWsLfKeN29c7SEr1PZ0d+Zyynv+cndxBACA1hASAwAAgCqxp7EpvvDwoty6MX16xBtnjanImKCrOmPCsHjPYS0vxv5HK3bsiXff/VRsr28o+7gAgI7vj3/8Yzz++OOZNaeffnr06tWr1c956KGHtritubk57rzzzv0aI+2nX79+mdt79ux63Xw7g+nTp7e4bdWqVcVuX+WS13lixowZZdt3V7J+/frMQO4LX/jCku9zzZo1sWLFipI/LzxTVlgrfS4df/zxJZ+0dK2UgmJkmzp1aruEuFKHynQjgq6kpiZ7iV265oYDdcghh0SfPn1a3H7vvfeWZXLvueeezO3Pfvazo6NJN1Rpzy6Uec+fNz4AAKgEITEAAACoEj+YuzKW78i/a/yHjpwcPWtrKzIm6MpeO210XDCjdYHM+Vt2xofvmRv1TU1lHxcA0HGlxc6f//znc+vOP//8/Xreww8/vNhNoiV/+tOf9uv5aD+bN2/O3N67t47R1SivC8Ndd91Vtn3/5S9/aXFbOm8cdNBBZdt3V5IVlujWrVsMHz685Pt88MEHS/6csL/Hdzq20zFeao7v1sk6hy9YsCA2bNgQ5XD//fdHV9O9e/fM7UKNtEXqNnrssce2uH3ZsmUl767Y1NQUd999d4vb+/fvXwyvdTR5nbzKefOFp0OyWbLCfgAAUCl1FdsTAAAAcMBW7tgd18xenlt36rgh8ewRA800VMg7Dh4Xm/fUx22L8++efP+6LfFfDyyITx4zNXMRNwDQeX35y18uLtjNctRRR8Vhhx22X887ZMiQ4s888sgj+9x+xx13xI4dOyxYqwJ5XTlGjx5dsbFQOscdd1zm9p/97Gfx0pe+tORTnrqUZS3kTx3OBg8eXPL9dkW7du1qcdvAgQPL8h3wtttuK/lzwv4e3+kapBwc362T1eWnsbExfvWrX8XrXve6KLWf//zn0dXkBT/27NnTKTsIXX755cUH5XfSSSfFn//85xa3//KXv4yLL764ZPtL14jr1q1rcfsJJ5wQtR3wZoQpsJk+e1oKwab/nr5TlSOgnzz11FOZ20eNGlWW/QIAwP7QSQwAAACqwBcfWRx7mpoza/rU1ca7D51YsTEB/3vn/Q8dNTlOHtO6hZW/Xro+vvXEMlMHAF1QWkz7/e9/P//a4kMfOqDnP/XUU1vctmXLlrjmmmsO6HmprPvuuy9z+4QJEyo2FkrbSSwrjPXXv/411q9fX5bzTuoS0ZLnPve5Jd9nV5XVSWnnzp0l39+SJUuKAWBo7+M7hdBL7Z577om5c+eW/Hk7o3STgKyuOrfffnvJ97l169Yuef7JC1Wn621oi9NPP73YUSwrPNvcnP1vRPvj5ptvztz+kpe8JDqqiROz/x0sXVuXS1b3tcT3NQAAOgIhMQAAAOjg7ly5Me5ctSm37q0HjYuhvbpXZEzA/1NbKMSnjp0Wxwzr36ppuXrOirh54WpTCABdyO9+97u47LLLchf1pU5CRxxxxAHt4+yzzy7eVb0lKaC2efPmA3puKmP79u25iw6PPPJIL0cVqqmpiTPOOKPF7Q0NDXHFFVeUfBH/VVddlVlTju5lXVVWeCCFaNLrUUrf+c53MgOAUKnje82aNSUNLSTf/va3S/p8nVm69jvllFNa3P7QQw/FAw88UNJ9XnvttbF3797oavK6ua5cubJiY6HznmuPP/74FrcvXrw4/vjHP5ZkX6nT1i9+8YsWtw8YMKDY2ayjOvzwwzO3lyMg+/T3taxub60ZGwAAVIKQGAAAAHRguxsbi13E8kzt3zteOWVkRcYE/KvutTXx2efMjFmD+rRqej730KL4y8qNphIAOrm0aPp73/tevOtd74r6+vrM2lGjRsVHPvKRA97XkCFD4swzz2xx+7Zt2+LDH/6wUEErOi+luWoP3/jGN3IXXWctHKVjS0HOLDfccEPMnj27ZPv7yle+Ehs3tvydY+bMmTFr1qyS7a+ry+swc9ddd5VsX6mDz0033VSy54O2HN+pe9Kjjz5askn80Y9+VNYOMJ3Ry172sszt//3f/12y679Vq1bFd7/73eiK0rV2//4t3yBqwYIFFR0PndPrXve6zO1f/vKXo7Gxsc37+epXv5r5/fTVr3515g1I2ttxxx2Xuf3OO+8shmRL7eqrr8688UrqvHn00UeXfL8AALC/hMQAAACgA7t2zspYuXNPbt0HjpwUdTWFiowJ2Lc+3Wrji8fPijF9euROUVqa89H75sXjG9tnATIAUH4LFy6M17/+9fHZz342d2FuWkz2pS99KQYNGtSmfV566aWZi/n+9Kc/xec+97molFIsYKy0tDj9hS98YbED065duyq234cffjiuueaazJpDDz00Jk2aVLExUVqHHHJI5oLW9H752Mc+VpLuLKlrzPXXX59Z85a3vKXN++H/6d27d4wbNy7z3FIK69evj3//93839VTUhAkTomfPnmU/vlPIJl03sX+e+9znxrRp01rc/sQTT+R+JrRWCpxV8vqoo0nXYi3529/+VtGx0Dk973nPi+nTp7e4fe7cucUO0W3x97//PX76059mfjd9wxveEG118803x4wZM1p8vOAFLzjg5043zkjdzrJ88pOfjJ07d0appJs5pBvAZEnX+nnjAgCAShASAwAAgA5q2fZdcd2cFbl1Z04YFkcMbfkupkDlDO7ZLb58wqwY0L0ut3ZPY1O87+7Zxfc6ANB5zJkzJz74wQ/GS17ykrj//vtz62tqauLTn/50HHnkkW3e99ixY+NNb3pTZk1aVPhf//VfJQmitGT79u3FBXRnnXVWVKN0d/jLL788Tj755Ljiiiti06ZNZd1fWqj55je/OTdUl9dZgI7vne98Z+b21I0ndRRsS8eXFFBN+8k6nqZMmRKnn376Ae+D/e/0lz4Pbr311jZNXeoMd9FFF8W6deu8BFRUCqAfc8wxLW6/7bbb2hyQWbp0afH47soBpLZ461vfmrn9M5/5TKuuS7N8/etfj9/97nfRlT3rWc9qcdsf//jHWL16dUXHQ+eUulDndYu97777Dui516xZE+973/syrzVf+9rXxvDhw6MjS0G2c845JzfUlX7XhoaGNu8vzdvb3/723NBZmjsAAOgIhMQAAACgA2pubo4vPLw49jY1Z9b17VYblxwyvmLjAvKN79crvnD8zOjRiu5+m/c2xHvueio27ak3tQBQxdfuTz75ZHz3u9+NV7ziFfGyl72suFi6NV20UkAsLdpNP1Mql1xySRx88MGZNdddd12cf/75xTBJKc2fPz/+53/+p3gH/NQJZNWqVVHNUjgsLcJMv8+HPvShYpgrvd6lkoJ6V155ZXFR/LZt2R1mp06dGi996UtLtm/aRwpZ5L2OP//5z4uLg1PYcn+lYzR1MNywYUNm3Sc+8Ymora3d7+cn20knnZS5/aMf/Wixo+OBSAHCV77ylcUgMnS04zt9Nqbrj3ScHoi//OUv8apXvUrApg3SZ0tWl6v6+vpikOyOO+7Y7+dOYZIvf/nL8bWvfS26utRtNuu6Lr0PBHlpqxe96EWZwfP0fk6BpXvvvXe/nnf58uVx4YUXZp5rBw8enHtTg44i/S6pk2uWFN5MXdFSyOtApXlOgbS877YzZ85sU3c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" ] @@ -711,23 +718,26 @@ "metadata": { "image/png": { "height": 559, - "width": 1593 + "width": 1732 } }, "output_type": "display_data" } ], "source": [ - "az.plot_dist(idata_seg, var_names=[\"tau\"])\n", - "ax = plt.gcf().axes[0]\n", - "ax.axvline(\n", - " np.sqrt(tau2_DL),\n", - " color=\"C1\",\n", - " linestyle=\"--\",\n", - " label=f\"DerSimonian–Laird τ = {np.sqrt(tau2_DL):.2f}\",\n", + "pc = az.plot_dist(\n", + " idata_seg,\n", + " var_names=[\"tau\"],\n", + " visuals={\n", + " \"title\": {\"text\": r\"Posterior of between-segment SD $\\tau$ (one study, six segments)\"}\n", + " },\n", + ")\n", + "az.add_lines(\n", + " pc,\n", + " values=np.sqrt(tau2_DL),\n", + " visuals={\"ref_line\": {\"color\": \"C1\", \"label\": f\"DerSimonian–Laird τ = {np.sqrt(tau2_DL):.2f}\"}},\n", ")\n", - "ax.set_title(r\"Posterior of between-segment SD $\\tau$ (one study, six segments)\")\n", - "ax.legend()" + "pc.get_viz(\"plot\").legend()" ] }, { @@ -735,7 +745,7 @@ "id": "2e5f189d", "metadata": {}, "source": [ - "Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor was the segment. Replace it with \"study\" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution. The substitution is mechanical; what it opens is not. Once studies replace segments, τ becomes the quantity the replication programme was designed to estimate.\n", + "Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor was the segment. Replace it with \"study\" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution. Making that substitution is mechanical. What it reveals is substantive: once studies replace segments, τ becomes the quantity the replication programme was designed to estimate.\n", "\n", "## The hierarchical re-framing\n", "\n", @@ -1151,7 +1161,7 @@ "source": [ "### The substance lives in $\\tau$\n", "\n", - "The hierarchical model returns two population-level quantities, and the conventional reporting habit of leading with $\\mu$ obscures the more important one. The posterior of $\\tau$ — the between-market standard deviation of true effects — is what tells the team how transportable any single result is to a new context. A small $\\tau$ means the markets are nearly exchangeable, and the experiment generalises cleanly; a large $\\tau$ means the markets are heterogeneous, and the next market is a meaningfully new experiment. Reporting only $\\mu$ collapses this into a point and hides the variability that the next stakeholder will live with." + "The hierarchical model returns two population-level quantities, and the conventional reporting habit of leading with $\\mu$ obscures the more important one. The posterior of $\\tau$, the between-market standard deviation of true effects, is what tells the team how transportable any single result is to a new context. A small $\\tau$ means the markets are nearly exchangeable, and the experiment generalises cleanly; a large $\\tau$ means the markets are heterogeneous, and the next market is a meaningfully new experiment. Reporting only $\\mu$ collapses this into a point and hides the variability that the next stakeholder will live with." ] }, { @@ -1341,7 +1351,7 @@ "\n", "## Predicting the next experiment\n", "\n", - "The stakeholder question that occasioned this entire exercise is rarely \"what was the population mean?\" — it is \"what will happen if we run this in Market I next quarter?\". The hierarchical model answers in two posterior-predictive flavours, and the difference between them matters.\n", + "The stakeholder question that occasioned this entire exercise is about a specific market: what will happen if we run this in Market I next quarter? The population mean is rarely what they are asking for. The hierarchical model answers in two posterior-predictive flavours, and the difference between them matters.\n", "\n", "**The true-effect predictive** asks for the unknown $\\theta_{\\text{new}}$ of a new market drawn from the same population:\n", "\n", @@ -1369,15 +1379,25 @@ "outputs": [ { "data": { - "image/png": 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" + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": { "image/png": { - "height": 923, - "width": 4023 + "height": 559, + "width": 1439 } }, "output_type": "display_data" @@ -1391,34 +1411,31 @@ "s_new = np.median(se_obs)\n", "d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples))\n", "\n", - "fig, ax = plt.subplots(figsize=(20, 4.5))\n", - "bins = np.linspace(\n", - " min(d_hat_new_samples.min(), theta_new_samples.min()) - 0.2,\n", - " max(d_hat_new_samples.max(), theta_new_samples.max()) + 0.2,\n", - " 80,\n", - ")\n", - "ax.hist(\n", - " theta_new_samples,\n", - " bins=bins,\n", - " alpha=0.55,\n", - " label=r\"True effect $\\theta_{\\text{new}}$\",\n", - " color=\"C0\",\n", - " density=True,\n", + "dt = az.from_dict(\n", + " {\n", + " \"posterior\": {\n", + " \"theta_new\": theta_new_samples,\n", + " \"d_hat_new\": d_hat_new_samples,\n", + " }\n", + " },\n", + " sample_dims=[\"sample\"],\n", ")\n", - "ax.hist(\n", - " d_hat_new_samples,\n", - " bins=bins,\n", - " alpha=0.45,\n", - " label=r\"Observed estimate $\\hat d_{\\text{new}}$\",\n", - " color=\"C3\",\n", - " density=True,\n", + "\n", + "pc = az.plot_dist(\n", + " dt,\n", + " kind=\"hist\",\n", + " sample_dims=[\"sample\"],\n", + " cols=[],\n", + " aes={\"color\": [\"__variable__\"]},\n", + " visuals={\n", + " \"point_estimate\": False,\n", + " \"point_estimate_text\": False,\n", + " \"credible_interval\": False,\n", + " \"title\": {\"text\": \"Two predictive distributions for the ninth market\"},\n", + " },\n", ")\n", - "ax.axvline(0.0, color=\"black\", linestyle=\":\")\n", - "ax.axvline(mu_samples.mean(), color=\"C0\", linestyle=\"--\", alpha=0.8, label=r\"$\\mu_{\\text{post}}$\")\n", - "ax.set_xlabel(\"Predicted effect in a new market\")\n", - "ax.set_ylabel(\"Posterior predictive density\")\n", - "ax.set_title(\"Two predictive distributions for the ninth market\")\n", - "ax.legend();" + "pc.add_legend(\"__variable__\")\n", + "az.add_lines(pc, values=0.0, visuals={\"ref_line\": {\"color\": \"black\", \"label\": \"0\"}})" ] }, { @@ -1496,15 +1513,409 @@ "id": "cb419ca8", "metadata": {}, "source": [ - "The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\\theta_{\\text{new}}$ as the planning input for the next market — which is what {ref}`assurance_planning` will then consume as its prior — is the way to feed accumulating evidence forward without losing track of the noise.\n", + "The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\\theta_{\\text{new}}$ as the planning input for the next market, which {ref}`assurance_planning` then consumes as its prior, is the way to feed accumulating evidence forward without losing track of the noise." + ] + }, + { + "cell_type": "markdown", + "id": "d4c871ab", + "metadata": {}, + "source": [ + "## How much to borrow\n", + "\n", + "Borrowing across markets raises a question the hierarchical model answers quietly: how far should one market's estimate move toward the others. The shrinkage already shown is that answer in action. Each market's pull toward the population mean is a weight set by its own precision against the between-market variance $\\tau$ the data infer." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4c87404e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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marketseweight_on_population_mean
0Market A0.3850.628
1Market B0.3710.610
2Market C0.3300.553
3Market D0.3000.505
4Market E0.2930.495
5Market F0.2440.403
6Market G0.2150.345
7Market H0.1670.241
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" + ], + "text/plain": [ + " market se weight_on_population_mean\n", + "0 Market A 0.385 0.628\n", + "1 Market B 0.371 0.610\n", + "2 Market C 0.330 0.553\n", + "3 Market D 0.300 0.505\n", + "4 Market E 0.293 0.495\n", + "5 Market F 0.244 0.403\n", + "6 Market G 0.215 0.345\n", + "7 Market H 0.167 0.241" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tau_post_mean = float(idata_partial.posterior[\"tau\"].mean())\n", + "shrinkage_weight = se_obs**2 / (se_obs**2 + tau_post_mean**2)\n", + "borrow_df = pd.DataFrame(\n", + " {\n", + " \"market\": MARKET_NAMES,\n", + " \"se\": se_obs,\n", + " \"weight_on_population_mean\": shrinkage_weight,\n", + " }\n", + ").round(3)\n", + "borrow_df" + ] + }, + { + "cell_type": "markdown", + "id": "5511e1fd", + "metadata": {}, + "source": [ + "Noisy markets carry the largest weight on the population mean, so they borrow the most; precise markets keep their own estimate. The between-market variance sets the scale, and the data set $\\tau$, so the borrowing rate adapts to the evidence. This is the dynamic borrowing the FDA guidance describes, where the amount borrowed responds to the similarity among the sources {cite:p}`fda2026bayesian`.\n", + "\n", + "Borrowing earns its keep through precision: a prior built from real past experiments sharpens the next posterior and lowers the sample size needed to reach a given assurance, so the eight markets already run are information the ninth should use. The open question is how much of it to grant, and a *power prior* makes that dial explicit {cite:p}`ibrahim2000power`. Raise the pooled likelihood from the past markets to a discount $\\alpha$ in the unit interval and use the result as the prior for the new market. With $n_0$ past subjects it contributes about $\\alpha\\, n_0$ observations' worth of information, so $\\alpha$ is the fraction of the historical sample size carried forward, the prior effective sample size {ref}`assurance_planning` planned around. At $\\alpha = 1$ the past markets count in full, as if pooled; as $\\alpha$ falls toward zero the prior widens and the borrowing fades. Fixing $\\alpha$ in advance is what makes the power prior the static counterpart to the hierarchical model's data-driven $\\tau$.\n", "\n", + "The word *power* here means raising the likelihood to an exponent, a separate idea from the statistical power and assurance of {ref}`assurance_planning`. The two meet only through effective sample size: $\\alpha$ sets how many past observations the prior is worth, and that count is the quantity the planning notebook traded against sample size." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5915e805", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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alphaprior_meanprior_sd
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" + ], + "text/plain": [ + " alpha prior_mean prior_sd\n", + "0 0.10 0.461 0.288\n", + "1 0.25 0.461 0.182\n", + "2 0.50 0.461 0.129\n", + "3 0.75 0.461 0.105\n", + "4 1.00 0.461 0.091" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w_pool = 1.0 / se_obs**2\n", + "pooled_mean = float(np.sum(w_pool * d_hat_obs) / np.sum(w_pool))\n", + "pooled_var = float(1.0 / np.sum(w_pool))\n", + "\n", + "alpha_grid = np.array([0.1, 0.25, 0.5, 0.75, 1.0])\n", + "power_prior_df = pd.DataFrame(\n", + " {\"alpha\": alpha_grid, \"prior_mean\": pooled_mean, \"prior_sd\": np.sqrt(pooled_var / alpha_grid)}\n", + ").round(3)\n", + "power_prior_df" + ] + }, + { + "cell_type": "markdown", + "id": "ef5a7989", + "metadata": {}, + "source": [ + "The power prior borrows the precision of the pooled mean and treats every market as one draw from a single shared effect. A genuinely new market also carries between-market variation, which the hierarchical predictive folds in through $\\tau$. The figure sets the two side by side." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "de589904", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 923, + "width": 4023 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "theta_new_mean = float(theta_new_samples.mean())\n", + "theta_new_sd = float(theta_new_samples.std())\n", + "alpha_match = (\n", + " pooled_var / theta_new_sd**2\n", + ") # discount at which the power prior reaches the tau-driven width\n", + "xx = np.linspace(pooled_mean - 1.2, pooled_mean + 1.2, 400)\n", + "\n", + "fig, ax = plt.subplots(figsize=(20, 4.5))\n", + "for a in [1.0, 0.5, 0.1]:\n", + " sd_a = np.sqrt(pooled_var / a)\n", + " ax.plot(xx, stats.norm.pdf(xx, pooled_mean, sd_a), label=rf\"Power prior, $\\alpha$ = {a}\")\n", + "ax.plot(\n", + " xx,\n", + " stats.norm.pdf(xx, pooled_mean, np.sqrt(pooled_var / alpha_match)),\n", + " color=\"C1\",\n", + " linestyle=\"--\",\n", + " label=rf\"Power prior matching $\\tau$ ($\\alpha \\approx$ {alpha_match:.2f})\",\n", + ")\n", + "ax.plot(\n", + " xx,\n", + " stats.norm.pdf(xx, theta_new_mean, theta_new_sd),\n", + " color=\"black\",\n", + " linewidth=2.5,\n", + " label=r\"Hierarchical predictive ($\\tau$-driven)\",\n", + ")\n", + "ax.axvline(0.0, color=\"grey\", linestyle=\":\", alpha=0.6)\n", + "ax.set_xlabel(r\"Effect in a new market $\\theta_{\\text{new}}$\")\n", + "ax.set_ylabel(\"Prior density\")\n", + "ax.set_title(\"Static power prior versus dynamic hierarchical borrowing\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "d91959be", + "metadata": {}, + "source": [ + "At $\\alpha = 1$ the static power prior is the tightest curve, far more confident about a new market than the spread across markets warrants. Matching the hierarchical predictive takes a large deviation from full borrowing: the dashed curve marks the small $\\alpha$ at which the static prior finally reaches the $\\tau$-driven width, and that value has to be set by hand. The hierarchical model arrives there on its own, because $\\tau$ reads the between-market spread from the data.\n", + "\n", + "Either prior becomes the belief the next market inherits, and that inheritance is legitimate only when the new market is drawn from the same population the past markets describe. Borrowing sharpens the posterior when that holds and pulls it toward the wrong centre when it fails, so the strength of borrowing has to be earned rather than assumed. A prior-data conflict check is the gate {cite:p}`evans2006checking`: locate the incoming estimate in the prior predictive distribution, and read a small tail probability as the new market disagreeing with the borrowed belief." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b0b7bf16", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " incoming estimate prior-predictive tail probability\n", + "concordant market 0.28 0.568\n", + "conflicting market -0.40 0.044" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def prior_data_conflict_tail(d_new, prior_pred_samples):\n", + " centre = np.median(prior_pred_samples)\n", + " return float(np.mean(np.abs(prior_pred_samples - centre) >= abs(d_new - centre)))\n", + "\n", + "\n", + "concordant_d_new = 0.28 # a new market in line with the population\n", + "conflicting_d_new = -0.40 # a new market that contradicts the population\n", + "pd.DataFrame(\n", + " {\n", + " \"incoming estimate\": [concordant_d_new, conflicting_d_new],\n", + " \"prior-predictive tail probability\": [\n", + " prior_data_conflict_tail(concordant_d_new, d_hat_new_samples),\n", + " prior_data_conflict_tail(conflicting_d_new, d_hat_new_samples),\n", + " ],\n", + " },\n", + " index=[\"concordant market\", \"conflicting market\"],\n", + ").round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "690ea5aa", + "metadata": {}, + "source": [ + "The concordant market sits in the body of the predictive and the tail probability is large; the prior and the new data describe one population, so the experiment can borrow at full strength and keep the precision that buys. The conflicting market sits far out and the tail probability is small, the trigger the guidance names for reassessing how much to borrow {cite:p}`fda2026bayesian`: lower $\\alpha$, widen the prior, or hold the borrowed estimate aside until the discrepancy is understood. Dynamic borrowing through $\\tau$ softens this failure on its own, since a heterogeneous set of markets produces a wide predictive that absorbs a surprising estimate, while a static power prior never adapts, so the analyst must run the check every time. Whatever borrowing strength passes the check is the prior the next experiment plans with." + ] + }, + { + "cell_type": "markdown", + "id": "e7c2c1c7", + "metadata": {}, + "source": [ "## Replication as the data-generating process\n", "\n", - "A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population is not a summary. It is the population our experiments were drawn from, made audible.\n", + "A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population describes the very population our experiments were drawn from.\n", "\n", - "Replication is often described as a verification protocol: a post-hoc check. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The triptych closes here, though \"closes\" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three problems, one machinery. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior — something to argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The problem changes; its proper characterisation is always a posterior.\n", + "Replication is often described as a verification protocol, a post-hoc check on a result already in hand. It is better understood as the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we never observed directly; pooling constructs that population from the draws; the next experiment tests whether the constructed population keeps predicting new markets. The three notebooks make the same move on three problems. Planning became a posterior over the posteriors a future experiment will compute. Interpretation became a posterior over the bias a single experiment cannot rule out. Synthesis became a posterior over the population a series of experiments samples from. The likelihood changed from Gaussian to Bernoulli each time and the picture held. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior, something to argue about rather than assume. The problem changes; its proper characterisation is always a posterior.\n", "\n", - "And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\\theta_{\\text{new}} \\sim \\mathcal{N}(\\mu, \\tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it — each notebook's synthesis becoming the next notebook's prior, each conversation overheard becoming the next conversation's opening question.\n", + "And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\\theta_{\\text{new}} \\sim \\mathcal{N}(\\mu, \\tau)$ is the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis that ends one experiment's lifecycle is the input to the design of the next. The lifecycle runs as a loop rather than a line from plan to verdict, and the posterior is what travels around it: each notebook's synthesis becomes the next notebook's prior, and each overheard conversation becomes the next one's opening question.\n", "\n", "## Authors\n", "\n", @@ -1521,7 +1932,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "id": "2e2e3295", "metadata": { "execution": { @@ -1536,7 +1947,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Tue, 02 Jun 2026\n", + "Last updated: Fri, 12 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.13.13\n", @@ -1551,7 +1962,6 @@ "pandas : 2.3.3\n", "pymc : 6.0.1\n", "scipy : 1.17.1\n", - "seaborn : 0.13.2\n", "\n", "Watermark: 2.6.0\n", "\n" diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md index cb225be96..fd3a789b2 100644 --- a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md +++ b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md @@ -20,9 +20,17 @@ kernelspec: :author: Nathaniel Forde ::: +:::{figure} experimentation_triptych.jpeg +:name: experimentation-triptych +:width: 100% +:align: center + +The experimentation lifecycle as a Bosch triptych. *Left, Bayesian Assurance:* before any data arrive, the planner reads possible effects from the prior and asks what the experiment will likely conclude. *Centre, Sensitivity Analysis:* a single experiment is wracked by the biases it cannot rule out, and the model is contorted to see which commitments its conclusion can survive. *Right, Meta-Analysis (this notebook):* many experiments are pooled through a hierarchy of levels into a synthesis that becomes the next plan's prior. Three panels, one posterior machinery. +::: + ## The replication-as-evidence problem -Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question — "did it work?" — and gets, depending on which colleague answers, two incompatible defaults: vote-counting ("four out of eight worked, so it's a wash"), or pool-everything ("the combined estimate is positive, so it works"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that estimates between-market differences rather than assuming them away. +Eight quarterly A/B tests of the same checkout-flow redesign, run across eight markets, return eight different point estimates. Three cross the conventional significance threshold; the other five do not. The product manager asks the natural question, "did it work?", and gets two incompatible defaults depending on which colleague answers: vote-counting ("four out of eight worked, so it's a wash"), or pool-everything ("the combined estimate is positive, so it works"). Both are mistakes. The vote-count discards the magnitude information in each estimate; the pool-everything pretends the markets are exchangeable in a way the evidence does not support. The honest answer requires a model that estimates between-market differences rather than assuming them away. Each experiment speaks about one market. The hierarchy is what lets us hear all of them at once. @@ -42,7 +50,6 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd import pymc as pm -import seaborn as sns from scipy import stats @@ -237,19 +244,22 @@ comparison.round(3) ``` ```{code-cell} ipython3 -az.plot_dist(idata_seg, var_names=["tau"]) -ax = plt.gcf().axes[0] -ax.axvline( - np.sqrt(tau2_DL), - color="C1", - linestyle="--", - label=f"DerSimonian–Laird τ = {np.sqrt(tau2_DL):.2f}", +pc = az.plot_dist( + idata_seg, + var_names=["tau"], + visuals={ + "title": {"text": r"Posterior of between-segment SD $\tau$ (one study, six segments)"} + }, +) +az.add_lines( + pc, + values=np.sqrt(tau2_DL), + visuals={"ref_line": {"color": "C1", "label": f"DerSimonian–Laird τ = {np.sqrt(tau2_DL):.2f}"}}, ) -ax.set_title(r"Posterior of between-segment SD $\tau$ (one study, six segments)") -ax.legend() +pc.get_viz("plot").legend() ``` -Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor was the segment. Replace it with "study" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution. The substitution is mechanical; what it opens is not. Once studies replace segments, τ becomes the quantity the replication programme was designed to estimate. +Three estimators, three commitments about how much the segments share. The fixed-effect ANOVA assumes one common effect and pools completely; the random-effects ANOVA admits between-segment variance and estimates it by moments; the hierarchical model carries that variance as a posterior. The grouping factor was the segment. Replace it with "study" and the model is untouched: meta-analysis is the random-effects analysis of variance with studies as the groups, and the index $k$ ranges over experiments rather than segments. The rest of this notebook makes exactly that substitution. Making that substitution is mechanical. What it reveals is substantive: once studies replace segments, τ becomes the quantity the replication programme was designed to estimate. ## The hierarchical re-framing @@ -431,7 +441,7 @@ ax.legend(loc="lower right"); ### The substance lives in $\tau$ -The hierarchical model returns two population-level quantities, and the conventional reporting habit of leading with $\mu$ obscures the more important one. The posterior of $\tau$ — the between-market standard deviation of true effects — is what tells the team how transportable any single result is to a new context. A small $\tau$ means the markets are nearly exchangeable, and the experiment generalises cleanly; a large $\tau$ means the markets are heterogeneous, and the next market is a meaningfully new experiment. Reporting only $\mu$ collapses this into a point and hides the variability that the next stakeholder will live with. +The hierarchical model returns two population-level quantities, and the conventional reporting habit of leading with $\mu$ obscures the more important one. The posterior of $\tau$, the between-market standard deviation of true effects, is what tells the team how transportable any single result is to a new context. A small $\tau$ means the markets are nearly exchangeable, and the experiment generalises cleanly; a large $\tau$ means the markets are heterogeneous, and the next market is a meaningfully new experiment. Reporting only $\mu$ collapses this into a point and hides the variability that the next stakeholder will live with. ```{code-cell} ipython3 az.plot_dist(idata_partial, var_names=["mu", "tau"]) @@ -517,7 +527,7 @@ The Bernoulli picture is the Gaussian picture on a different link. The populatio ## Predicting the next experiment -The stakeholder question that occasioned this entire exercise is rarely "what was the population mean?" — it is "what will happen if we run this in Market I next quarter?". The hierarchical model answers in two posterior-predictive flavours, and the difference between them matters. +The stakeholder question that occasioned this entire exercise is about a specific market: what will happen if we run this in Market I next quarter? The population mean is rarely what they are asking for. The hierarchical model answers in two posterior-predictive flavours, and the difference between them matters. **The true-effect predictive** asks for the unknown $\theta_{\text{new}}$ of a new market drawn from the same population: @@ -537,34 +547,31 @@ theta_new_samples = rng_pp.normal(mu_samples, tau_samples) s_new = np.median(se_obs) d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples)) -fig, ax = plt.subplots(figsize=(20, 4.5)) -bins = np.linspace( - min(d_hat_new_samples.min(), theta_new_samples.min()) - 0.2, - max(d_hat_new_samples.max(), theta_new_samples.max()) + 0.2, - 80, -) -ax.hist( - theta_new_samples, - bins=bins, - alpha=0.55, - label=r"True effect $\theta_{\text{new}}$", - color="C0", - density=True, +dt = az.from_dict( + { + "posterior": { + "theta_new": theta_new_samples, + "d_hat_new": d_hat_new_samples, + } + }, + sample_dims=["sample"], ) -ax.hist( - d_hat_new_samples, - bins=bins, - alpha=0.45, - label=r"Observed estimate $\hat d_{\text{new}}$", - color="C3", - density=True, + +pc = az.plot_dist( + dt, + kind="hist", + sample_dims=["sample"], + cols=[], + aes={"color": ["__variable__"]}, + visuals={ + "point_estimate": False, + "point_estimate_text": False, + "credible_interval": False, + "title": {"text": "Two predictive distributions for the ninth market"}, + }, ) -ax.axvline(0.0, color="black", linestyle=":") -ax.axvline(mu_samples.mean(), color="C0", linestyle="--", alpha=0.8, label=r"$\mu_{\text{post}}$") -ax.set_xlabel("Predicted effect in a new market") -ax.set_ylabel("Posterior predictive density") -ax.set_title("Two predictive distributions for the ninth market") -ax.legend(); +pc.add_legend("__variable__") +az.add_lines(pc, values=0.0, visuals={"ref_line": {"color": "black", "label": "0"}}) ``` ```{code-cell} ipython3 @@ -576,15 +583,115 @@ pd.DataFrame( ).round(3) ``` -The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\theta_{\text{new}}$ as the planning input for the next market — which is what {ref}`assurance_planning` will then consume as its prior — is the way to feed accumulating evidence forward without losing track of the noise. +The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\theta_{\text{new}}$ as the planning input for the next market, which {ref}`assurance_planning` then consumes as its prior, is the way to feed accumulating evidence forward without losing track of the noise. + ++++ + +## How much to borrow + +Borrowing across markets raises a question the hierarchical model answers quietly: how far should one market's estimate move toward the others. The shrinkage already shown is that answer in action. Each market's pull toward the population mean is a weight set by its own precision against the between-market variance $\tau$ the data infer. + +```{code-cell} ipython3 +tau_post_mean = float(idata_partial.posterior["tau"].mean()) +shrinkage_weight = se_obs**2 / (se_obs**2 + tau_post_mean**2) +borrow_df = pd.DataFrame( + { + "market": MARKET_NAMES, + "se": se_obs, + "weight_on_population_mean": shrinkage_weight, + } +).round(3) +borrow_df +``` + +Noisy markets carry the largest weight on the population mean, so they borrow the most; precise markets keep their own estimate. The between-market variance sets the scale, and the data set $\tau$, so the borrowing rate adapts to the evidence. This is the dynamic borrowing the FDA guidance describes, where the amount borrowed responds to the similarity among the sources {cite:p}`fda2026bayesian`. + +Borrowing earns its keep through precision: a prior built from real past experiments sharpens the next posterior and lowers the sample size needed to reach a given assurance, so the eight markets already run are information the ninth should use. The open question is how much of it to grant, and a *power prior* makes that dial explicit {cite:p}`ibrahim2000power`. Raise the pooled likelihood from the past markets to a discount $\alpha$ in the unit interval and use the result as the prior for the new market. With $n_0$ past subjects it contributes about $\alpha\, n_0$ observations' worth of information, so $\alpha$ is the fraction of the historical sample size carried forward, the prior effective sample size {ref}`assurance_planning` planned around. At $\alpha = 1$ the past markets count in full, as if pooled; as $\alpha$ falls toward zero the prior widens and the borrowing fades. Fixing $\alpha$ in advance is what makes the power prior the static counterpart to the hierarchical model's data-driven $\tau$. + +The word *power* here means raising the likelihood to an exponent, a separate idea from the statistical power and assurance of {ref}`assurance_planning`. The two meet only through effective sample size: $\alpha$ sets how many past observations the prior is worth, and that count is the quantity the planning notebook traded against sample size. + +```{code-cell} ipython3 +w_pool = 1.0 / se_obs**2 +pooled_mean = float(np.sum(w_pool * d_hat_obs) / np.sum(w_pool)) +pooled_var = float(1.0 / np.sum(w_pool)) + +alpha_grid = np.array([0.1, 0.25, 0.5, 0.75, 1.0]) +power_prior_df = pd.DataFrame( + {"alpha": alpha_grid, "prior_mean": pooled_mean, "prior_sd": np.sqrt(pooled_var / alpha_grid)} +).round(3) +power_prior_df +``` + +The power prior borrows the precision of the pooled mean and treats every market as one draw from a single shared effect. A genuinely new market also carries between-market variation, which the hierarchical predictive folds in through $\tau$. The figure sets the two side by side. + +```{code-cell} ipython3 +theta_new_mean = float(theta_new_samples.mean()) +theta_new_sd = float(theta_new_samples.std()) +alpha_match = ( + pooled_var / theta_new_sd**2 +) # discount at which the power prior reaches the tau-driven width +xx = np.linspace(pooled_mean - 1.2, pooled_mean + 1.2, 400) + +fig, ax = plt.subplots(figsize=(20, 4.5)) +for a in [1.0, 0.5, 0.1]: + sd_a = np.sqrt(pooled_var / a) + ax.plot(xx, stats.norm.pdf(xx, pooled_mean, sd_a), label=rf"Power prior, $\alpha$ = {a}") +ax.plot( + xx, + stats.norm.pdf(xx, pooled_mean, np.sqrt(pooled_var / alpha_match)), + color="C1", + linestyle="--", + label=rf"Power prior matching $\tau$ ($\alpha \approx$ {alpha_match:.2f})", +) +ax.plot( + xx, + stats.norm.pdf(xx, theta_new_mean, theta_new_sd), + color="black", + linewidth=2.5, + label=r"Hierarchical predictive ($\tau$-driven)", +) +ax.axvline(0.0, color="grey", linestyle=":", alpha=0.6) +ax.set_xlabel(r"Effect in a new market $\theta_{\text{new}}$") +ax.set_ylabel("Prior density") +ax.set_title("Static power prior versus dynamic hierarchical borrowing") +ax.legend(); +``` + +At $\alpha = 1$ the static power prior is the tightest curve, far more confident about a new market than the spread across markets warrants. Matching the hierarchical predictive takes a large deviation from full borrowing: the dashed curve marks the small $\alpha$ at which the static prior finally reaches the $\tau$-driven width, and that value has to be set by hand. The hierarchical model arrives there on its own, because $\tau$ reads the between-market spread from the data. + +Either prior becomes the belief the next market inherits, and that inheritance is legitimate only when the new market is drawn from the same population the past markets describe. Borrowing sharpens the posterior when that holds and pulls it toward the wrong centre when it fails, so the strength of borrowing has to be earned rather than assumed. A prior-data conflict check is the gate {cite:p}`evans2006checking`: locate the incoming estimate in the prior predictive distribution, and read a small tail probability as the new market disagreeing with the borrowed belief. + +```{code-cell} ipython3 +def prior_data_conflict_tail(d_new, prior_pred_samples): + centre = np.median(prior_pred_samples) + return float(np.mean(np.abs(prior_pred_samples - centre) >= abs(d_new - centre))) + + +concordant_d_new = 0.28 # a new market in line with the population +conflicting_d_new = -0.40 # a new market that contradicts the population +pd.DataFrame( + { + "incoming estimate": [concordant_d_new, conflicting_d_new], + "prior-predictive tail probability": [ + prior_data_conflict_tail(concordant_d_new, d_hat_new_samples), + prior_data_conflict_tail(conflicting_d_new, d_hat_new_samples), + ], + }, + index=["concordant market", "conflicting market"], +).round(3) +``` + +The concordant market sits in the body of the predictive and the tail probability is large; the prior and the new data describe one population, so the experiment can borrow at full strength and keep the precision that buys. The conflicting market sits far out and the tail probability is small, the trigger the guidance names for reassessing how much to borrow {cite:p}`fda2026bayesian`: lower $\alpha$, widen the prior, or hold the borrowed estimate aside until the discrepancy is understood. Dynamic borrowing through $\tau$ softens this failure on its own, since a heterogeneous set of markets produces a wide predictive that absorbs a surprising estimate, while a static power prior never adapts, so the analyst must run the check every time. Whatever borrowing strength passes the check is the prior the next experiment plans with. + ++++ ## Replication as the data-generating process -A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population is not a summary. It is the population our experiments were drawn from, made audible. +A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population describes the very population our experiments were drawn from. -Replication is often described as a verification protocol: a post-hoc check. Replication is the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we did not have direct access to; pooling is the operation that constructs the population from the draws; the next experiment is the test that the population so constructed will continue to predict. The triptych closes here, though "closes" is the wrong shape for it. The first notebook turned planning into a posterior over future posteriors; the second turned interpretation into a posterior over the bias an experiment cannot rule out; the third turned a series of experiments into a posterior over the population they sample from. Three problems, one machinery. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior — something to argue about rather than assume. The likelihood changed from Gaussian to Bernoulli each time and the picture did not. The problem changes; its proper characterisation is always a posterior. +Replication is often described as a verification protocol, a post-hoc check on a result already in hand. It is better understood as the data-generating process whose distribution we are trying to learn. Each new market is a draw from a population we never observed directly; pooling constructs that population from the draws; the next experiment tests whether the constructed population keeps predicting new markets. The three notebooks make the same move on three problems. Planning became a posterior over the posteriors a future experiment will compute. Interpretation became a posterior over the bias a single experiment cannot rule out. Synthesis became a posterior over the population a series of experiments samples from. The likelihood changed from Gaussian to Bernoulli each time and the picture held. In each, an assumption a conventional analysis leaves implicit is made a parameter with a posterior, something to argue about rather than assume. The problem changes; its proper characterisation is always a posterior. -And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\theta_{\text{new}} \sim \mathcal{N}(\mu, \tau)$ is precisely the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis at the end of one experiment's lifecycle is the input to the design of the next. The lifecycle is not a line from plan to verdict but a loop, and the posterior is what travels around it — each notebook's synthesis becoming the next notebook's prior, each conversation overheard becoming the next conversation's opening question. +And the last question feeds the first. The population this notebook inferred is the prior the planning notebook consumes: $\theta_{\text{new}} \sim \mathcal{N}(\mu, \tau)$ is the kind of belief {ref}`assurance_planning` integrates over before the next experiment is run. The synthesis that ends one experiment's lifecycle is the input to the design of the next. The lifecycle runs as a loop rather than a line from plan to verdict, and the posterior is what travels around it: each notebook's synthesis becomes the next notebook's prior, and each overheard conversation becomes the next one's opening question. ## Authors diff --git a/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb b/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb index b6bbc4d86..a5dd910c3 100644 --- a/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb +++ b/examples/causal_inference/sensitivity_unmeasured_confounding.ipynb @@ -14,18 +14,26 @@ ":author: Nathaniel Forde\n", ":::\n", "\n", - "All applied inference is argument. Against every experiment you can set the contention that the working conditions were imperfect. Some aspect of the evaluation was flawed. Maybe treatment assignment introduced a subtle kind of bias, or the subjects didn't comply fully with the design. Against every experiment you can contrast the scientific ideal of perfect randomisation and clear adherence. This contrast is just due diligence and sensitivity analysis varies that contrast against the ideal limit case. \n", + ":::{figure} experimentation_triptych.jpeg\n", + ":name: experimentation-triptych\n", + ":width: 100%\n", + ":align: center\n", + "\n", + "The experimentation lifecycle as a Bosch triptych. *Left, Bayesian Assurance:* before any data arrive, the planner reads possible effects from the prior and asks what the experiment will likely conclude. *Centre, Sensitivity Analysis (this notebook):* a single experiment is wracked by the biases it cannot rule out, and the model is contorted to see which commitments its conclusion can survive. *Right, Meta-Analysis:* many experiments are pooled through a hierarchy of levels into a synthesis that becomes the next plan's prior. Three panels, one posterior machinery.\n", + ":::\n", + "\n", + "All applied inference is argument. Against every experiment you can set the contention that the working conditions were imperfect. Some aspect of the evaluation was flawed. Maybe treatment assignment introduced a subtle kind of bias, or the subjects didn't comply fully with the design. Against every experiment you can contrast the scientific ideal of perfect randomisation and clear adherence. Holding an experiment against that ideal is due diligence. Sensitivity analysis does it systematically, by varying how far the working conditions fall short of perfect randomisation.\n", "\n", "## The randomisation gap\n", "\n", "Randomisation is an assumption you maintain by design, not a fact you assert by intention. The checkout experiment ran, the headline effect on revenue-per-visitor looks positive, and somewhere in the diagnostic notes there is a line about compliance being 73%: users on slow connections were silently routed back to the old flow by a CDN edge case, and the routing was not random in the way it touched the population. The intent-to-treat estimate now depends on a counterfactual the experiment did not produce: what those users would have done under the new flow. Without further assumptions, the data cannot say what the treatment effect would have been if randomisation had held.\n", "\n", - "This notebook develops the Bayesian response to this situation. Where Rosenbaum's $\\Gamma$ {cite:p}`rosenbaum2002observational` and the E-value {cite:p}`vanderweele2017sensitivity` give point summaries of how strong an unmeasured confounder would need to be to nullify the result, the Bayesian framing makes the same unmeasured confounder a *parameter* in the model, with a prior over its plausible strength and a posterior shaped by both the data and the analyst's commitments {cite:p}`imbens2003sensitivity`, {cite:p}`cinellihazlett2020omitted`. We develop the machinery on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the second of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart.\n", + "This notebook develops the Bayesian response to this situation. Where Rosenbaum's $\\Gamma$ {cite:p}`rosenbaum2002observational` and the E-value {cite:p}`vanderweele2017sensitivity` give point summaries of how strong an unmeasured confounder would need to be to nullify the result, the Bayesian framing makes the same unmeasured confounder a *parameter* in the model, with a prior over its plausible strength and a posterior shaped by both the data and the analyst's commitments {cite:p}`imbens2003sensitivity`, {cite:p}`cinellihazlett2020omitted`. We develop the machinery on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the second of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The clean primary analysis of a well-run experiment, the posterior on the effect with the decision rule applied, appears as the demonstration step in {ref}`assurance_planning`; this notebook takes up the interpretation once that clean identification is itself in question.\n", "\n", ":::{admonition} Where this lands in regulatory practice\n", ":class: note\n", "\n", - "The sensitivity construction here is not a workaround; it is the regulatory recommendation. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials describes sensitivity analysis as varying the prior over a critical assumption, and notes that \"some approaches can build uncertainty about specific assumptions into the prior itself\" {cite:p}`fda2026bayesian`, which is the move made below. The guidance goes further and sanctions modelling a discrepancy between data sources with \"an assumed bias parameter in the model\", the exact object this notebook places a prior on and sweeps. The clinical-trial setting differs from a product quasi-experiment; the bias parameter is the same.\n", + "The sensitivity construction here follows the regulatory recommendation directly. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials describes sensitivity analysis as varying the prior over a critical assumption, and notes that \"some approaches can build uncertainty about specific assumptions into the prior itself\" {cite:p}`fda2026bayesian`, which is the move made below. The guidance goes further and sanctions modelling a discrepancy between data sources with \"an assumed bias parameter in the model\", the exact object this notebook places a prior on and sweeps. The clinical-trial setting differs from a product quasi-experiment; the bias parameter is the same.\n", ":::" ] }, @@ -45,15 +53,11 @@ "source": [ "import warnings\n", "\n", - "from dataclasses import dataclass\n", - "\n", "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", - "import pytensor.tensor as pt\n", - "import seaborn as sns\n", "\n", "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n", "warnings.filterwarnings(\"ignore\", category=UserWarning)" @@ -158,7 +162,7 @@ "id": "e8f31fe6", "metadata": {}, "source": [ - "What we want is $\\tau$, the causal effect of $T$ on $Y$. What the data identify is $\\tau$ *plus* the contribution of any path that flows through $U$ — specifically the product of the two red edges, which we collect into a single bias term $\\beta = \\lambda_T \\cdot \\lambda_Y / \\mathrm{var}(T)$ at the scale of the observed difference in means {cite:p}`hernan2020whatif`, {cite:p}`cunningham2021causal`. The naïve estimator returns $\\tau + \\beta$. The data are silent on the decomposition; the prior is what sets it.\n", + "What we want is $\\tau$, the causal effect of $T$ on $Y$. What the data identify is $\\tau$ *plus* the contribution of any path that flows through $U$: the product of the two red edges, collected into a single bias term $\\beta = \\lambda_T \\cdot \\lambda_Y / \\mathrm{var}(T)$ at the scale of the observed difference in means {cite:p}`hernan2020whatif`, {cite:p}`cunningham2021causal`. The naïve estimator returns $\\tau + \\beta$. The data are silent on the decomposition; the prior is what sets it.\n", "\n", "## The bias parameter\n", "\n", @@ -219,7 +223,7 @@ "id": "ba7bfe6c", "metadata": {}, "source": [ - "The observed difference recovers the *biased* effect, as it must. $\\tau$ is what we want; $\\beta$ is what we cannot observe. Before asking how a prior reshapes that sum, confirm the machinery is faithful: hand the model the belief an analyst would hold if they knew the confounder exactly — a prior on $\\beta$ centred at the true bias — and the treatment effect should return unbiased across repeated experiments." + "The observed difference recovers the *biased* effect, as it must. $\\tau$ is what we want; $\\beta$ is what we cannot observe. Before asking how a prior reshapes that sum, confirm the machinery is faithful: hand the model the belief an analyst would hold if they knew the confounder exactly, a prior on $\\beta$ centred at the true bias, and the treatment effect should return unbiased across repeated experiments." ] }, { @@ -302,7 +306,7 @@ "id": "054df778", "metadata": {}, "source": [ - "The sampling distribution of the posterior mean sits on the true $\\tau = 0.30$: with the confounder's strength correctly specified, the estimator is unbiased across repeated experiments. The recovery is real, but read what produced it. The data fixed the sum $\\tau + \\beta$ through the observed difference; the correctly-centred prior on $\\beta$ is what split that sum into its parts. A single difference in means cannot separate a treatment effect from a confound that imitates it — the two are not identified by the data, only by the data together with a commitment about the confounder. The prior is not a nuisance to be tuned away. It is the content of the decomposition, and that is why the honest analysis does not settle on one prior but varies it. Recentre the prior on $\\beta$ at zero — where an analyst lands by default, having no oracle for the confounder's size — and the estimate moves. The moderate prior below makes the shift concrete." + "The sampling distribution of the posterior mean sits on the true $\\tau = 0.30$: with the confounder's strength correctly specified, the estimator is unbiased across repeated experiments. The recovery is real, but read what produced it. The data fixed the sum $\\tau + \\beta$ through the observed difference; the correctly-centred prior on $\\beta$ is what split that sum into its parts. A single difference in means cannot separate a treatment effect from a confound that imitates it. The data identify the two only in combination with a commitment about the confounder. The prior is the content of that decomposition, which is why an honest analysis varies it rather than fixing it. Recentre the prior on $\\beta$ at zero, where an analyst lands by default with no oracle for the confounder's size, and the estimate moves. The moderate prior below makes the shift concrete." ] }, { @@ -381,7 +385,7 @@ "\n", "### Tipping-point analysis under a Gaussian outcome\n", "\n", - "The model with a flat prior on $\\tau$ and a Gaussian prior on $\\beta$ is fully conjugate; the posterior is a closed-form bivariate Normal that we can compute analytically. This lets us trace the tipping point — the bias prior strength at which the posterior probability of a positive treatment effect drops below the team's decision threshold." + "The model with a flat prior on $\\tau$ and a Gaussian prior on $\\beta$ is fully conjugate; the posterior is a closed-form bivariate Normal that we can compute analytically. This lets us trace the tipping point: the bias prior strength at which the posterior probability of a positive treatment effect drops below the team's decision threshold." ] }, { @@ -578,7 +582,7 @@ "id": "e15e75ee", "metadata": {}, "source": [ - "Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The problem has changed from what the treatment did to which prior commitments on the unmeasured bias are required to believe it did anything. Its proper characterisation has not changed: it is a posterior either way.\n", + "Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is a price tag on the claim rather than a refutation of the experiment. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The question has shifted from what the treatment did to which commitments about the unmeasured bias are needed to believe it did anything, and a posterior answers the new question as readily as the old.\n", "\n", "The same machinery runs on the log-odds scale. Before mapping the full topology of the bias-prior space, we confirm that the one-dimensional fragility picture is not an artefact of the Gaussian likelihood." ] @@ -794,6 +798,14 @@ " }\n", " )\n", "tipping_bern_df = pd.DataFrame(tipping_records_bern)\n", + "# prob_positive decreases as the bias prior loosens; interpolate to the threshold crossing\n", + "tip_bern = float(\n", + " np.interp(\n", + " decision_threshold,\n", + " tipping_bern_df[\"prob_positive\"].values[::-1],\n", + " tipping_bern_df[\"bias_sd\"].values[::-1],\n", + " )\n", + ")\n", "tipping_bern_df.round(3)" ] }, @@ -812,7 +824,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -855,11 +867,11 @@ " label=f\"Decision threshold = {decision_threshold}\",\n", ")\n", "axes[1].axvline(\n", - " 0.2,\n", + " tip_bern,\n", " color=\"C3\",\n", " linestyle=\":\",\n", " alpha=0.7,\n", - " label=f\"Tipping point ≈ {tipping_point:.2f}\",\n", + " label=f\"Tipping point ≈ {tip_bern:.2f}\",\n", ")\n", "axes[1].set_xlabel(r\"Prior SD on bias $\\beta$ (log-odds)\")\n", "axes[1].set_ylabel(r\"$P(\\tau > 0 \\mid \\text{data})$\")\n", @@ -872,7 +884,7 @@ "id": "a03b1409", "metadata": {}, "source": [ - "The shape of the curve confirms what the Gaussian case showed: the decision rule loses confidence as the prior on bias widens, and the tipping point is identifiable on the log-odds scale just as it was on the revenue scale. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve is the full posterior over that commitment. One dimension, two likelihoods, the same fragility picture. The complete audit — both dimensions of the bias prior, the full region of commitments the conclusion can survive — is the surface below." + "The shape of the curve confirms what the Gaussian case showed: the decision rule loses confidence as the prior on bias widens, and the tipping point is identifiable on the log-odds scale just as it was on the revenue scale. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve is the full posterior over that commitment. One dimension, two likelihoods, the same fragility picture. The complete audit needs both dimensions of the bias prior, the full region of commitments the conclusion can survive. That surface is below." ] }, { @@ -957,7 +969,7 @@ "id": "13d4a25c", "metadata": {}, "source": [ - "The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right. It tells both parties exactly what they are disagreeing about: which region of the bias-prior plane their prior commitments occupy, and whether that region sits inside or outside the contour of defensibility. The audit has a legible geometry." + "The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph locates the disagreement rather than settling it. It shows which region of the bias-prior plane each party's commitments occupy, and whether that region sits inside or outside the contour of defensibility. The audit has a legible geometry." ] }, { @@ -967,11 +979,11 @@ "source": [ "## Robustness as a posterior question\n", "\n", - "The randomisation gap is not closed by a sensitivity analysis. It is mapped. The analyst who runs the sweep above has not proved the experiment robust; they have produced an audit-able exhibit of which bias commitments the experiment can survive, and which it cannot. The audit is the deliverable.\n", + "A sensitivity analysis maps the randomisation gap rather than closing it. The sweep above does not prove the experiment robust; it produces an auditable exhibit of which bias commitments the experiment can survive and which it cannot. That exhibit is the deliverable.\n", "\n", - "This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. The sensitivity surface extends this from a point to a topology: the full region of bias-prior commitments the experiment can survive, with its boundary drawn as a contour. Two analysts holding the same data and disagreeing about a conclusion are not disagreeing about the data. They occupy different coordinates in the bias-prior plane, and the surface tells them precisely which commitment separates them.\n", + "This changes what honest reporting looks like. The headline from a quasi-experiment becomes the *tipping point*, the prior on the bias at which the conclusion turns, and the sensitivity surface widens that point into a region: the full set of bias-prior commitments the experiment can survive, with its boundary drawn as a contour. Two analysts who hold the same data and reach opposite conclusions are reading the same difference in means through different priors on the confounder, and the surface shows precisely which commitment divides them.\n", "\n", - "The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In each: an assumption a conventional analysis leaves implicit becomes a parameter with a posterior — something to argue about rather than assume. The problem changes; its proper characterisation is always a posterior.\n", + "What a clean experiment reports as one posterior on the effect, a quasi-experiment reports as a posterior spread over the bias the data cannot rule out. Mapping that spread, and naming the commitment a positive conclusion requires, is what the quasi-experiment owes its readers. {ref}`meta_analysis_experiments` turns from a single threatened experiment to a whole series of them.\n", "\n", "## Authors\n", "\n", @@ -1003,7 +1015,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Tue, 02 Jun 2026\n", + "Last updated: Fri, 12 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.13.13\n", @@ -1017,9 +1029,7 @@ "numpy : 2.3.5\n", "pandas : 2.3.3\n", "pymc : 6.0.1\n", - "pytensor : 3.0.3\n", "scipy : 1.17.1\n", - "seaborn : 0.13.2\n", "\n", "Watermark: 2.6.0\n", "\n" diff --git a/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md b/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md index 21c113e6b..8a4b5941b 100644 --- a/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md +++ b/examples/causal_inference/sensitivity_unmeasured_confounding.myst.md @@ -20,32 +20,36 @@ kernelspec: :author: Nathaniel Forde ::: -All applied inference is argument. Against every experiment you can set the contention that the working conditions were imperfect. Some aspect of the evaluation was flawed. Maybe treatment assignment introduced a subtle kind of bias, or the subjects didn't comply fully with the design. Against every experiment you can contrast the scientific ideal of perfect randomisation and clear adherence. This contrast is just due diligence and sensitivity analysis varies that contrast against the ideal limit case. +:::{figure} experimentation_triptych.jpeg +:name: experimentation-triptych +:width: 100% +:align: center + +The experimentation lifecycle as a Bosch triptych. *Left, Bayesian Assurance:* before any data arrive, the planner reads possible effects from the prior and asks what the experiment will likely conclude. *Centre, Sensitivity Analysis (this notebook):* a single experiment is wracked by the biases it cannot rule out, and the model is contorted to see which commitments its conclusion can survive. *Right, Meta-Analysis:* many experiments are pooled through a hierarchy of levels into a synthesis that becomes the next plan's prior. Three panels, one posterior machinery. +::: + +All applied inference is argument. Against every experiment you can set the contention that the working conditions were imperfect. Some aspect of the evaluation was flawed. Maybe treatment assignment introduced a subtle kind of bias, or the subjects didn't comply fully with the design. Against every experiment you can contrast the scientific ideal of perfect randomisation and clear adherence. Holding an experiment against that ideal is due diligence. Sensitivity analysis does it systematically, by varying how far the working conditions fall short of perfect randomisation. ## The randomisation gap Randomisation is an assumption you maintain by design, not a fact you assert by intention. The checkout experiment ran, the headline effect on revenue-per-visitor looks positive, and somewhere in the diagnostic notes there is a line about compliance being 73%: users on slow connections were silently routed back to the old flow by a CDN edge case, and the routing was not random in the way it touched the population. The intent-to-treat estimate now depends on a counterfactual the experiment did not produce: what those users would have done under the new flow. Without further assumptions, the data cannot say what the treatment effect would have been if randomisation had held. -This notebook develops the Bayesian response to this situation. Where Rosenbaum's $\Gamma$ {cite:p}`rosenbaum2002observational` and the E-value {cite:p}`vanderweele2017sensitivity` give point summaries of how strong an unmeasured confounder would need to be to nullify the result, the Bayesian framing makes the same unmeasured confounder a *parameter* in the model, with a prior over its plausible strength and a posterior shaped by both the data and the analyst's commitments {cite:p}`imbens2003sensitivity`, {cite:p}`cinellihazlett2020omitted`. We develop the machinery on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the second of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. +This notebook develops the Bayesian response to this situation. Where Rosenbaum's $\Gamma$ {cite:p}`rosenbaum2002observational` and the E-value {cite:p}`vanderweele2017sensitivity` give point summaries of how strong an unmeasured confounder would need to be to nullify the result, the Bayesian framing makes the same unmeasured confounder a *parameter* in the model, with a prior over its plausible strength and a posterior shaped by both the data and the analyst's commitments {cite:p}`imbens2003sensitivity`, {cite:p}`cinellihazlett2020omitted`. We develop the machinery on a continuous outcome (revenue per visitor) and then re-run it on a binary outcome (conversion). This is the second of three notebooks on the lifecycle of a Bayesian experiment; see {ref}`assurance_planning` for the planning counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The clean primary analysis of a well-run experiment, the posterior on the effect with the decision rule applied, appears as the demonstration step in {ref}`assurance_planning`; this notebook takes up the interpretation once that clean identification is itself in question. :::{admonition} Where this lands in regulatory practice :class: note -The sensitivity construction here is not a workaround; it is the regulatory recommendation. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials describes sensitivity analysis as varying the prior over a critical assumption, and notes that "some approaches can build uncertainty about specific assumptions into the prior itself" {cite:p}`fda2026bayesian`, which is the move made below. The guidance goes further and sanctions modelling a discrepancy between data sources with "an assumed bias parameter in the model", the exact object this notebook places a prior on and sweeps. The clinical-trial setting differs from a product quasi-experiment; the bias parameter is the same. +The sensitivity construction here follows the regulatory recommendation directly. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials describes sensitivity analysis as varying the prior over a critical assumption, and notes that "some approaches can build uncertainty about specific assumptions into the prior itself" {cite:p}`fda2026bayesian`, which is the move made below. The guidance goes further and sanctions modelling a discrepancy between data sources with "an assumed bias parameter in the model", the exact object this notebook places a prior on and sweeps. The clinical-trial setting differs from a product quasi-experiment; the bias parameter is the same. ::: ```{code-cell} ipython3 import warnings -from dataclasses import dataclass - import arviz as az import matplotlib.pyplot as plt import numpy as np import pandas as pd import pymc as pm -import pytensor.tensor as pt -import seaborn as sns warnings.filterwarnings("ignore", category=RuntimeWarning) warnings.filterwarnings("ignore", category=UserWarning) @@ -96,7 +100,7 @@ ax.text(6.8, 3.3, r"$\lambda_Y$", color="firebrick", fontsize=12) ax.set_title("The identification gap: $U$ unobserved", fontsize=12); ``` -What we want is $\tau$, the causal effect of $T$ on $Y$. What the data identify is $\tau$ *plus* the contribution of any path that flows through $U$ — specifically the product of the two red edges, which we collect into a single bias term $\beta = \lambda_T \cdot \lambda_Y / \mathrm{var}(T)$ at the scale of the observed difference in means {cite:p}`hernan2020whatif`, {cite:p}`cunningham2021causal`. The naïve estimator returns $\tau + \beta$. The data are silent on the decomposition; the prior is what sets it. +What we want is $\tau$, the causal effect of $T$ on $Y$. What the data identify is $\tau$ *plus* the contribution of any path that flows through $U$: the product of the two red edges, collected into a single bias term $\beta = \lambda_T \cdot \lambda_Y / \mathrm{var}(T)$ at the scale of the observed difference in means {cite:p}`hernan2020whatif`, {cite:p}`cunningham2021causal`. The naïve estimator returns $\tau + \beta$. The data are silent on the decomposition; the prior is what sets it. ## The bias parameter @@ -129,7 +133,7 @@ print(f"Observed difference d_hat = {d_hat:.3f} (sampling SE = {sigma_d:.3f})") print(f"True tau = {TRUE_TAU}, true bias = {TRUE_BIAS}, true sum = {TRUE_TAU + TRUE_BIAS}") ``` -The observed difference recovers the *biased* effect, as it must. $\tau$ is what we want; $\beta$ is what we cannot observe. Before asking how a prior reshapes that sum, confirm the machinery is faithful: hand the model the belief an analyst would hold if they knew the confounder exactly — a prior on $\beta$ centred at the true bias — and the treatment effect should return unbiased across repeated experiments. +The observed difference recovers the *biased* effect, as it must. $\tau$ is what we want; $\beta$ is what we cannot observe. Before asking how a prior reshapes that sum, confirm the machinery is faithful: hand the model the belief an analyst would hold if they knew the confounder exactly, a prior on $\beta$ centred at the true bias, and the treatment effect should return unbiased across repeated experiments. ```{code-cell} ipython3 def gaussian_sensitivity_posterior( @@ -177,7 +181,7 @@ ax.set_title(r"Recovery of $\tau$ when the bias prior is correctly centred") ax.legend(); ``` -The sampling distribution of the posterior mean sits on the true $\tau = 0.30$: with the confounder's strength correctly specified, the estimator is unbiased across repeated experiments. The recovery is real, but read what produced it. The data fixed the sum $\tau + \beta$ through the observed difference; the correctly-centred prior on $\beta$ is what split that sum into its parts. A single difference in means cannot separate a treatment effect from a confound that imitates it — the two are not identified by the data, only by the data together with a commitment about the confounder. The prior is not a nuisance to be tuned away. It is the content of the decomposition, and that is why the honest analysis does not settle on one prior but varies it. Recentre the prior on $\beta$ at zero — where an analyst lands by default, having no oracle for the confounder's size — and the estimate moves. The moderate prior below makes the shift concrete. +The sampling distribution of the posterior mean sits on the true $\tau = 0.30$: with the confounder's strength correctly specified, the estimator is unbiased across repeated experiments. The recovery is real, but read what produced it. The data fixed the sum $\tau + \beta$ through the observed difference; the correctly-centred prior on $\beta$ is what split that sum into its parts. A single difference in means cannot separate a treatment effect from a confound that imitates it. The data identify the two only in combination with a commitment about the confounder. The prior is the content of that decomposition, which is why an honest analysis varies it rather than fixing it. Recentre the prior on $\beta$ at zero, where an analyst lands by default with no oracle for the confounder's size, and the estimate moves. The moderate prior below makes the shift concrete. ```{code-cell} ipython3 def gaussian_sensitivity_model(d_hat, sigma_d, tau_prior=(0.0, 1.0), bias_prior=(0.0, 0.3)): @@ -214,7 +218,7 @@ Under the moderate prior, centred at zero, the posterior of $\tau$ is pulled wel ### Tipping-point analysis under a Gaussian outcome -The model with a flat prior on $\tau$ and a Gaussian prior on $\beta$ is fully conjugate; the posterior is a closed-form bivariate Normal that we can compute analytically. This lets us trace the tipping point — the bias prior strength at which the posterior probability of a positive treatment effect drops below the team's decision threshold. +The model with a flat prior on $\tau$ and a Gaussian prior on $\beta$ is fully conjugate; the posterior is a closed-form bivariate Normal that we can compute analytically. This lets us trace the tipping point: the bias prior strength at which the posterior probability of a positive treatment effect drops below the team's decision threshold. ```{code-cell} ipython3 # Verify the closed form agrees with MCMC under the moderate prior @@ -294,7 +298,7 @@ axes[1].set_title("Tipping point in the decision rule") axes[1].legend(); ``` -Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is not a refutation of the experiment — it is a price tag. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The problem has changed from what the treatment did to which prior commitments on the unmeasured bias are required to believe it did anything. Its proper characterisation has not changed: it is a posterior either way. +Read the left panel from left to right: as the analyst loosens their prior on the bias, the posterior mean of $\tau$ drifts downward and the posterior interval widens. Read the right panel: there is a bias prior strength at which the posterior probability of a positive effect falls below the conventional decision threshold. The tipping point is a price tag on the claim rather than a refutation of the experiment. To assert a positive effect requires a stated belief that the bias prior is *tighter* than the tipping point. The honest version of the conversation with stakeholders runs through this number. The posterior is what makes that conversation possible: the same inference machinery that reads the experiment's results in a clean world is what traces the contour of its fragility here. The question has shifted from what the treatment did to which commitments about the unmeasured bias are needed to believe it did anything, and a posterior answers the new question as readily as the old. The same machinery runs on the log-odds scale. Before mapping the full topology of the bias-prior space, we confirm that the one-dimensional fragility picture is not an artefact of the Gaussian likelihood. @@ -369,6 +373,14 @@ for bias_sd in [0.05, 0.15, 0.2, 0.30, 0.50, 0.80]: } ) tipping_bern_df = pd.DataFrame(tipping_records_bern) +# prob_positive decreases as the bias prior loosens; interpolate to the threshold crossing +tip_bern = float( + np.interp( + decision_threshold, + tipping_bern_df["prob_positive"].values[::-1], + tipping_bern_df["bias_sd"].values[::-1], + ) +) tipping_bern_df.round(3) ``` @@ -401,11 +413,11 @@ axes[1].axhline( label=f"Decision threshold = {decision_threshold}", ) axes[1].axvline( - 0.2, + tip_bern, color="C3", linestyle=":", alpha=0.7, - label=f"Tipping point ≈ {tipping_point:.2f}", + label=f"Tipping point ≈ {tip_bern:.2f}", ) axes[1].set_xlabel(r"Prior SD on bias $\beta$ (log-odds)") axes[1].set_ylabel(r"$P(\tau > 0 \mid \text{data})$") @@ -413,7 +425,7 @@ axes[1].set_title("Tipping point on the log-odds scale") axes[1].legend(); ``` -The shape of the curve confirms what the Gaussian case showed: the decision rule loses confidence as the prior on bias widens, and the tipping point is identifiable on the log-odds scale just as it was on the revenue scale. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve is the full posterior over that commitment. One dimension, two likelihoods, the same fragility picture. The complete audit — both dimensions of the bias prior, the full region of commitments the conclusion can survive — is the surface below. +The shape of the curve confirms what the Gaussian case showed: the decision rule loses confidence as the prior on bias widens, and the tipping point is identifiable on the log-odds scale just as it was on the revenue scale. The E-value of {cite:p}`vanderweele2017sensitivity` corresponds, roughly, to the bias magnitude at which a frequentist decision would tip; the Bayesian curve is the full posterior over that commitment. One dimension, two likelihoods, the same fragility picture. The complete audit needs both dimensions of the bias prior, the full region of commitments the conclusion can survive. That surface is below. +++ @@ -459,17 +471,17 @@ ax.set_title(r"$P(\tau > 0)$ under bias-prior commitments") plt.colorbar(im, ax=ax, label=r"$P(\tau > 0 \mid d_{\text{obs}})$"); ``` -The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph does not tell anyone who is right. It tells both parties exactly what they are disagreeing about: which region of the bias-prior plane their prior commitments occupy, and whether that region sits inside or outside the contour of defensibility. The audit has a legible geometry. +The decision threshold contour partitions the bias-prior plane into a region where the experiment is defensible and a region where it is not. The analyst who wants to claim a positive effect commits to a point inside the inner contour. The dissenter who wants to claim the effect is artefactual commits to a point outside it. The graph locates the disagreement rather than settling it. It shows which region of the bias-prior plane each party's commitments occupy, and whether that region sits inside or outside the contour of defensibility. The audit has a legible geometry. +++ ## Robustness as a posterior question -The randomisation gap is not closed by a sensitivity analysis. It is mapped. The analyst who runs the sweep above has not proved the experiment robust; they have produced an audit-able exhibit of which bias commitments the experiment can survive, and which it cannot. The audit is the deliverable. +A sensitivity analysis maps the randomisation gap rather than closing it. The sweep above does not prove the experiment robust; it produces an auditable exhibit of which bias commitments the experiment can survive and which it cannot. That exhibit is the deliverable. -This shifts what counts as honest reporting. The headline number from a quasi-experiment is no longer the point estimate of the treatment effect; it is the *tipping point* — the prior on the bias at which the conclusion turns. The sensitivity surface extends this from a point to a topology: the full region of bias-prior commitments the experiment can survive, with its boundary drawn as a contour. Two analysts holding the same data and disagreeing about a conclusion are not disagreeing about the data. They occupy different coordinates in the bias-prior plane, and the surface tells them precisely which commitment separates them. +This changes what honest reporting looks like. The headline from a quasi-experiment becomes the *tipping point*, the prior on the bias at which the conclusion turns, and the sensitivity surface widens that point into a region: the full set of bias-prior commitments the experiment can survive, with its boundary drawn as a contour. Two analysts who hold the same data and reach opposite conclusions are reading the same difference in means through different priors on the confounder, and the surface shows precisely which commitment divides them. -The first notebook used the posterior to ask what an experiment will probably say. This one uses the posterior to ask what the experiment did say, once the assumption of clean identification is itself a parameter. The next will use the posterior to ask what a whole series of experiments jointly implies. In each: an assumption a conventional analysis leaves implicit becomes a parameter with a posterior — something to argue about rather than assume. The problem changes; its proper characterisation is always a posterior. +What a clean experiment reports as one posterior on the effect, a quasi-experiment reports as a posterior spread over the bias the data cannot rule out. Mapping that spread, and naming the commitment a positive conclusion requires, is what the quasi-experiment owes its readers. {ref}`meta_analysis_experiments` turns from a single threatened experiment to a whole series of them. ## Authors diff --git a/examples/references.bib b/examples/references.bib index b3b0a2598..e5a8f246c 100644 --- a/examples/references.bib +++ b/examples/references.bib @@ -269,6 +269,15 @@ @book{enders2022 year = {2022}, publisher = {The Guilford Press} } +@article{evans2006checking, + title = {Checking for prior-data conflict}, + author = {Evans, Michael and Moshonov, Hadas}, + journal = {Bayesian Analysis}, + volume = {1}, + number = {4}, + pages = {893--914}, + year = {2006} +} @book{facure2023causal, title = {Causal Inference in Python}, author = {Facure, Matheus}, @@ -476,6 +485,15 @@ @article{iacobucci2017mean year = {2017}, publisher = {Springer} } +@article{ibrahim2000power, + title = {Power prior distributions for regression models}, + author = {Ibrahim, Joseph G. and Chen, Ming-Hui}, + journal = {Statistical Science}, + volume = {15}, + number = {1}, + pages = {46--60}, + year = {2000} +} @article{imbens2003sensitivity, title = {Sensitivity to exogeneity assumptions in program evaluation}, author = {Imbens, Guido W.}, From bbf43ecf74bee852881e6a0fbb21550aaf43f3e6 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Sat, 13 Jun 2026 08:12:17 +0100 Subject: [PATCH 8/9] tidying assurance --- .../assurance_planning_simulation.ipynb | 41 ++++++++++--------- .../assurance_planning_simulation.myst.md | 29 +++++++------ 2 files changed, 38 insertions(+), 32 deletions(-) diff --git a/examples/causal_inference/assurance_planning_simulation.ipynb b/examples/causal_inference/assurance_planning_simulation.ipynb index e66ab1fa0..e4b0d5169 100644 --- a/examples/causal_inference/assurance_planning_simulation.ipynb +++ b/examples/causal_inference/assurance_planning_simulation.ipynb @@ -22,18 +22,17 @@ "The experimentation lifecycle as a Bosch triptych. *Left, Bayesian Assurance (this notebook):* before any data arrive, the planner reads possible effects from the prior and asks what the experiment will likely conclude. *Centre, Sensitivity Analysis:* a single experiment is wracked by the biases it cannot rule out, and the model is contorted to see which commitments its conclusion can survive. *Right, Meta-Analysis:* many experiments are pooled through a hierarchy of levels into a synthesis that becomes the next plan's prior. Three panels, one posterior machinery.\n", ":::\n", "\n", - "Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records, orientated at a new angle in pursuit of theory, anomalous findings and the next question.\n", - "\n", + "Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again, if we learn from the past. This is the cylce. \n", "## The question planners are actually asking\n", "\n", - "When we experiment, we implicitly assume the experiment will be useful. Our questions are \"operationalised\" as power heuristics: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold.\n", + "When we experiment, we implicitly assume the experiment will be useful. Our questions are \"operationalised\" as power heuristics: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we often ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold.\n", "\n", - "An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (like revenue per visitor) or binary (like conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery serves all three; what changes is the problem put to it.\n", + "An alternative is Bayesian Assurance. The construction is mechanical: draw from the priors, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The process is the same whether the response is continuous (like revenue per visitor) or binary (like conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same inferential machinery serves all three panels of the triptych, but what changes is the problem put to it.\n", "\n", ":::{admonition} Where this lands in regulatory practice\n", ":class: note\n", "\n", - "The assurance focus here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\\Pr(\\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference. The generative and inference models below are distinct objects for this reason, and a later section runs the assurance loop with the two priors deliberately mismatched. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical.\n", + "We are going to apply Bayesian assurance to a question in product-analytics but the technique has a wider application. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name. The FDA guidance formalises the success rule as $\\Pr(\\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference. The generative and inference models below are distinct objects for this reason. Below we'll runs the assurance loop with the two priors deliberately mismatched. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical.\n", ":::" ] }, @@ -130,7 +129,7 @@ "source": [ "## Power and assurance: two questions, different focus\n", "\n", - "Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null:\n", + "Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null at that value:\n", "\n", "$$\n", "\\text{Power}(N) = P\\!\\left(\\text{reject } H_0 \\mid \\theta = \\theta_0,\\; N\\right).\n", @@ -142,7 +141,7 @@ "\\text{Assurance}(N) = \\mathbb{E}_{\\substack{\\theta \\,\\sim\\, \\pi(\\theta) \\\\ \\hat{d} \\,\\sim\\, p(\\hat{d} \\mid \\theta,\\, N)}}\\!\\left[\\mathbb{1}\\!\\bigl(P(\\theta > 0 \\mid \\hat{d},\\, N) > c\\bigr)\\right].\n", "$$\n", "\n", - "The outer expectation averages over effect sizes drawn from the prior; the inner averages over data generated under each effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in.\n", + "The outer expectation averages over effect sizes drawn from the prior; the inner averages over data generated under each effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in. With that description in place, we can quantify how likely the result is when conditioned on the prior distribution. \n", "\n", "## The simulation engine\n", "\n", @@ -152,7 +151,7 @@ "2. **Fit the analysis model** to each synthetic dataset and apply the decision rule.\n", "3. **Record** the fraction of synthetic experiments in which the decision succeeded.\n", "\n", - "Drawing a truth and generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive." + "Drawing a truth and generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive. This workflow is defined as a function and passed through a sample size grid to determine how many _N_ is required to achieve surety in the finding. " ] }, { @@ -187,7 +186,9 @@ "\n", "The Gaussian case is the cleanest place to make the mechanics explicit. Each arm produces a revenue-per-visitor outcome with known noise scale $\\sigma_{\\text{obs}}$ (we treat this as known here; in practice it is estimated from a pilot or historical data). The team is interested in the treatment effect $\\delta = \\mu_B - \\mu_A$, and holds a prior over $\\delta$ informed by past experiments in similar product surfaces. That prior is not conjured from nothing: {ref}`meta_analysis_experiments` shows how a meta-analysis of past experiments produces exactly this kind of prior, so the synthesis at the end of one experiment's lifecycle is the planning input at the start of the next.\n", "\n", - "Two models do the work. The *generative model* is unconditioned: it places the planning prior on $\\delta$ and generates synthetic datasets, and it is the object we hand to `pm.sample_prior_predictive`. The *inference model* conditions on a dataset and returns the posterior on $\\delta$; the decision rule asks whether the posterior probability of a positive effect exceeds a threshold (0.95 below). For the assurance loop the conjugate Normal–Normal posterior on $\\delta$ admits a closed form, which lets us evaluate the decision on each synthetic experiment without running MCMC every time. We verify that closed form against the inference model's MCMC posterior at a single sample size before relying on it." + "Two models do the work. The *generative model* is unconditioned: it places the planning prior on $\\delta$ and generates synthetic datasets, and it is the object we hand to `pm.sample_prior_predictive`. The *inference model* conditions on a dataset and returns the posterior on $\\delta$; the decision rule asks whether the posterior probability of a positive effect exceeds a threshold (0.95 below). For the assurance loop the conjugate Normal–Normal posterior on $\\delta$ admits a closed form, which lets us evaluate the decision on each synthetic experiment without running MCMC every time. \n", + "\n", + "We will verify that closed form against the inference model's MCMC posterior at a single sample size before relying on it. This also underwrites the idea that we can use MCMC for this procedure even with more complex models. " ] }, { @@ -294,7 +295,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -439,7 +440,7 @@ "id": "fb2d9901", "metadata": {}, "source": [ - "The two posteriors agree to within MCMC noise. The closed form will carry the decision step; the generative model will carry the data step." + "The two posteriors agree to within MCMC noise. Now we get to the heart of the technique and push our procedure through different sample values of _N_." ] }, { @@ -587,6 +588,8 @@ "id": "191f92bd", "metadata": {}, "source": [ + "For each simulated future dataset, we compute the posterior distribution of the treatment effect and evaluate the posterior probability that the effect is positive, `prob_positive`. A trial is considered decisive if `prob_positive` exceeds the pre-specified `DECISION_THRESHOLD`. The Bayesian assurance is the proportion of simulated trials that satisfy this decision criterion. As the planned sample size increases, posterior uncertainty decreases, leading to a larger proportion of decisive trials and therefore higher assurance.\n", + "\n", "For comparison we compute frequentist power at a single fixed effect size. The convention is to set the effect to the prior mean, which is itself an arbitrary choice, and a revealing one." ] }, @@ -605,14 +608,14 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 923, + "height": 1123, "width": 3023 } }, @@ -636,7 +639,7 @@ "\n", "prior_prob_positive = 1.0 - stats.norm.cdf(0.0, EFFECT_PRIOR.mu, EFFECT_PRIOR.sigma)\n", "\n", - "fig, ax = plt.subplots(figsize=(15, 4.5))\n", + "fig, ax = plt.subplots(figsize=(15, 5.5))\n", "ax.plot(N_GRID, assurance_df[\"assurance\"], marker=\"o\", linewidth=2, label=\"Assurance (Bayesian)\")\n", "ax.plot(\n", " N_GRID,\n", @@ -912,7 +915,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -1662,9 +1665,9 @@ "\n", "## Planning as a posterior over future posteriors\n", "\n", - "The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty.\n", + "The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say.\n", "\n", - "Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability that the inference about to be drawn will be useful? The operation that produces assurance is the same one that will produce the posterior at the end of the experiment. Only the data are still hypothetical. Assurance makes explicit the assumption power leaves buried, a believed distribution of effects, and gives that assumption a posterior to argue over.\n", + "Statistical power questions answers a conditional: given a specific effect, what probability of detection? Bayesian assurance answers a marginal question: given everything currently believed, what probability that the inference about to be drawn will be useful? The operation that produces assurance is the same one that will produce the posterior at the end of the experiment - only the data are still hypothetical. Assurance makes explicit the assumption power leaves buried, a believed distribution of effects, and derives from that assumption a posterior to evaluate.\n", "\n", "The Gaussian case and the Bernoulli case ran the same inference machinery. What changed was the likelihood, and with it the ceiling its geometry sets on how much assurance any sample size can buy. The next notebook, {ref}`sensitivity_confounding`, keeps the machinery and changes the question: not what an experiment will probably say, but what it did say once clean identification is itself in doubt.\n", "\n", @@ -1698,7 +1701,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Fri, 12 Jun 2026\n", + "Last updated: Sat, 13 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.13.13\n", diff --git a/examples/causal_inference/assurance_planning_simulation.myst.md b/examples/causal_inference/assurance_planning_simulation.myst.md index 47b8ed9d0..576b625b4 100644 --- a/examples/causal_inference/assurance_planning_simulation.myst.md +++ b/examples/causal_inference/assurance_planning_simulation.myst.md @@ -28,18 +28,17 @@ kernelspec: The experimentation lifecycle as a Bosch triptych. *Left, Bayesian Assurance (this notebook):* before any data arrive, the planner reads possible effects from the prior and asks what the experiment will likely conclude. *Centre, Sensitivity Analysis:* a single experiment is wracked by the biases it cannot rule out, and the model is contorted to see which commitments its conclusion can survive. *Right, Meta-Analysis:* many experiments are pooled through a hierarchy of levels into a synthesis that becomes the next plan's prior. Three panels, one posterior machinery. ::: -Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again. Each new experiment is a digression from the historic records, orientated at a new angle in pursuit of theory, anomalous findings and the next question. - +Experimental questions are seeded in the science that preceded them. Experiments spawn further questions again, if we learn from the past. This is the cylce. ## The question planners are actually asking -When we experiment, we implicitly assume the experiment will be useful. Our questions are "operationalised" as power heuristics: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold. +When we experiment, we implicitly assume the experiment will be useful. Our questions are "operationalised" as power heuristics: given an effect size, what is the probability the experiment crosses a fixed threshold? This is an indirect and contorted gauge of efficacy. Instead of asking whether the treatment is effective, we often ask whether the results are surprising assuming a degree of effectiveness. The slippage between these two questions has produced a generation of experimental plans calibrated to invented effect sizes rather than to the beliefs the planners actually hold. -An alternative is Bayesian Assurance. The construction is mechanical: draw an effect from the prior, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The engine is the same whether the response is continuous (like revenue per visitor) or binary (like conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same posterior machinery serves all three; what changes is the problem put to it. +An alternative is Bayesian Assurance. The construction is mechanical: draw from the priors, generate a dataset, fit the treatment model, apply the decision rule, repeat. The result is a single number: the probability the experiment will conclude what its stakeholders need. The process is the same whether the response is continuous (like revenue per visitor) or binary (like conversion); we develop it on the Gaussian case first and confirm the process is invariant to the response scale. This notebook is the first of three on the lifecycle of a Bayesian experiment; see {ref}`sensitivity_confounding` for the interpretation counterpart and {ref}`meta_analysis_experiments` for the synthesis counterpart. The same inferential machinery serves all three panels of the triptych, but what changes is the problem put to it. :::{admonition} Where this lands in regulatory practice :class: note -The assurance focus here is not only a product-analytics convenience. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name, and formalises the success rule as $\Pr(\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference. The generative and inference models below are distinct objects for this reason, and a later section runs the assurance loop with the two priors deliberately mismatched. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical. +We are going to apply Bayesian assurance to a question in product-analytics but the technique has a wider application. The FDA's 2026 draft guidance on Bayesian methodology in clinical trials defines *Bayesian power* as the probability of meeting the success criterion averaged over a prior distribution {cite:p}`fda2026bayesian`, which is assurance under another name. The FDA guidance formalises the success rule as $\Pr(\text{effect} > a) > c$ (the rule used below, with $a = 0$ and $c = 0.95$). The same guidance separates the *design prior* that generates the synthetic trials from the *analysis prior* used in inference. The generative and inference models below are distinct objects for this reason. Below we'll runs the assurance loop with the two priors deliberately mismatched. The clinical trial setting in the FDA report differs from product experimentation; the machinery is identical. ::: ```{code-cell} ipython3 @@ -85,7 +84,7 @@ class EffectPrior: ## Power and assurance: two questions, different focus -Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null: +Power and assurance share an interface and answer different questions. Power conditions on a specific effect size $\theta_0$ that the planner has been pressed into naming, and computes the probability of rejecting the null at that value: $$ \text{Power}(N) = P\!\left(\text{reject } H_0 \mid \theta = \theta_0,\; N\right). @@ -97,7 +96,7 @@ $$ \text{Assurance}(N) = \mathbb{E}_{\substack{\theta \,\sim\, \pi(\theta) \\ \hat{d} \,\sim\, p(\hat{d} \mid \theta,\, N)}}\!\left[\mathbb{1}\!\bigl(P(\theta > 0 \mid \hat{d},\, N) > c\bigr)\right]. $$ -The outer expectation averages over effect sizes drawn from the prior; the inner averages over data generated under each effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in. +The outer expectation averages over effect sizes drawn from the prior; the inner averages over data generated under each effect ({cite:p}`ohagan2005assurance`, {cite:p}`spiegelhalter2004bayesian`). The integration is the entire move. Where power asks the planner to invent a world, assurance asks them to describe the one they already believe they are in. With that description in place, we can quantify how likely the result is when conditioned on the prior distribution. ## The simulation engine @@ -107,7 +106,7 @@ Assurance is an expectation over the *prior predictive distribution*: the joint 2. **Fit the analysis model** to each synthetic dataset and apply the decision rule. 3. **Record** the fraction of synthetic experiments in which the decision succeeded. -Drawing a truth and generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive. +Drawing a truth and generating data under it is exactly what `pm.sample_prior_predictive` does on a model with no observed data, so we let PyMC's generative machinery produce the synthetic experiments rather than hand-rolling the sampling with `numpy`. The generative model that produces the data and the inference model that analyses it are separate objects, and keeping them separate is the point: the generative model encodes the worlds we are planning for, the inference model encodes how we will reason once real data arrive. This workflow is defined as a function and passed through a sample size grid to determine how many _N_ is required to achieve surety in the finding. ```{code-cell} ipython3 def assurance_curve(assurance_fn, N_grid, n_sims, rng, **kwargs): @@ -123,7 +122,9 @@ def assurance_curve(assurance_fn, N_grid, n_sims, rng, **kwargs): The Gaussian case is the cleanest place to make the mechanics explicit. Each arm produces a revenue-per-visitor outcome with known noise scale $\sigma_{\text{obs}}$ (we treat this as known here; in practice it is estimated from a pilot or historical data). The team is interested in the treatment effect $\delta = \mu_B - \mu_A$, and holds a prior over $\delta$ informed by past experiments in similar product surfaces. That prior is not conjured from nothing: {ref}`meta_analysis_experiments` shows how a meta-analysis of past experiments produces exactly this kind of prior, so the synthesis at the end of one experiment's lifecycle is the planning input at the start of the next. -Two models do the work. The *generative model* is unconditioned: it places the planning prior on $\delta$ and generates synthetic datasets, and it is the object we hand to `pm.sample_prior_predictive`. The *inference model* conditions on a dataset and returns the posterior on $\delta$; the decision rule asks whether the posterior probability of a positive effect exceeds a threshold (0.95 below). For the assurance loop the conjugate Normal–Normal posterior on $\delta$ admits a closed form, which lets us evaluate the decision on each synthetic experiment without running MCMC every time. We verify that closed form against the inference model's MCMC posterior at a single sample size before relying on it. +Two models do the work. The *generative model* is unconditioned: it places the planning prior on $\delta$ and generates synthetic datasets, and it is the object we hand to `pm.sample_prior_predictive`. The *inference model* conditions on a dataset and returns the posterior on $\delta$; the decision rule asks whether the posterior probability of a positive effect exceeds a threshold (0.95 below). For the assurance loop the conjugate Normal–Normal posterior on $\delta$ admits a closed form, which lets us evaluate the decision on each synthetic experiment without running MCMC every time. + +We will verify that closed form against the inference model's MCMC posterior at a single sample size before relying on it. This also underwrites the idea that we can use MCMC for this procedure even with more complex models. ```{code-cell} ipython3 SIGMA_OBS = 4.0 @@ -219,7 +220,7 @@ pd.DataFrame( ).round(3) ``` -The two posteriors agree to within MCMC noise. The closed form will carry the decision step; the generative model will carry the data step. +The two posteriors agree to within MCMC noise. Now we get to the heart of the technique and push our procedure through different sample values of _N_. ```{code-cell} ipython3 def gaussian_assurance_at_N(N, n_sims, prior, sigma_obs, threshold, rng, analysis_prior=None): @@ -254,6 +255,8 @@ assurance_df = assurance_curve( assurance_df ``` +For each simulated future dataset, we compute the posterior distribution of the treatment effect and evaluate the posterior probability that the effect is positive, `prob_positive`. A trial is considered decisive if `prob_positive` exceeds the pre-specified `DECISION_THRESHOLD`. The Bayesian assurance is the proportion of simulated trials that satisfy this decision criterion. As the planned sample size increases, posterior uncertainty decreases, leading to a larger proportion of decisive trials and therefore higher assurance. + For comparison we compute frequentist power at a single fixed effect size. The convention is to set the effect to the prior mean, which is itself an arbitrary choice, and a revealing one. ```{code-cell} ipython3 @@ -273,7 +276,7 @@ power_at_pessimistic = np.array( prior_prob_positive = 1.0 - stats.norm.cdf(0.0, EFFECT_PRIOR.mu, EFFECT_PRIOR.sigma) -fig, ax = plt.subplots(figsize=(15, 4.5)) +fig, ax = plt.subplots(figsize=(15, 5.5)) ax.plot(N_GRID, assurance_df["assurance"], marker="o", linewidth=2, label="Assurance (Bayesian)") ax.plot( N_GRID, @@ -753,9 +756,9 @@ The three curves disagree most where they should: at small sample sizes where th ## Planning as a posterior over future posteriors -The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. Assurance is the posterior we can compute now, over the posteriors we will compute later: a rehearsal under the lights of our current uncertainty. +The experiment we have not yet run is a posterior we have not yet computed. What we have is a prior, a model, and the patience to ask what the experiment will likely say. -Power answers a conditional: given a specific effect, what probability of detection? Assurance answers a marginal: given everything currently believed, what probability that the inference about to be drawn will be useful? The operation that produces assurance is the same one that will produce the posterior at the end of the experiment. Only the data are still hypothetical. Assurance makes explicit the assumption power leaves buried, a believed distribution of effects, and gives that assumption a posterior to argue over. +Statistical power questions answers a conditional: given a specific effect, what probability of detection? Bayesian assurance answers a marginal question: given everything currently believed, what probability that the inference about to be drawn will be useful? The operation that produces assurance is the same one that will produce the posterior at the end of the experiment - only the data are still hypothetical. Assurance makes explicit the assumption power leaves buried, a believed distribution of effects, and derives from that assumption a posterior to evaluate. The Gaussian case and the Bernoulli case ran the same inference machinery. What changed was the likelihood, and with it the ceiling its geometry sets on how much assurance any sample size can buy. The next notebook, {ref}`sensitivity_confounding`, keeps the machinery and changes the question: not what an experiment will probably say, but what it did say once clean identification is itself in doubt. From 5008d5e292d5efda347fe2b355c14aa4c16a704c Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Sat, 13 Jun 2026 19:51:21 +0100 Subject: [PATCH 9/9] tidying metaanalysis --- .../multiple_experiments_meta_analysis.ipynb | 467 +++++++++++------- ...multiple_experiments_meta_analysis.myst.md | 188 ++++--- 2 files changed, 384 insertions(+), 271 deletions(-) diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.ipynb b/examples/causal_inference/multiple_experiments_meta_analysis.ipynb index 3ab12a0a2..2a5163070 100644 --- a/examples/causal_inference/multiple_experiments_meta_analysis.ipynb +++ b/examples/causal_inference/multiple_experiments_meta_analysis.ipynb @@ -701,7 +701,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -1179,30 +1179,69 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 559, + "width": 1223 } }, "output_type": "display_data" } ], "source": [ - "az.plot_dist(idata_partial, var_names=[\"mu\", \"tau\"])\n", - "axes = plt.gcf().axes\n", - "axes[0].axvline(TRUE_MU, color=\"C2\", linestyle=\"--\", label=f\"true = {TRUE_MU:.2f}\")\n", - "axes[0].legend()\n", - "axes[0].set_title(r\"Population mean $\\mu$\")\n", - "axes[1].axvline(TRUE_TAU, color=\"C2\", linestyle=\"--\", label=f\"true = {TRUE_TAU:.2f}\")\n", - "axes[1].legend()\n", - "axes[1].set_title(r\"Between-market SD $\\tau$\")\n", - "plt.tight_layout()" + "pc_mu = az.plot_dist(\n", + " idata_partial,\n", + " var_names=[\"mu\"],\n", + " visuals={\"title\": {\"text\": r\"Population mean $\\mu$\"}},\n", + ")\n", + "az.add_lines(\n", + " pc_mu,\n", + " values=TRUE_MU,\n", + " visuals={\"ref_line\": {\"color\": \"C2\", \"label\": f\"true = {TRUE_MU:.2f}\"}},\n", + ")\n", + "pc_mu.get_viz(\"plot\").legend()\n", + "\n", + "pc_tau = az.plot_dist(\n", + " idata_partial,\n", + " var_names=[\"tau\"],\n", + " visuals={\"title\": {\"text\": r\"Between-market SD $\\tau$\"}},\n", + ")\n", + "az.add_lines(\n", + " pc_tau,\n", + " values=TRUE_TAU,\n", + " visuals={\"ref_line\": {\"color\": \"C2\", \"label\": f\"true = {TRUE_TAU:.2f}\"}},\n", + ")\n", + "pc_tau.get_viz(\"plot\").legend()" ] }, { @@ -1210,7 +1249,7 @@ "id": "ef1c249d", "metadata": {}, "source": [ - "The posterior of $\\tau$ is concentrated well away from zero, which is itself the result of substantive interest: the eight markets disagree about the size of the treatment effect in a way the data demand be respected. The next section turns this into a prediction.\n", + "The posterior of $\\tau$ is concentrated well away from zero, which is itself the result of substantive interest: the eight markets disagree about the size of the treatment effect in a way the data demand be respected. The conversion-rate version tells the same story, as we confirm next.\n", "\n", "## The same machinery on a binary outcome\n", "\n", @@ -1316,80 +1355,32 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "" ] }, - "metadata": { - "image/png": { - "height": 501, - "width": 2367 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_dist(idata_partial_bern, var_names=[\"mu\", \"tau\"])\n", - "axes = plt.gcf().axes\n", - "axes[0].axvline(TRUE_MU_LOGIT, color=\"C2\", linestyle=\"--\", label=f\"true = {TRUE_MU_LOGIT:.2f}\")\n", - "axes[0].legend()\n", - "axes[0].set_title(r\"Population log-odds effect $\\mu$\")\n", - "axes[1].axvline(TRUE_TAU_LOGIT, color=\"C2\", linestyle=\"--\", label=f\"true = {TRUE_TAU_LOGIT:.2f}\")\n", - "axes[1].legend()\n", - "axes[1].set_title(r\"Between-market SD $\\tau$ (log-odds)\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "f7537167", - "metadata": {}, - "source": [ - "The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. The log-odds parameterisation carries a structural advantage the probability scale does not: a given value of τ means the same degree of between-market variability in the treatment effect regardless of what the baseline conversion rate happens to be. On the probability scale, a τ of 0.05 is meaningful heterogeneity at a 5\\% baseline and negligible noise at a 50\\% baseline; on the logit, τ is scale-invariant in the way the analysis needs it to be.\n", - "\n", - "## Predicting the next experiment\n", - "\n", - "The stakeholder question that occasioned this entire exercise is about a specific market: what will happen if we run this in Market I next quarter? The population mean is rarely what they are asking for. The hierarchical model answers in two posterior-predictive flavours, and the difference between them matters.\n", - "\n", - "**The true-effect predictive** asks for the unknown $\\theta_{\\text{new}}$ of a new market drawn from the same population:\n", - "\n", - "$$\\theta_{\\text{new}} \\mid \\text{data} \\sim \\mathcal{N}(\\mu_{\\text{post}}, \\sqrt{\\tau_{\\text{post}}^2 + \\sigma_{\\mu, \\text{post}}^2}).$$\n", - "\n", - "**The observation predictive** layers experimental noise on top:\n", - "\n", - "$$\\hat d_{\\text{new}} \\mid \\text{data} \\sim \\theta_{\\text{new}} + \\mathcal{N}(0, s_{\\text{new}}).$$\n", - "\n", - "The first is what the team should report when they say \"our best estimate of the effect in a new market\". The second is what they should report when they say \"what we expect to see if we run the experiment again\". The gap between the two is exactly the experimental-noise envelope that single-market estimates conflate with population variation." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6d7d4a17", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-01T17:59:37.409143Z", - "iopub.status.busy": "2026-06-01T17:59:37.409054Z", - "iopub.status.idle": "2026-06-01T17:59:37.777264Z", - "shell.execute_reply": "2026-06-01T17:59:37.776876Z" - } - }, - "outputs": [ + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, { "data": { + "image/png": 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" ] }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "image/png": { + "height": 559, + "width": 1223 + } + }, + "output_type": "display_data" }, { "data": { - "image/png": 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" ] @@ -1397,123 +1388,44 @@ "metadata": { "image/png": { "height": 559, - "width": 1439 + "width": 1223 } }, "output_type": "display_data" } ], "source": [ - "mu_samples = idata_partial.posterior[\"mu\"].values.flatten()\n", - "tau_samples = idata_partial.posterior[\"tau\"].values.flatten()\n", - "rng_pp = np.random.default_rng(RANDOM_SEED + 1)\n", - "theta_new_samples = rng_pp.normal(mu_samples, tau_samples)\n", - "s_new = np.median(se_obs)\n", - "d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples))\n", - "\n", - "dt = az.from_dict(\n", - " {\n", - " \"posterior\": {\n", - " \"theta_new\": theta_new_samples,\n", - " \"d_hat_new\": d_hat_new_samples,\n", - " }\n", - " },\n", - " sample_dims=[\"sample\"],\n", + "pc_mu = az.plot_dist(\n", + " idata_partial_bern,\n", + " var_names=[\"mu\"],\n", + " visuals={\"title\": {\"text\": r\"Population log-odds effect $\\mu$\"}},\n", ")\n", + "az.add_lines(\n", + " pc_mu,\n", + " values=TRUE_MU_LOGIT,\n", + " visuals={\"ref_line\": {\"color\": \"C2\", \"label\": f\"true = {TRUE_MU_LOGIT:.2f}\"}},\n", + ")\n", + "pc_mu.get_viz(\"plot\").legend()\n", "\n", - "pc = az.plot_dist(\n", - " dt,\n", - " kind=\"hist\",\n", - " sample_dims=[\"sample\"],\n", - " cols=[],\n", - " aes={\"color\": [\"__variable__\"]},\n", - " visuals={\n", - " \"point_estimate\": False,\n", - " \"point_estimate_text\": False,\n", - " \"credible_interval\": False,\n", - " \"title\": {\"text\": \"Two predictive distributions for the ninth market\"},\n", - " },\n", + "pc_tau = az.plot_dist(\n", + " idata_partial_bern,\n", + " var_names=[\"tau\"],\n", + " visuals={\"title\": {\"text\": r\"Between-market SD $\\tau$ (log-odds)\"}},\n", ")\n", - "pc.add_legend(\"__variable__\")\n", - "az.add_lines(pc, values=0.0, visuals={\"ref_line\": {\"color\": \"black\", \"label\": \"0\"}})" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "645d5c5e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-01T17:59:37.779232Z", - "iopub.status.busy": "2026-06-01T17:59:37.779104Z", - "iopub.status.idle": "2026-06-01T17:59:37.783309Z", - "shell.execute_reply": "2026-06-01T17:59:37.782922Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
posterior probability > 0
$\\theta_{\\text{new}}$ (true effect)0.941
$\\hat d_{\\text{new}}$ (observed estimate)0.884
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" - ], - "text/plain": [ - " posterior probability > 0\n", - "$\\theta_{\\text{new}}$ (true effect) 0.941\n", - "$\\hat d_{\\text{new}}$ (observed estimate) 0.884" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prob_theta_new_positive = float((theta_new_samples > 0).mean())\n", - "prob_d_hat_new_positive = float((d_hat_new_samples > 0).mean())\n", - "pd.DataFrame(\n", - " {\"posterior probability > 0\": [prob_theta_new_positive, prob_d_hat_new_positive]},\n", - " index=[r\"$\\theta_{\\text{new}}$ (true effect)\", r\"$\\hat d_{\\text{new}}$ (observed estimate)\"],\n", - ").round(3)" + "az.add_lines(\n", + " pc_tau,\n", + " values=TRUE_TAU_LOGIT,\n", + " visuals={\"ref_line\": {\"color\": \"C2\", \"label\": f\"true = {TRUE_TAU_LOGIT:.2f}\"}},\n", + ")\n", + "pc_tau.get_viz(\"plot\").legend()" ] }, { "cell_type": "markdown", - "id": "cb419ca8", + "id": "355a8d07", "metadata": {}, "source": [ - "The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\\theta_{\\text{new}}$ as the planning input for the next market, which {ref}`assurance_planning` then consumes as its prior, is the way to feed accumulating evidence forward without losing track of the noise." + "The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. The log-odds parameterisation carries a structural advantage the probability scale does not: a given value of τ means the same degree of between-market variability in the treatment effect regardless of what the baseline conversion rate happens to be. On the probability scale, a τ of 0.05 is meaningful heterogeneity at a 5\\% baseline and negligible noise at a 50\\% baseline; on the logit, τ is scale-invariant in the way the analysis needs it to be." ] }, { @@ -1523,12 +1435,12 @@ "source": [ "## How much to borrow\n", "\n", - "Borrowing across markets raises a question the hierarchical model answers quietly: how far should one market's estimate move toward the others. The shrinkage already shown is that answer in action. Each market's pull toward the population mean is a weight set by its own precision against the between-market variance $\\tau$ the data infer." + "With $\\tau$ estimated on both scales, borrowing across markets raises a question the hierarchical model answers quietly: how far should one market's estimate move toward the others. The shrinkage already shown is that answer in action. Each market's pull toward the population mean is a weight set by its own precision against the between-market variance $\\tau$ the data infer." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "id": "4c87404e", "metadata": {}, "outputs": [ @@ -1623,7 +1535,7 @@ "7 Market H 0.167 0.241" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1655,7 +1567,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "id": "5915e805", "metadata": {}, "outputs": [ @@ -1729,7 +1641,7 @@ "4 1.00 0.461 0.091" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1748,15 +1660,38 @@ }, { "cell_type": "markdown", - "id": "ef5a7989", + "id": "bb991411", "metadata": {}, "source": [ - "The power prior borrows the precision of the pooled mean and treats every market as one draw from a single shared effect. A genuinely new market also carries between-market variation, which the hierarchical predictive folds in through $\\tau$. The figure sets the two side by side." + "The power prior must be measured against the belief the hierarchical model already holds about a market it has not seen. That belief is the model's *predictive distribution*, and it comes in two forms. The true-effect predictive gives the unknown effect of a new market drawn from the same population,\n", + "\n", + "$$\\theta_{\\text{new}} \\mid \\text{data} \\sim \\mathcal{N}\\!\\left(\\mu_{\\text{post}},\\ \\sqrt{\\tau_{\\text{post}}^2 + \\sigma_{\\mu,\\text{post}}^2}\\right),$$\n", + "\n", + "and the observation predictive layers experimental noise on top,\n", + "\n", + "$$\\hat d_{\\text{new}} \\mid \\text{data} \\sim \\theta_{\\text{new}} + \\mathcal{N}(0, s_{\\text{new}}).$$\n", + "\n", + "We draw both now. The first sets the width the power prior is chasing; the second is the yardstick for the conflict check below. The power prior borrows the precision of the pooled mean and treats every market as one draw from a single shared effect, while the hierarchical predictive folds the between-market variation back in through $\\tau$. The figure sets the two side by side." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, + "id": "91ce8350", + "metadata": {}, + "outputs": [], + "source": [ + "mu_samples = idata_partial.posterior[\"mu\"].values.flatten()\n", + "tau_samples = idata_partial.posterior[\"tau\"].values.flatten()\n", + "rng_pp = np.random.default_rng(RANDOM_SEED + 1)\n", + "theta_new_samples = rng_pp.normal(mu_samples, tau_samples)\n", + "s_new = np.median(se_obs)\n", + "d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "id": "de589904", "metadata": {}, "outputs": [ @@ -1821,7 +1756,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "b0b7bf16", "metadata": {}, "outputs": [ @@ -1871,7 +1806,7 @@ "conflicting market -0.40 0.044" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1904,6 +1839,154 @@ "The concordant market sits in the body of the predictive and the tail probability is large; the prior and the new data describe one population, so the experiment can borrow at full strength and keep the precision that buys. The conflicting market sits far out and the tail probability is small, the trigger the guidance names for reassessing how much to borrow {cite:p}`fda2026bayesian`: lower $\\alpha$, widen the prior, or hold the borrowed estimate aside until the discrepancy is understood. Dynamic borrowing through $\\tau$ softens this failure on its own, since a heterogeneous set of markets produces a wide predictive that absorbs a surprising estimate, while a static power prior never adapts, so the analyst must run the check every time. Whatever borrowing strength passes the check is the prior the next experiment plans with." ] }, + { + "cell_type": "markdown", + "id": "469f684d", + "metadata": {}, + "source": [ + "## Predicting the next experiment\n", + "\n", + "The predictive we drew to calibrate borrowing also answers the question that occasioned the whole exercise: what will happen if we run the redesign in a market we have not tested yet. The two flavours now do decision work. The true-effect predictive $\\theta_{\\text{new}}$ is the best estimate of the effect in a new market; the observation predictive $\\hat d_{\\text{new}}$ is what to expect the next experiment to actually return, noise included. The gap between them is the experimental-noise envelope that a single-market estimate conflates with population variation." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1a090549", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 559, + "width": 1439 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "dt = az.from_dict(\n", + " {\n", + " \"posterior\": {\n", + " \"theta_new\": theta_new_samples,\n", + " \"d_hat_new\": d_hat_new_samples,\n", + " }\n", + " },\n", + " sample_dims=[\"sample\"],\n", + ")\n", + "\n", + "pc = az.plot_dist(\n", + " dt,\n", + " kind=\"hist\",\n", + " sample_dims=[\"sample\"],\n", + " cols=[],\n", + " aes={\"color\": [\"__variable__\"]},\n", + " visuals={\n", + " \"point_estimate\": False,\n", + " \"point_estimate_text\": False,\n", + " \"credible_interval\": False,\n", + " \"title\": {\"text\": \"Two predictive distributions for the ninth market\"},\n", + " },\n", + ")\n", + "pc.add_legend(\"__variable__\")\n", + "az.add_lines(pc, values=0.0, visuals={\"ref_line\": {\"color\": \"black\", \"label\": \"0\"}})" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "645d5c5e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-01T17:59:37.779232Z", + "iopub.status.busy": "2026-06-01T17:59:37.779104Z", + "iopub.status.idle": "2026-06-01T17:59:37.783309Z", + "shell.execute_reply": "2026-06-01T17:59:37.782922Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
posterior probability > 0
$\\theta_{\\text{new}}$ (true effect)0.941
$\\hat d_{\\text{new}}$ (observed estimate)0.884
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" + ], + "text/plain": [ + " posterior probability > 0\n", + "$\\theta_{\\text{new}}$ (true effect) 0.941\n", + "$\\hat d_{\\text{new}}$ (observed estimate) 0.884" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prob_theta_new_positive = float((theta_new_samples > 0).mean())\n", + "prob_d_hat_new_positive = float((d_hat_new_samples > 0).mean())\n", + "pd.DataFrame(\n", + " {\"posterior probability > 0\": [prob_theta_new_positive, prob_d_hat_new_positive]},\n", + " index=[r\"$\\theta_{\\text{new}}$ (true effect)\", r\"$\\hat d_{\\text{new}}$ (observed estimate)\"],\n", + ").round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "cb419ca8", + "metadata": {}, + "source": [ + "The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\\theta_{\\text{new}}$ as the planning input for the next market, which {ref}`assurance_planning` then consumes as its prior, is the way to feed accumulating evidence forward without losing track of the noise." + ] + }, { "cell_type": "markdown", "id": "e7c2c1c7", @@ -1932,7 +2015,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "2e2e3295", "metadata": { "execution": { @@ -1947,7 +2030,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Fri, 12 Jun 2026\n", + "Last updated: Sat, 13 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.13.13\n", diff --git a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md index fd3a789b2..230be620c 100644 --- a/examples/causal_inference/multiple_experiments_meta_analysis.myst.md +++ b/examples/causal_inference/multiple_experiments_meta_analysis.myst.md @@ -444,18 +444,32 @@ ax.legend(loc="lower right"); The hierarchical model returns two population-level quantities, and the conventional reporting habit of leading with $\mu$ obscures the more important one. The posterior of $\tau$, the between-market standard deviation of true effects, is what tells the team how transportable any single result is to a new context. A small $\tau$ means the markets are nearly exchangeable, and the experiment generalises cleanly; a large $\tau$ means the markets are heterogeneous, and the next market is a meaningfully new experiment. Reporting only $\mu$ collapses this into a point and hides the variability that the next stakeholder will live with. ```{code-cell} ipython3 -az.plot_dist(idata_partial, var_names=["mu", "tau"]) -axes = plt.gcf().axes -axes[0].axvline(TRUE_MU, color="C2", linestyle="--", label=f"true = {TRUE_MU:.2f}") -axes[0].legend() -axes[0].set_title(r"Population mean $\mu$") -axes[1].axvline(TRUE_TAU, color="C2", linestyle="--", label=f"true = {TRUE_TAU:.2f}") -axes[1].legend() -axes[1].set_title(r"Between-market SD $\tau$") -plt.tight_layout() +pc_mu = az.plot_dist( + idata_partial, + var_names=["mu"], + visuals={"title": {"text": r"Population mean $\mu$"}}, +) +az.add_lines( + pc_mu, + values=TRUE_MU, + visuals={"ref_line": {"color": "C2", "label": f"true = {TRUE_MU:.2f}"}}, +) +pc_mu.get_viz("plot").legend() + +pc_tau = az.plot_dist( + idata_partial, + var_names=["tau"], + visuals={"title": {"text": r"Between-market SD $\tau$"}}, +) +az.add_lines( + pc_tau, + values=TRUE_TAU, + visuals={"ref_line": {"color": "C2", "label": f"true = {TRUE_TAU:.2f}"}}, +) +pc_tau.get_viz("plot").legend() ``` -The posterior of $\tau$ is concentrated well away from zero, which is itself the result of substantive interest: the eight markets disagree about the size of the treatment effect in a way the data demand be respected. The next section turns this into a prediction. +The posterior of $\tau$ is concentrated well away from zero, which is itself the result of substantive interest: the eight markets disagree about the size of the treatment effect in a way the data demand be respected. The conversion-rate version tells the same story, as we confirm next. ## The same machinery on a binary outcome @@ -512,84 +526,38 @@ with partial_bern_model: ``` ```{code-cell} ipython3 -az.plot_dist(idata_partial_bern, var_names=["mu", "tau"]) -axes = plt.gcf().axes -axes[0].axvline(TRUE_MU_LOGIT, color="C2", linestyle="--", label=f"true = {TRUE_MU_LOGIT:.2f}") -axes[0].legend() -axes[0].set_title(r"Population log-odds effect $\mu$") -axes[1].axvline(TRUE_TAU_LOGIT, color="C2", linestyle="--", label=f"true = {TRUE_TAU_LOGIT:.2f}") -axes[1].legend() -axes[1].set_title(r"Between-market SD $\tau$ (log-odds)") -plt.tight_layout() -``` - -The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. The log-odds parameterisation carries a structural advantage the probability scale does not: a given value of τ means the same degree of between-market variability in the treatment effect regardless of what the baseline conversion rate happens to be. On the probability scale, a τ of 0.05 is meaningful heterogeneity at a 5\% baseline and negligible noise at a 50\% baseline; on the logit, τ is scale-invariant in the way the analysis needs it to be. - -## Predicting the next experiment - -The stakeholder question that occasioned this entire exercise is about a specific market: what will happen if we run this in Market I next quarter? The population mean is rarely what they are asking for. The hierarchical model answers in two posterior-predictive flavours, and the difference between them matters. - -**The true-effect predictive** asks for the unknown $\theta_{\text{new}}$ of a new market drawn from the same population: - -$$\theta_{\text{new}} \mid \text{data} \sim \mathcal{N}(\mu_{\text{post}}, \sqrt{\tau_{\text{post}}^2 + \sigma_{\mu, \text{post}}^2}).$$ - -**The observation predictive** layers experimental noise on top: - -$$\hat d_{\text{new}} \mid \text{data} \sim \theta_{\text{new}} + \mathcal{N}(0, s_{\text{new}}).$$ - -The first is what the team should report when they say "our best estimate of the effect in a new market". The second is what they should report when they say "what we expect to see if we run the experiment again". The gap between the two is exactly the experimental-noise envelope that single-market estimates conflate with population variation. - -```{code-cell} ipython3 -mu_samples = idata_partial.posterior["mu"].values.flatten() -tau_samples = idata_partial.posterior["tau"].values.flatten() -rng_pp = np.random.default_rng(RANDOM_SEED + 1) -theta_new_samples = rng_pp.normal(mu_samples, tau_samples) -s_new = np.median(se_obs) -d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples)) - -dt = az.from_dict( - { - "posterior": { - "theta_new": theta_new_samples, - "d_hat_new": d_hat_new_samples, - } - }, - sample_dims=["sample"], +pc_mu = az.plot_dist( + idata_partial_bern, + var_names=["mu"], + visuals={"title": {"text": r"Population log-odds effect $\mu$"}}, ) - -pc = az.plot_dist( - dt, - kind="hist", - sample_dims=["sample"], - cols=[], - aes={"color": ["__variable__"]}, - visuals={ - "point_estimate": False, - "point_estimate_text": False, - "credible_interval": False, - "title": {"text": "Two predictive distributions for the ninth market"}, - }, +az.add_lines( + pc_mu, + values=TRUE_MU_LOGIT, + visuals={"ref_line": {"color": "C2", "label": f"true = {TRUE_MU_LOGIT:.2f}"}}, ) -pc.add_legend("__variable__") -az.add_lines(pc, values=0.0, visuals={"ref_line": {"color": "black", "label": "0"}}) -``` +pc_mu.get_viz("plot").legend() -```{code-cell} ipython3 -prob_theta_new_positive = float((theta_new_samples > 0).mean()) -prob_d_hat_new_positive = float((d_hat_new_samples > 0).mean()) -pd.DataFrame( - {"posterior probability > 0": [prob_theta_new_positive, prob_d_hat_new_positive]}, - index=[r"$\theta_{\text{new}}$ (true effect)", r"$\hat d_{\text{new}}$ (observed estimate)"], -).round(3) +pc_tau = az.plot_dist( + idata_partial_bern, + var_names=["tau"], + visuals={"title": {"text": r"Between-market SD $\tau$ (log-odds)"}}, +) +az.add_lines( + pc_tau, + values=TRUE_TAU_LOGIT, + visuals={"ref_line": {"color": "C2", "label": f"true = {TRUE_TAU_LOGIT:.2f}"}}, +) +pc_tau.get_viz("plot").legend() ``` -The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\theta_{\text{new}}$ as the planning input for the next market, which {ref}`assurance_planning` then consumes as its prior, is the way to feed accumulating evidence forward without losing track of the noise. +The Bernoulli picture is the Gaussian picture on a different link. The population mean log-odds effect is recovered; the between-market variance is recovered; the per-market shrinkage works as before. The log-odds parameterisation carries a structural advantage the probability scale does not: a given value of τ means the same degree of between-market variability in the treatment effect regardless of what the baseline conversion rate happens to be. On the probability scale, a τ of 0.05 is meaningful heterogeneity at a 5\% baseline and negligible noise at a 50\% baseline; on the logit, τ is scale-invariant in the way the analysis needs it to be. +++ ## How much to borrow -Borrowing across markets raises a question the hierarchical model answers quietly: how far should one market's estimate move toward the others. The shrinkage already shown is that answer in action. Each market's pull toward the population mean is a weight set by its own precision against the between-market variance $\tau$ the data infer. +With $\tau$ estimated on both scales, borrowing across markets raises a question the hierarchical model answers quietly: how far should one market's estimate move toward the others. The shrinkage already shown is that answer in action. Each market's pull toward the population mean is a weight set by its own precision against the between-market variance $\tau$ the data infer. ```{code-cell} ipython3 tau_post_mean = float(idata_partial.posterior["tau"].mean()) @@ -622,7 +590,24 @@ power_prior_df = pd.DataFrame( power_prior_df ``` -The power prior borrows the precision of the pooled mean and treats every market as one draw from a single shared effect. A genuinely new market also carries between-market variation, which the hierarchical predictive folds in through $\tau$. The figure sets the two side by side. +The power prior must be measured against the belief the hierarchical model already holds about a market it has not seen. That belief is the model's *predictive distribution*, and it comes in two forms. The true-effect predictive gives the unknown effect of a new market drawn from the same population, + +$$\theta_{\text{new}} \mid \text{data} \sim \mathcal{N}\!\left(\mu_{\text{post}},\ \sqrt{\tau_{\text{post}}^2 + \sigma_{\mu,\text{post}}^2}\right),$$ + +and the observation predictive layers experimental noise on top, + +$$\hat d_{\text{new}} \mid \text{data} \sim \theta_{\text{new}} + \mathcal{N}(0, s_{\text{new}}).$$ + +We draw both now. The first sets the width the power prior is chasing; the second is the yardstick for the conflict check below. The power prior borrows the precision of the pooled mean and treats every market as one draw from a single shared effect, while the hierarchical predictive folds the between-market variation back in through $\tau$. The figure sets the two side by side. + +```{code-cell} ipython3 +mu_samples = idata_partial.posterior["mu"].values.flatten() +tau_samples = idata_partial.posterior["tau"].values.flatten() +rng_pp = np.random.default_rng(RANDOM_SEED + 1) +theta_new_samples = rng_pp.normal(mu_samples, tau_samples) +s_new = np.median(se_obs) +d_hat_new_samples = theta_new_samples + rng_pp.normal(0.0, s_new, size=len(theta_new_samples)) +``` ```{code-cell} ipython3 theta_new_mean = float(theta_new_samples.mean()) @@ -685,6 +670,51 @@ The concordant market sits in the body of the predictive and the tail probabilit +++ +## Predicting the next experiment + +The predictive we drew to calibrate borrowing also answers the question that occasioned the whole exercise: what will happen if we run the redesign in a market we have not tested yet. The two flavours now do decision work. The true-effect predictive $\theta_{\text{new}}$ is the best estimate of the effect in a new market; the observation predictive $\hat d_{\text{new}}$ is what to expect the next experiment to actually return, noise included. The gap between them is the experimental-noise envelope that a single-market estimate conflates with population variation. + +```{code-cell} ipython3 +dt = az.from_dict( + { + "posterior": { + "theta_new": theta_new_samples, + "d_hat_new": d_hat_new_samples, + } + }, + sample_dims=["sample"], +) + +pc = az.plot_dist( + dt, + kind="hist", + sample_dims=["sample"], + cols=[], + aes={"color": ["__variable__"]}, + visuals={ + "point_estimate": False, + "point_estimate_text": False, + "credible_interval": False, + "title": {"text": "Two predictive distributions for the ninth market"}, + }, +) +pc.add_legend("__variable__") +az.add_lines(pc, values=0.0, visuals={"ref_line": {"color": "black", "label": "0"}}) +``` + +```{code-cell} ipython3 +prob_theta_new_positive = float((theta_new_samples > 0).mean()) +prob_d_hat_new_positive = float((d_hat_new_samples > 0).mean()) +pd.DataFrame( + {"posterior probability > 0": [prob_theta_new_positive, prob_d_hat_new_positive]}, + index=[r"$\theta_{\text{new}}$ (true effect)", r"$\hat d_{\text{new}}$ (observed estimate)"], +).round(3) +``` + +The probability the true effect is positive in the next market is higher than the probability the next experiment will return a positive estimate. The gap is the experimental-noise tax: each individual experiment is a noisy realisation of an underlying truth, and the team will sometimes see a negative estimate even when the true effect is positive. Reporting the meta-analytic posterior on $\theta_{\text{new}}$ as the planning input for the next market, which {ref}`assurance_planning` then consumes as its prior, is the way to feed accumulating evidence forward without losing track of the noise. + ++++ + ## Replication as the data-generating process A single experiment is overheard speech. Eight experiments are conversation. The hierarchical model is what lets us hear them as conversation, and the posterior on the population describes the very population our experiments were drawn from.