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--- a/examples/causal_inference/counterfactuals_do_operator.ipynb
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@@ -1,2931 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "domestic-remove",
-   "metadata": {},
-   "source": [
-    "(counterfactuals_do_operator)=\n",
-    "# Counterfactual generation using pymc do-operator\n",
-    "\n",
-    ":::{post} August, 2023\n",
-    ":tags: causality, causal inference, do-operator, counterfactuals\n",
-    ":category: beginner, reference\n",
-    ":author: Shekhar Khandelwal\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "elect-softball",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "import arviz.preview as az\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import pymc as pm\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "level-balance",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%config InlineBackend.figure_format = 'retina'  # high resolution figures\n",
-    "az.style.use(\"arviz-variat\")\n",
-    "rng = np.random.default_rng(42)\n",
-    "SEED = 8927"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sapphire-yellow",
-   "metadata": {},
-   "source": [
-    "# Introduction\n",
-    "\n",
-    "In the realm of data science and analytics, understanding the causal relationships between variables is paramount. While traditional statistical methods have provided insights into these relationships, the advent of probabilistic programming has ushered in a new era of causal analysis. In this article, we will explore the power of counterfactuals in causal analysis using the PyMC framework, with a special focus on the “do-operator.”\n",
-    "Counterfactuals are essentially “what-if” scenarios that allow us to understand the potential outcomes had a different action been taken or a different condition been present. By leveraging the PyMC framework and its “do-operator,” we can programmatically simulate these scenarios, giving us a deeper understanding of the relationships between predictors and target variables.\n",
-    "\n",
-    "Through a step-by-step guide, we will delve into the process of building a PyMC model skeleton, generating data using the do-operator, and validating the relationships captured by the model. Furthermore, we will explore the magic of the do-operator in simulating different ‘what-if’ scenarios, akin to programmatic A/B testing.\n",
-    "\n",
-    "- Step 1. Build a pymc model skeleton\n",
-    "- Step 2. Use model skeleton and generate data using do-operator to infuse relationship between predictors and target variable (ssshhh, that’s a hidden superhero feature of do-operator ;) )\n",
-    "- Step 3. Use observe-operator to assign generated data on the model skeleton\n",
-    "- Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples (isn’t that what we expect a predictive model to do ;) )\n",
-    "- Step 5. Use do-operator to time travel, and generate target variable with different ‘what-if’ scenarios (basically mimic A/B testing…programatically)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3756a0d-447e-4ffd-8305-e0f2329dbc3a",
-   "metadata": {},
-   "source": [
-    "### Step 1. Build a pymc model skeleton\n",
-    "\n",
-    "For this demo, we are building a very simple Linear Regression model.\n",
-    "- Predictor — ‘a’, ‘b’, ‘c’\n",
-    "- Target Variable — ‘y’\n",
-    "- Coefficients —\n",
-    ">- ‘beta_ay’ -> coefficient of |a|\n",
-    ">- ‘beta_by’ -> coefficient of |b|\n",
-    ">- ‘beta_cy’ -> coefficient of |c|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "21e66b38",
-   "metadata": {},
-   "outputs": [
-    {
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-     },
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-   "source": [
-    "with pm.Model(coords={\"i\": [0]}) as model_generative:\n",
-    "    # priors\n",
-    "    beta_y0 = pm.Normal(\"beta_y0\")\n",
-    "    beta_ay = pm.Normal(\"beta_ay\")\n",
-    "    beta_by = pm.Normal(\"beta_by\")\n",
-    "    beta_cy = pm.Normal(\"beta_cy\")\n",
-    "    # observation noise on Y\n",
-    "    sigma_y = pm.HalfNormal(\"sigma_y\")\n",
-    "    # core nodes and causal relationships\n",
-    "    a = pm.Normal(\"a\", mu=0, sigma=1, dims=\"i\")\n",
-    "    b = pm.Normal(\"b\", mu=0, sigma=1, dims=\"i\")\n",
-    "    c = pm.Normal(\"c\", mu=0, sigma=1, dims=\"i\")\n",
-    "    y_mu = pm.Deterministic(\n",
-    "        \"y_mu\", beta_y0 + (beta_ay * a) + (beta_by * b) + (beta_cy * c), dims=\"i\"\n",
-    "    )\n",
-    "    y = pm.Normal(\"y\", mu=y_mu, sigma=sigma_y, dims=\"i\")\n",
-    "\n",
-    "\n",
-    "pm.model_to_graphviz(model_generative)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2320755-54f5-4051-b73e-feb60de8b783",
-   "metadata": {},
-   "source": [
-    "### Step 2. Use model skeleton and generate data using do-operator to infuse relationship between predictors and target variable. We will use this generated data for modelling later.\n",
-    "\n",
-    "Let’s first define the predictors relationship with target variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "62d01fc3-9a12-4dcd-b2f1-d7a09116a3c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "true_values = {\"beta_ay\": 1.5, \"beta_by\": 0.7, \"beta_cy\": 0.3, \"sigma_y\": 0.2, \"beta_y0\": 0.0}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4bad6441-6921-446b-82a1-6a995e74faff",
-   "metadata": {},
-   "source": [
-    "Basically what we are saying here is, we are intentionally defining the coefficient values, which we expect predictive model to predict later on.\n",
-    "\n",
-    "Now the magic begins. We will use do-operator to use this dictionary and sample data variables. How do we do this ? Simple by passing two arguments to pymc do-operator. First, the model skeleton object. And second, the coefficient dictionary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fbf04aa4-e68f-43fd-ba70-91a287b6b12d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_simulate = pm.do(model_generative, true_values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a149f565-ccb3-4118-ac5a-da67733e3e5a",
-   "metadata": {},
-   "source": [
-    "This will create a new model object with the coefficent variables values infused. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "113da0d7-b9d7-4cd2-98fa-7fa794169b94",
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-    "The gray shades on the coefficient variables depicts the tale. Check the previous model graph, it was all white.\n",
-    "\n",
-    "Now, all we have to do is generate samples, the known pymc way.\n",
-    "\n",
-    "Lets generate 100 samples."
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-   "execution_count": 7,
-   "id": "2c651c0a-29f8-4669-baf7-986687f59317",
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-     "text": [
-      "Sampling: [a, b, c, y]\n"
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-    "N = 100\n",
-    "\n",
-    "with model_simulate:\n",
-    "    simulate = pm.sample_prior_predictive(samples=N)"
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-   "cell_type": "markdown",
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-   "execution_count": 8,
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-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
-       "Dimensions:  (chain: 1, draw: 100, i: 1)\n",
-       "Coordinates:\n",
-       "  * chain    (chain) int64 8B 0\n",
-       "  * draw     (draw) int64 800B 0 1 2 3 4 5 6 7 8 ... 91 92 93 94 95 96 97 98 99\n",
-       "  * i        (i) int64 8B 0\n",
-       "Data variables:\n",
-       "    b        (chain, draw, i) float64 800B 0.9658 0.04536 ... -0.08396 0.5739\n",
-       "    y        (chain, draw, i) float64 800B 0.6151 -1.398 ... -0.2965 0.7868\n",
-       "    y_mu     (chain, draw, i) float64 800B 0.4538 -1.286 1.129 ... -0.4846 0.924\n",
-       "    c        (chain, draw, i) float64 800B -1.584 -1.12 ... -1.041 -0.3577\n",
-       "    a        (chain, draw, i) float64 800B 0.1686 -0.6548 ... -0.07567 0.4198\n",
-       "Attributes:\n",
-       "    created_at:                 2026-02-12T08:05:25.902369+00:00\n",
-       "    arviz_version:              0.23.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.27.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-76e8141e-b95e-4272-8fa2-e854b47fc7ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-76e8141e-b95e-4272-8fa2-e854b47fc7ed' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 100</li><li><span class='xr-has-index'>i</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-941797a4-4b20-4505-aa9d-ca543db7dc6e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-941797a4-4b20-4505-aa9d-ca543db7dc6e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-a61402a9-f52f-468a-a68e-7fc3121477f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a61402a9-f52f-468a-a68e-7fc3121477f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1828df2e-59f0-434b-9cd9-c17f6fbabcdf' class='xr-var-data-in' type='checkbox'><label for='data-1828df2e-59f0-434b-9cd9-c17f6fbabcdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 94 95 96 97 98 99</div><input id='attrs-04632e8c-0894-4e20-ba1e-dfb3e4a70120' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04632e8c-0894-4e20-ba1e-dfb3e4a70120' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-446a9d1e-add5-445a-945b-8c3ba0bcb113' class='xr-var-data-in' type='checkbox'><label for='data-446a9d1e-add5-445a-945b-8c3ba0bcb113' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
-       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-5eb9d83f-abf8-42d1-ad6d-ad8df994b3d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5eb9d83f-abf8-42d1-ad6d-ad8df994b3d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8261c1c3-4d20-4f2c-87cc-a06ad76e0067' class='xr-var-data-in' type='checkbox'><label for='data-8261c1c3-4d20-4f2c-87cc-a06ad76e0067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c63c301-54a5-496d-9156-cef70d328b25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c63c301-54a5-496d-9156-cef70d328b25' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>b</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9658 0.04536 ... -0.08396 0.5739</div><input id='attrs-d7c65c3a-6208-43f9-81f7-ffac7d82b406' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7c65c3a-6208-43f9-81f7-ffac7d82b406' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb1022ec-7a7b-4120-bb39-12623f642969' class='xr-var-data-in' type='checkbox'><label for='data-cb1022ec-7a7b-4120-bb39-12623f642969' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.96578864],\n",
-       "        [ 0.04535678],\n",
-       "        [ 0.95512263],\n",
-       "        [-0.62905488],\n",
-       "        [ 0.04620517],\n",
-       "        [-0.56234234],\n",
-       "        [ 0.51485273],\n",
-       "        [-0.90325592],\n",
-       "        [-1.9031824 ],\n",
-       "        [-0.3701944 ],\n",
-       "        [ 2.56117018],\n",
-       "        [-1.0951665 ],\n",
-       "        [ 0.88999605],\n",
-       "        [-1.90940323],\n",
-       "        [ 0.79304454],\n",
-       "        [ 0.67633095],\n",
-       "        [-0.38085012],\n",
-       "        [ 0.18737293],\n",
-       "        [ 0.71978757],\n",
-       "        [ 1.04918112],\n",
-       "...\n",
-       "        [ 0.93531183],\n",
-       "        [-0.72078953],\n",
-       "        [ 0.36932017],\n",
-       "        [-0.61813624],\n",
-       "        [ 1.31269031],\n",
-       "        [ 0.89062412],\n",
-       "        [-0.26376773],\n",
-       "        [-1.14830568],\n",
-       "        [-0.77270192],\n",
-       "        [ 1.35784906],\n",
-       "        [ 1.10280322],\n",
-       "        [-0.08834695],\n",
-       "        [ 0.02066176],\n",
-       "        [-0.43220345],\n",
-       "        [ 0.74985938],\n",
-       "        [ 0.55224686],\n",
-       "        [ 1.30682748],\n",
-       "        [ 0.8553943 ],\n",
-       "        [-0.08396438],\n",
-       "        [ 0.57387823]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6151 -1.398 ... -0.2965 0.7868</div><input id='attrs-8a281ea5-56ac-4c92-978f-20ca54d3684d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a281ea5-56ac-4c92-978f-20ca54d3684d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-695405f0-3619-4012-8e9b-fa896ef28e98' class='xr-var-data-in' type='checkbox'><label for='data-695405f0-3619-4012-8e9b-fa896ef28e98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.6151373 ],\n",
-       "        [-1.39761735],\n",
-       "        [ 0.93963576],\n",
-       "        [-1.48293002],\n",
-       "        [-1.00315284],\n",
-       "        [-0.41158656],\n",
-       "        [-0.50682793],\n",
-       "        [-1.88013435],\n",
-       "        [-2.151     ],\n",
-       "        [-1.19785714],\n",
-       "        [ 0.40304857],\n",
-       "        [-1.01630605],\n",
-       "        [ 1.12681415],\n",
-       "        [-2.74493423],\n",
-       "        [ 1.54512657],\n",
-       "        [ 0.8933305 ],\n",
-       "        [ 0.88110903],\n",
-       "        [-1.34668909],\n",
-       "        [ 2.18767082],\n",
-       "        [-0.00553468],\n",
-       "...\n",
-       "        [-0.18698384],\n",
-       "        [-0.59998581],\n",
-       "        [-0.17950389],\n",
-       "        [-0.44244282],\n",
-       "        [ 0.79841001],\n",
-       "        [-0.35196212],\n",
-       "        [ 2.30331236],\n",
-       "        [-2.47852173],\n",
-       "        [-1.07201524],\n",
-       "        [ 0.27864594],\n",
-       "        [-1.10143203],\n",
-       "        [ 1.91914669],\n",
-       "        [-1.21508136],\n",
-       "        [ 1.29046819],\n",
-       "        [ 0.1013738 ],\n",
-       "        [ 2.5054925 ],\n",
-       "        [ 1.63845801],\n",
-       "        [-1.36558804],\n",
-       "        [-0.29648284],\n",
-       "        [ 0.78679256]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_mu</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4538 -1.286 ... -0.4846 0.924</div><input id='attrs-f56d39c0-a9c2-4663-b0f8-ab187f77b6dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f56d39c0-a9c2-4663-b0f8-ab187f77b6dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd94496d-d6c3-4cef-b5b0-a61fd8a32508' class='xr-var-data-in' type='checkbox'><label for='data-fd94496d-d6c3-4cef-b5b0-a61fd8a32508' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.45380138],\n",
-       "        [-1.28636493],\n",
-       "        [ 1.12940314],\n",
-       "        [-1.41486841],\n",
-       "        [-0.87527127],\n",
-       "        [-0.22175704],\n",
-       "        [-0.28950416],\n",
-       "        [-2.22785197],\n",
-       "        [-1.87077867],\n",
-       "        [-1.21006829],\n",
-       "        [ 0.35197582],\n",
-       "        [-1.02183924],\n",
-       "        [ 1.43349117],\n",
-       "        [-2.51737718],\n",
-       "        [ 1.75679151],\n",
-       "        [ 0.9751541 ],\n",
-       "        [ 0.76208179],\n",
-       "        [-1.22892018],\n",
-       "        [ 1.92070399],\n",
-       "        [-0.19078556],\n",
-       "...\n",
-       "        [-0.22227173],\n",
-       "        [-0.72427292],\n",
-       "        [-0.03139603],\n",
-       "        [-0.36904346],\n",
-       "        [ 0.9760599 ],\n",
-       "        [-0.09790939],\n",
-       "        [ 2.32826833],\n",
-       "        [-2.82680566],\n",
-       "        [-1.10028364],\n",
-       "        [ 0.41033171],\n",
-       "        [-1.12297415],\n",
-       "        [ 1.55488462],\n",
-       "        [-1.10602734],\n",
-       "        [ 1.29506175],\n",
-       "        [ 0.31672473],\n",
-       "        [ 2.31618457],\n",
-       "        [ 1.74859019],\n",
-       "        [-1.41251561],\n",
-       "        [-0.48456193],\n",
-       "        [ 0.92403139]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.584 -1.12 ... -1.041 -0.3577</div><input id='attrs-f7a54b7d-6943-44ec-b8f8-0c487b51fb02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7a54b7d-6943-44ec-b8f8-0c487b51fb02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d45ceecf-6946-4e93-a2a6-0ee8b1597ccd' class='xr-var-data-in' type='checkbox'><label for='data-d45ceecf-6946-4e93-a2a6-0ee8b1597ccd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.58388113],\n",
-       "        [-1.11963379],\n",
-       "        [-0.11525159],\n",
-       "        [ 1.35029781],\n",
-       "        [-0.38788934],\n",
-       "        [-0.03411288],\n",
-       "        [-0.01839664],\n",
-       "        [-1.56730884],\n",
-       "        [ 0.42571462],\n",
-       "        [-1.30603563],\n",
-       "        [ 0.27856971],\n",
-       "        [-1.10266914],\n",
-       "        [ 0.07983902],\n",
-       "        [-1.01822647],\n",
-       "        [ 1.25788247],\n",
-       "        [-0.17689302],\n",
-       "        [-0.69244113],\n",
-       "        [-0.45133353],\n",
-       "        [ 0.35457389],\n",
-       "        [ 1.77393419],\n",
-       "...\n",
-       "        [-3.25192101],\n",
-       "        [ 0.89924804],\n",
-       "        [-0.55637451],\n",
-       "        [-0.01344661],\n",
-       "        [-0.36075952],\n",
-       "        [ 0.28044401],\n",
-       "        [ 2.29238651],\n",
-       "        [ 0.36527649],\n",
-       "        [ 1.05337441],\n",
-       "        [-0.13405649],\n",
-       "        [ 0.11310538],\n",
-       "        [ 0.63243539],\n",
-       "        [-0.42712372],\n",
-       "        [-1.36933077],\n",
-       "        [ 0.29535891],\n",
-       "        [ 0.92955522],\n",
-       "        [ 0.80499083],\n",
-       "        [-1.53661015],\n",
-       "        [-1.04094539],\n",
-       "        [-0.3577285 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1686 -0.6548 ... -0.07567 0.4198</div><input id='attrs-9a249447-d470-444f-acf3-72e42c04a2fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a249447-d470-444f-acf3-72e42c04a2fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fed8b55d-8fa4-4dd5-8846-e92ca9d9b2c0' class='xr-var-data-in' type='checkbox'><label for='data-fed8b55d-8fa4-4dd5-8846-e92ca9d9b2c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.68609116e-01],\n",
-       "        [-6.54816356e-01],\n",
-       "        [ 3.30261852e-01],\n",
-       "        [-9.19746228e-01],\n",
-       "        [-5.27498728e-01],\n",
-       "        [ 1.21410975e-01],\n",
-       "        [-4.29588053e-01],\n",
-       "        [-7.50253450e-01],\n",
-       "        [-4.44176919e-01],\n",
-       "        [-3.72747684e-01],\n",
-       "        [-1.01627615e+00],\n",
-       "        [ 5.03853697e-02],\n",
-       "        [ 5.24361483e-01],\n",
-       "        [-5.83551315e-01],\n",
-       "        [ 5.49530389e-01],\n",
-       "        [ 3.69860231e-01],\n",
-       "        [ 8.24272810e-01],\n",
-       "        [-8.16454113e-01],\n",
-       "        [ 8.73653679e-01],\n",
-       "        [-9.71595072e-01],\n",
-       "...\n",
-       "        [ 6.57241931e-02],\n",
-       "        [-3.26329772e-01],\n",
-       "        [-8.20052027e-02],\n",
-       "        [ 4.51239273e-02],\n",
-       "        [ 1.10269693e-01],\n",
-       "        [-5.36986321e-01],\n",
-       "        [ 1.21679319e+00],\n",
-       "        [-1.42171643e+00],\n",
-       "        [-5.83603083e-01],\n",
-       "        [-3.33297120e-01],\n",
-       "        [-1.28591201e+00],\n",
-       "        [ 9.51331246e-01],\n",
-       "        [-6.61568975e-01],\n",
-       "        [ 1.33893560e+00],\n",
-       "        [-1.97856337e-01],\n",
-       "        [ 1.10049680e+00],\n",
-       "        [ 3.94875802e-01],\n",
-       "        [-1.03353905e+00],\n",
-       "        [-7.56688324e-02],\n",
-       "        [ 4.19756783e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa2aa9b6-f76b-4bfa-8086-80a506244d19' class='xr-section-summary-in' type='checkbox'  ><label for='section-aa2aa9b6-f76b-4bfa-8086-80a506244d19' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-839da9c5-5911-40f5-9367-53c89905471d' class='xr-index-data-in' type='checkbox'/><label for='index-839da9c5-5911-40f5-9367-53c89905471d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ac0cd00b-9fcf-41ff-9fae-ee738085c177' class='xr-index-data-in' type='checkbox'/><label for='index-ac0cd00b-9fcf-41ff-9fae-ee738085c177' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
-       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f3dfffdc-840c-4b54-bf9c-8487ee1e5d33' class='xr-index-data-in' type='checkbox'/><label for='index-f3dfffdc-840c-4b54-bf9c-8487ee1e5d33' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;i&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-878e5842-f8d7-413c-b085-f302035da6a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-878e5842-f8d7-413c-b085-f302035da6a3' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2026-02-12T08:05:25.902369+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.23.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.27.1</dd></dl></div></li></ul></div></div><br></div>\n",
-       "                      </ul>\n",
-       "                  </div>\n",
-       "            </li>\n",
-       "            \n",
-       "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_constant_data1570021a-751a-4997-9042-acd7e741d916\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_constant_data1570021a-751a-4997-9042-acd7e741d916\" class = \"xr-section-summary\">constant_data</label>\n",
-       "                  <div class=\"xr-section-inline-details\"></div>\n",
-       "                  <div class=\"xr-section-details\">\n",
-       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
-       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1f1f1f;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 40B\n",
-       "Dimensions:  ()\n",
-       "Data variables:\n",
-       "    beta_cy  float64 8B 0.3\n",
-       "    beta_by  float64 8B 0.7\n",
-       "    beta_ay  float64 8B 1.5\n",
-       "    beta_y0  float64 8B 0.0\n",
-       "    sigma_y  float64 8B 0.2\n",
-       "Attributes:\n",
-       "    created_at:                 2026-02-12T08:05:25.907365+00:00\n",
-       "    arviz_version:              0.23.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.27.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-70d21b12-a46d-47ef-85c2-de013ff79c5b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70d21b12-a46d-47ef-85c2-de013ff79c5b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-047ce436-7839-4855-9b9e-faf77bf36a1c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-047ce436-7839-4855-9b9e-faf77bf36a1c' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-27a8ac79-a1f3-4620-a89e-bc3cf1329586' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27a8ac79-a1f3-4620-a89e-bc3cf1329586' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_cy</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3</div><input id='attrs-877970f4-5203-4e11-afc8-67e8d7349e1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-877970f4-5203-4e11-afc8-67e8d7349e1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc44f82b-5f1c-4874-ad71-c8e54c217542' class='xr-var-data-in' type='checkbox'><label for='data-dc44f82b-5f1c-4874-ad71-c8e54c217542' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.3)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_by</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7</div><input id='attrs-67743fbc-4159-4923-82f9-f544619e5934' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67743fbc-4159-4923-82f9-f544619e5934' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c16e873-d840-4745-aee6-88af468d863c' class='xr-var-data-in' type='checkbox'><label for='data-0c16e873-d840-4745-aee6-88af468d863c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.7)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_ay</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-d6e9f2dc-ba8b-42a3-a70c-f7696713ba61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6e9f2dc-ba8b-42a3-a70c-f7696713ba61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc478221-c9c0-4a8b-ba99-0bb1a8808f55' class='xr-var-data-in' type='checkbox'><label for='data-fc478221-c9c0-4a8b-ba99-0bb1a8808f55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_y0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-a4d2155e-d119-4e74-87e0-2366266194b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4d2155e-d119-4e74-87e0-2366266194b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-220e018d-532b-467e-aa02-f8fe00a9b620' class='xr-var-data-in' type='checkbox'><label for='data-220e018d-532b-467e-aa02-f8fe00a9b620' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2</div><input id='attrs-ab66b985-fade-4a98-acd0-a959d3537afe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab66b985-fade-4a98-acd0-a959d3537afe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4db3d72-6743-43f4-a945-0e39a4f6fc13' class='xr-var-data-in' type='checkbox'><label for='data-e4db3d72-6743-43f4-a945-0e39a4f6fc13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.2)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73af573e-ede7-4398-8591-c6c4e5070995' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-73af573e-ede7-4398-8591-c6c4e5070995' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b24beb7f-1601-40c1-8154-36e95b091574' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b24beb7f-1601-40c1-8154-36e95b091574' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2026-02-12T08:05:25.907365+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.23.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.27.1</dd></dl></div></li></ul></div></div><br></div>\n",
-       "                      </ul>\n",
-       "                  </div>\n",
-       "            </li>\n",
-       "            \n",
-       "              </ul>\n",
-       "            </div>\n",
-       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-sections.group-sections {\n",
-       "  grid-template-columns: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
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-      "text/plain": [
-       "Inference data with groups:\n",
-       "\t> prior\n",
-       "\t> constant_data"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simulate"
-   ]
-  },
-  {
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-   "id": "9cc7caf3-61d7-47ac-a207-fa22778a9f2a",
-   "metadata": {},
-   "source": [
-    "Extract the sampled prior data into a pandas dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "86e38344-28ad-4e0d-a987-4385ed320571",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 4)\n"
-     ]
-    },
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-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>y</th>\n",
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-       "  <tbody>\n",
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-       "      <th>0</th>\n",
-       "      <td>0.168609</td>\n",
-       "      <td>0.965789</td>\n",
-       "      <td>-1.583881</td>\n",
-       "      <td>0.615137</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.654816</td>\n",
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-       "      <td>-1.397617</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.330262</td>\n",
-       "      <td>0.955123</td>\n",
-       "      <td>-0.115252</td>\n",
-       "      <td>0.939636</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.919746</td>\n",
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-       "      <th>4</th>\n",
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-      "text/plain": [
-       "          a         b         c         y\n",
-       "0  0.168609  0.965789 -1.583881  0.615137\n",
-       "1 -0.654816  0.045357 -1.119634 -1.397617\n",
-       "2  0.330262  0.955123 -0.115252  0.939636\n",
-       "3 -0.919746 -0.629055  1.350298 -1.482930\n",
-       "4 -0.527499  0.046205 -0.387889 -1.003153"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "observed = {\n",
-    "    \"a\": simulate.prior[\"a\"].values.flatten(),\n",
-    "    \"b\": simulate.prior[\"b\"].values.flatten(),\n",
-    "    \"c\": simulate.prior[\"c\"].values.flatten(),\n",
-    "    \"y\": simulate.prior[\"y\"].values.flatten(),\n",
-    "}\n",
-    "\n",
-    "df = pd.DataFrame(observed)\n",
-    "print(df.shape)\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "410b2941-ee10-444b-ab5b-36f229a6dba7",
-   "metadata": {},
-   "source": [
-    "Ok, so now we are all set with a sample data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae564cd5-23e4-4225-9ee8-e642ae47eeb7",
-   "metadata": {},
-   "source": [
-    "### Step 3. Use observe-operator to assign generated data on the model skeleton\n",
-    "\n",
-    "Now, this is another cool feature of pymc newly introduced observe method. Observe method, takes in a model skeleton and the dictionary with the data for the variables we want to infuse into the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "17860fac-d25b-46bf-b4d7-8a920610a853",
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-       "<path fill=\"none\" stroke=\"black\" d=\"M629.24,-286.93C622.94,-282.31 616.06,-278.07 609,-275.13 484.22,-223.17 432.73,-288.78 307,-239.13 303.09,-237.59 299.21,-235.69 295.43,-233.57\"/>\n",
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-       "</g>\n",
-       "<!-- sigma_y -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>sigma_y</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"372\" cy=\"-198.48\" rx=\"55.86\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"372\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">sigma_y</text>\n",
-       "<text text-anchor=\"middle\" x=\"372\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"372\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
-       "</g>\n",
-       "<!-- sigma_y&#45;&gt;y -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>sigma_y&#45;&gt;y</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M339.41,-165.35C324.69,-150.85 307.23,-133.64 292.02,-118.64\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"294.49,-116.16 284.91,-111.64 289.57,-121.15 294.49,-116.16\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>\n"
-      ],
-      "text/plain": [
-       "<graphviz.graphs.Digraph at 0x77a431602780>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_dict = {\"a\": df[\"a\"], \"b\": df[\"b\"], \"c\": df[\"c\"], \"y\": df[\"y\"]}\n",
-    "model_inference = pm.observe(model_generative, data_dict)\n",
-    "model_inference.set_dim(\"i\", N, coord_values=np.arange(N))\n",
-    "pm.model_to_graphviz(model_inference)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ad86c3a-ca67-406c-b114-1f5449b354a1",
-   "metadata": {},
-   "source": [
-    "See the gray matter again. This time we have observed data infused into the model, and we have to sample for the coefficient and other parameters.\n",
-    "\n",
-    "So, lets sample.\n",
-    "\n",
-    "### Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "6ef56be2-06a9-49b8-8044-e789f1d254e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [beta_cy, beta_by, beta_ay, beta_y0, sigma_y]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80ae42f1e35b4b2eb649124525109d25",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with model_inference:\n",
-    "    idata = pm.sample(random_seed=SEED)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "375287a3-068a-4815-b86b-f472676a5416",
-   "metadata": {},
-   "source": [
-    "Now, lets validate if model captured the infused coefficient values in the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "4a37b112",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2880x837.092 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 860,
-       "width": 2903
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pc = az.plot_dist(\n",
-    "    idata,\n",
-    "    var_names=list(true_values.keys()),\n",
-    ")\n",
-    "az.add_lines(pc, true_values);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c89e6a59-db38-499e-a6ac-73a53a4fca19",
-   "metadata": {},
-   "source": [
-    "BAM ! Pretty nice fit !\n",
-    "\n",
-    "Now, lets do what we are supposed to do ! Counterfactuals.\n",
-    "\n",
-    "Basically, this is about generating target variable values with different predictor values. Basically, answering what if questions !\n",
-    "\n",
-    "_What-if there was all ‘b’ values as 0 ?_\n",
-    "\n",
-    "_What-if all ‘b’ values were double ?_\n",
-    "\n",
-    "How to do this ? Here you go..\n",
-    "\n",
-    "### Step 5. Use do-operator to time travel, and generate target variable with different ‘what-if’ scenarios.\n",
-    "Since, we want to experiment with ‘b’, lets first assign observed values to ‘a’ and ‘c’. Not to ‘y’, because that’s what we want to sample. Correct !"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c0643453-0bb6-4ceb-9fb1-3adf57f031b9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_counterfactual = pm.do(model_inference, {\"a\": df[\"a\"], \"c\": df[\"c\"]})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e8067a3-6430-4cf9-b53e-a5bfbd8e4488",
-   "metadata": {},
-   "source": [
-    "Now, lets begin the fun part. Let’s generate counterfactuals.\n",
-    "\n",
-    "### _Scenario 1 :- What if all values for ‘b’ were 0 ?_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "f7cbeba3-d047-447f-bfe2-077604fd2fd9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_b0 = pm.do(model_counterfactual, {\"b\": np.zeros(N, dtype=\"int32\")}, prune_vars=True)\n",
-    "model_b1 = pm.do(model_counterfactual, {\"b\": df[\"b\"]}, prune_vars=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3b23275-029d-40a2-8603-796c740bed29",
-   "metadata": {},
-   "source": [
-    "Just sample."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "edd9b175-5a4b-457a-b9d9-560251733600",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: []\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "00f97e61c0844beea9faabcf53bc8a4c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: []\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "da4d917410444298ac97c020bc6d0f28",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Sample when 'b' was 0: P(y | (a,c), do(b=0))\n",
-    "idata_b0 = pm.sample_posterior_predictive(\n",
-    "    idata,\n",
-    "    model=model_b0,\n",
-    "    predictions=True,\n",
-    "    var_names=[\"y_mu\"],\n",
-    "    random_seed=SEED,\n",
-    ")\n",
-    "# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))\n",
-    "idata_b1 = pm.sample_posterior_predictive(\n",
-    "    idata,\n",
-    "    model=model_b1,\n",
-    "    predictions=True,\n",
-    "    var_names=[\"y_mu\"],\n",
-    "    random_seed=SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6cabe3d1-ecab-4bb8-bcea-d45e91ca0b04",
-   "metadata": {},
-   "source": [
-    "Some basic python and here we have the counterfactuals."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "4d853c28-4029-4372-a46d-132239918fe5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>y</th>\n",
-       "      <th>b_scenario_1</th>\n",
-       "      <th>y_scenario_1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.168609</td>\n",
-       "      <td>0.965789</td>\n",
-       "      <td>-1.583881</td>\n",
-       "      <td>0.615137</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.257788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.654816</td>\n",
-       "      <td>0.045357</td>\n",
-       "      <td>-1.119634</td>\n",
-       "      <td>-1.397617</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-1.374868</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.330262</td>\n",
-       "      <td>0.955123</td>\n",
-       "      <td>-0.115252</td>\n",
-       "      <td>0.939636</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.473174</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.919746</td>\n",
-       "      <td>-0.629055</td>\n",
-       "      <td>1.350298</td>\n",
-       "      <td>-1.482930</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.973051</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.527499</td>\n",
-       "      <td>0.046205</td>\n",
-       "      <td>-0.387889</td>\n",
-       "      <td>-1.003153</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.938561</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          a         b         c         y  b_scenario_1  y_scenario_1\n",
-       "0  0.168609  0.965789 -1.583881  0.615137             0     -0.257788\n",
-       "1 -0.654816  0.045357 -1.119634 -1.397617             0     -1.374868\n",
-       "2  0.330262  0.955123 -0.115252  0.939636             0      0.473174\n",
-       "3 -0.919746 -0.629055  1.350298 -1.482930             0     -0.973051\n",
-       "4 -0.527499  0.046205 -0.387889 -1.003153             0     -0.938561"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"b_scenario_1\"] = 0\n",
-    "df[\"y_scenario_1\"] = (\n",
-    "    idata_b0.predictions.y_mu.mean((\"chain\", \"draw\")).values.reshape(1, -1).flatten()\n",
-    ")\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ad2d911-2948-4553-9322-121f064696e0",
-   "metadata": {},
-   "source": [
-    "### _Scenario 2: What if ‘b’ was 5 times as observed_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "1c22e18f-cb96-4ca9-93f4-e8323b1528b1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_b0 = pm.do(model_counterfactual, {\"b\": 5 * df[\"b\"]}, prune_vars=True)\n",
-    "model_b1 = pm.do(model_counterfactual, {\"b\": df[\"b\"]}, prune_vars=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5422848d-e6cd-4f3e-8641-640ed227ea78",
-   "metadata": {},
-   "source": [
-    "Sample."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "97990259-7cd9-4801-8af0-b8e3a46ffa7b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: []\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6db6f2d022124836a956bf459978ba98",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: []\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "277438af40b340ed956569886307eaa2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>y</th>\n",
-       "      <th>b_scenario_1</th>\n",
-       "      <th>y_scenario_1</th>\n",
-       "      <th>b_scenario_2</th>\n",
-       "      <th>y_scenario_2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.168609</td>\n",
-       "      <td>0.965789</td>\n",
-       "      <td>-1.583881</td>\n",
-       "      <td>0.615137</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.257788</td>\n",
-       "      <td>4.828943</td>\n",
-       "      <td>3.090282</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.654816</td>\n",
-       "      <td>0.045357</td>\n",
-       "      <td>-1.119634</td>\n",
-       "      <td>-1.397617</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-1.374868</td>\n",
-       "      <td>0.226784</td>\n",
-       "      <td>-1.217631</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.330262</td>\n",
-       "      <td>0.955123</td>\n",
-       "      <td>-0.115252</td>\n",
-       "      <td>0.939636</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.473174</td>\n",
-       "      <td>4.775613</td>\n",
-       "      <td>3.784268</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.919746</td>\n",
-       "      <td>-0.629055</td>\n",
-       "      <td>1.350298</td>\n",
-       "      <td>-1.482930</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.973051</td>\n",
-       "      <td>-3.145274</td>\n",
-       "      <td>-3.153777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.527499</td>\n",
-       "      <td>0.046205</td>\n",
-       "      <td>-0.387889</td>\n",
-       "      <td>-1.003153</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.938561</td>\n",
-       "      <td>0.231026</td>\n",
-       "      <td>-0.778383</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          a         b         c         y  b_scenario_1  y_scenario_1  \\\n",
-       "0  0.168609  0.965789 -1.583881  0.615137             0     -0.257788   \n",
-       "1 -0.654816  0.045357 -1.119634 -1.397617             0     -1.374868   \n",
-       "2  0.330262  0.955123 -0.115252  0.939636             0      0.473174   \n",
-       "3 -0.919746 -0.629055  1.350298 -1.482930             0     -0.973051   \n",
-       "4 -0.527499  0.046205 -0.387889 -1.003153             0     -0.938561   \n",
-       "\n",
-       "   b_scenario_2  y_scenario_2  \n",
-       "0      4.828943      3.090282  \n",
-       "1      0.226784     -1.217631  \n",
-       "2      4.775613      3.784268  \n",
-       "3     -3.145274     -3.153777  \n",
-       "4      0.231026     -0.778383  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Sample when 'b' was 5 times b: P(y | (a,c), do(b=5*b))\n",
-    "idata_b0 = pm.sample_posterior_predictive(\n",
-    "    idata,\n",
-    "    model=model_b0,\n",
-    "    predictions=True,\n",
-    "    var_names=[\"y_mu\"],\n",
-    "    random_seed=SEED,\n",
-    ")\n",
-    "# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))\n",
-    "idata_b1 = pm.sample_posterior_predictive(\n",
-    "    idata,\n",
-    "    model=model_b1,\n",
-    "    predictions=True,\n",
-    "    var_names=[\"y_mu\"],\n",
-    "    random_seed=SEED,\n",
-    ")\n",
-    "\n",
-    "df[\"b_scenario_2\"] = 5 * df[\"b\"]\n",
-    "df[\"y_scenario_2\"] = (\n",
-    "    idata_b0.predictions.y_mu.mean((\"chain\", \"draw\")).values.reshape(1, -1).flatten()\n",
-    ")\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2005665d-300b-4a01-91e4-a0901dbbaa97",
-   "metadata": {},
-   "source": [
-    "Ok, so now you got the idea. It's an open playground. Go back in time, change whatever you want to change, and see how output changes.\n",
-    "\n",
-    "This opens the door for many more possibilities in various use cases. Especially, Causal Analytics !"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b743d58b-2678-4e17-9947-a8fe4ed03e21",
-   "metadata": {},
-   "source": [
-    "## Authors\n",
-    "- Authored by [Shekhar Khandelwal](https://github.com/shekharkhandelwal1983) in August 2023\n",
-    "- Updated by Osvaldo Martin in February 2026 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "closed-frank",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    "https://medium.com/@khandelwal-shekhar/counterfactuals-for-causal-analysis-via-pymc-do-operator-234ba04e4e80\n",
-    "\n",
-    "https://www.pymc-labs.io/blog-posts/causal-analysis-with-pymc-answering-what-if-with-the-new-do-operator/"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0717070c-04aa-4836-ab95-6b3eff0dcaaf",
-   "metadata": {},
-   "source": [
-    "## Watermark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "sound-calculation",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Last updated: Thu, 12 Feb 2026\n",
-      "\n",
-      "Python implementation: CPython\n",
-      "Python version       : 3.13.11\n",
-      "IPython version      : 8.29.0\n",
-      "\n",
-      "pytensor: 2.37.0\n",
-      "\n",
-      "arviz : 0.23.1\n",
-      "numpy : 2.2.6\n",
-      "pandas: 2.2.3\n",
-      "pymc  : 5.27.1\n",
-      "\n",
-      "Watermark: 2.6.0\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p pytensor"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e4386fc-4de9-4535-a160-d929315633ef",
-   "metadata": {},
-   "source": [
-    ":::{include} ../page_footer.md\n",
-    ":::"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "eabm",
-   "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": "3.13.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/causal_inference/interventional_what_if_do_operator.ipynb b/examples/causal_inference/interventional_what_if_do_operator.ipynb
new file mode 100644
index 000000000..54fb9720f
--- /dev/null
+++ b/examples/causal_inference/interventional_what_if_do_operator.ipynb
@@ -0,0 +1,3219 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "domestic-remove",
+   "metadata": {},
+   "source": [
+    "(interventional_what_if_do_operator)=\n",
+    "# Interventional what-if analysis using the PyMC do-operator\n",
+    "\n",
+    ":::{post} August, 2023\n",
+    ":tags: causal inference, do-operator, interventional analysis\n",
+    ":category: beginner, reference\n",
+    ":author: Shekhar Khandelwal\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "elect-softball",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "import arviz as az\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pymc as pm\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "level-balance",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = \"retina\"\n",
+    "az.style.use(\"arviz-darkgrid\")\n",
+    "rng = np.random.default_rng(42)\n",
+    "SEED = 8927"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sapphire-yellow",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "In the realm of data science and analytics, understanding the causal relationships between variables is paramount. While traditional statistical methods have provided insights into these relationships, the advent of probabilistic programming has ushered in a new era of causal analysis. In this article, we will explore the power of interventional what-if analysis using the PyMC framework, with a special focus on the `do`-operator.\n",
+    "\n",
+    "The `do`-operator lets us answer interventional \"what-if\" questions: *What would happen to the outcome if we forced a variable to a specific value?* By leveraging `pm.do`, we can programmatically simulate these scenarios, giving us a deeper understanding of the causal relationships between predictors and target variables.\n",
+    "\n",
+    "Through a step-by-step guide, we will delve into the process of building a PyMC model skeleton, generating data using the `do`-operator, and validating the relationships captured by the model. Furthermore, we will explore how the `do`-operator can simulate different what-if scenarios, akin to programmatic A/B testing.\n",
+    "\n",
+    ":::{admonition} Interventions vs. counterfactuals: where does this notebook sit on the causal ladder?\n",
+    ":class: important\n",
+    "\n",
+    "Pearl's *causal ladder* distinguishes three levels of causal reasoning:\n",
+    "\n",
+    "- **L1 — Association (seeing)**: What do we observe? $P(Y \\mid X)$\n",
+    "- **L2 — Intervention (doing)**: What happens if we force a variable to a value? $P(Y \\mid \\operatorname{do}(X = x))$\n",
+    "- **L3 — Counterfactual (imagining)**: What *would have happened* to *this specific unit* under a different action, given what we already observed? $Y_x \\mid X = x', Y = y'$\n",
+    "\n",
+    "`pm.do` directly supports **L2 queries**. This notebook demonstrates interventional what-if analysis — we set a variable to a new value and predict the outcome. True unit-level counterfactuals (L3) require an additional **abduction** step: inferring unit-specific exogenous terms ($U$) from observed evidence before predicting in the intervened world. That step is not demonstrated here.\n",
+    "\n",
+    "For readers interested in L3 counterfactual reasoning, see Pearl, Glymour & Jewell (2016), *Causal Inference in Statistics: A Primer*, Sections 4.2.3–4.2.4.\n",
+    ":::\n",
+    "\n",
+    "- Step 1. Build a PyMC model skeleton\n",
+    "- Step 2. Use model skeleton and generate data using `do`-operator to infuse relationship between predictors and target variable\n",
+    "- Step 3. Use `observe`-operator to assign generated data on the model skeleton\n",
+    "- Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples\n",
+    "- Step 5. Use `do`-operator to generate target variable with different what-if scenarios (programmatic A/B testing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3756a0d-447e-4ffd-8305-e0f2329dbc3a",
+   "metadata": {},
+   "source": [
+    "### Step 1. Build a pymc model skeleton\n",
+    "\n",
+    "For this demo, we are building a very simple Linear Regression model.\n",
+    "- Predictor — ‘a’, ‘b’, ‘c’\n",
+    "- Target Variable — ‘y’\n",
+    "- Coefficients —\n",
+    ">- ‘beta_ay’ -> coefficient of |a|\n",
+    ">- ‘beta_by’ -> coefficient of |b|\n",
+    ">- ‘beta_cy’ -> coefficient of |c|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "21e66b38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.54\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- y_mu -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>y_mu</title>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"338.54\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- beta_y0&#45;&gt;y_mu -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>beta_y0&#45;&gt;y_mu</title>\n",
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+       "</g>\n",
+       "<!-- beta_cy -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>beta_cy</title>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"442.54\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta_cy</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"442.54\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_cy&#45;&gt;y_mu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>beta_cy&#45;&gt;y_mu</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"373.99,-233.55 364.69,-228.47 368.79,-238.24 373.99,-233.55\"/>\n",
+       "</g>\n",
+       "<!-- beta_by -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>beta_by</title>\n",
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+       "</g>\n",
+       "<!-- beta_by&#45;&gt;y_mu -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>beta_by&#45;&gt;y_mu</title>\n",
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+       "</g>\n",
+       "<!-- sigma_y -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>sigma_y</title>\n",
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+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>y</title>\n",
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+       "</g>\n",
+       "<!-- sigma_y&#45;&gt;y -->\n",
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+       "<title>sigma_y&#45;&gt;y</title>\n",
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+       "<!-- beta_ay -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>beta_ay</title>\n",
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+       "</g>\n",
+       "<!-- beta_ay&#45;&gt;y_mu -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>beta_ay&#45;&gt;y_mu</title>\n",
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+       "<!-- y_mu&#45;&gt;y -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>y_mu&#45;&gt;y</title>\n",
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+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>c</title>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"142.54\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"142.54\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- c&#45;&gt;y_mu -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>c&#45;&gt;y_mu</title>\n",
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+       "</g>\n",
+       "<!-- a -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"242.54\" cy=\"-315.79\" rx=\"41.01\" ry=\"40.66\"/>\n",
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+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>a&#45;&gt;y_mu</title>\n",
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+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>b</title>\n",
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+       "</g>\n",
+       "<!-- b&#45;&gt;y_mu -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>b&#45;&gt;y_mu</title>\n",
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+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x123b393a0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with pm.Model(coords={\"i\": [0]}) as model_generative:\n",
+    "    # priors\n",
+    "    beta_y0 = pm.Normal(\"beta_y0\")\n",
+    "    beta_ay = pm.Normal(\"beta_ay\")\n",
+    "    beta_by = pm.Normal(\"beta_by\")\n",
+    "    beta_cy = pm.Normal(\"beta_cy\")\n",
+    "    # observation noise on Y\n",
+    "    sigma_y = pm.HalfNormal(\"sigma_y\")\n",
+    "    # core nodes and causal relationships\n",
+    "    a = pm.Normal(\"a\", mu=0, sigma=1, dims=\"i\")\n",
+    "    b = pm.Normal(\"b\", mu=0, sigma=1, dims=\"i\")\n",
+    "    c = pm.Normal(\"c\", mu=0, sigma=1, dims=\"i\")\n",
+    "    y_mu = pm.Deterministic(\n",
+    "        \"y_mu\", beta_y0 + (beta_ay * a) + (beta_by * b) + (beta_cy * c), dims=\"i\"\n",
+    "    )\n",
+    "    y = pm.Normal(\"y\", mu=y_mu, sigma=sigma_y, dims=\"i\")\n",
+    "\n",
+    "\n",
+    "pm.model_to_graphviz(model_generative)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2320755-54f5-4051-b73e-feb60de8b783",
+   "metadata": {},
+   "source": [
+    "### Step 2. Use model skeleton and generate data using do-operator to infuse relationship between predictors and target variable. We will use this generated data for modelling later.\n",
+    "\n",
+    "Let’s first define the predictors relationship with target variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62d01fc3-9a12-4dcd-b2f1-d7a09116a3c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_values = {\n",
+    "    \"beta_ay\": 1.5,\n",
+    "    \"beta_by\": 0.7,\n",
+    "    \"beta_cy\": 0.3,\n",
+    "    \"sigma_y\": 0.2,\n",
+    "    \"beta_y0\": 0.0,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bad6441-6921-446b-82a1-6a995e74faff",
+   "metadata": {},
+   "source": [
+    "Basically what we are saying here is, we are intentionally defining the coefficient values, which we expect predictive model to predict later on.\n",
+    "\n",
+    "Now the magic begins. We will use do-operator to use this dictionary and sample data variables. How do we do this ? Simple by passing two arguments to pymc do-operator. First, the model skeleton object. And second, the coefficient dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fbf04aa4-e68f-43fd-ba70-91a287b6b12d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_simulate = pm.do(model_generative, true_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a149f565-ccb3-4118-ac5a-da67733e3e5a",
+   "metadata": {},
+   "source": [
+    "This will create a new model object with the coefficent variables values infused. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "113da0d7-b9d7-4cd2-98fa-7fa794169b94",
+   "metadata": {},
+   "outputs": [
+    {
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x123ca5880>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_simulate.to_graphviz()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af0e13da-29e6-44a6-852e-c3e965d7c462",
+   "metadata": {},
+   "source": [
+    "The gray shades on the coefficient variables depicts the tale. Check the previous model graph, it was all white.\n",
+    "\n",
+    "Now, all we have to do is generate samples, the known pymc way.\n",
+    "\n",
+    "Lets generate 100 samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2c651c0a-29f8-4669-baf7-986687f59317",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [a, b, c, y]\n"
+     ]
+    }
+   ],
+   "source": [
+    "N = 100\n",
+    "\n",
+    "with model_simulate:\n",
+    "    simulate = pm.sample_prior_predictive(samples=N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ecd7313-e68c-4439-803c-576a5c474e4b",
+   "metadata": {},
+   "source": [
+    "We know that this generates an Arviz object, and since we have called sample_prior_predictive, hence the object will only contain priors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "99a3fede-e773-4823-bb5e-ece89805bcc6",
+   "metadata": {},
+   "outputs": [
+    {
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+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
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+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
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+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
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+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
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+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
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+       "\n",
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+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
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+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
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+       ".xr-group-box-contents > label {\n",
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+       ".xr-sections {\n",
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+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
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+       "  grid-column: 1;\n",
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+       "  white-space: nowrap;\n",
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+       "\n",
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+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
+       "Dimensions:  (chain: 1, draw: 100, i: 1)\n",
+       "Coordinates:\n",
+       "  * chain    (chain) int64 8B 0\n",
+       "  * draw     (draw) int64 800B 0 1 2 3 4 5 6 7 8 ... 91 92 93 94 95 96 97 98 99\n",
+       "  * i        (i) int64 8B 0\n",
+       "Data variables:\n",
+       "    y_mu     (chain, draw, i) float64 800B -0.3443 0.8572 1.0 ... 0.5904 1.887\n",
+       "    c        (chain, draw, i) float64 800B 2.691 0.6152 -2.07 ... 1.32 -0.09201\n",
+       "    y        (chain, draw, i) float64 800B -0.6458 1.099 0.8574 ... 0.418 1.735\n",
+       "    a        (chain, draw, i) float64 800B -0.5393 0.03546 ... 0.8255 0.7696\n",
+       "    b        (chain, draw, i) float64 800B -0.4894 0.8849 ... -1.491 1.086\n",
+       "Attributes:\n",
+       "    created_at:                 2026-03-05T12:47:20.381346+00:00\n",
+       "    arviz_version:              0.23.4\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.28.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-76552f08-d801-415f-9de7-73198e1e8324' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-76552f08-d801-415f-9de7-73198e1e8324' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 100</li><li><span class='xr-has-index'>i</span>: 1</li></ul></div></li><li class='xr-section-item'><input id='section-f7009564-9b8f-42e6-b640-8935f8f8ee0c' class='xr-section-summary-in' type='checkbox' checked /><label for='section-f7009564-9b8f-42e6-b640-8935f8f8ee0c' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f00995d2-3826-43d9-aa23-3f8885270169' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f00995d2-3826-43d9-aa23-3f8885270169' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf6160ef-d2ee-4cd6-a8a7-1f28e9879ff2' class='xr-var-data-in' type='checkbox'><label for='data-bf6160ef-d2ee-4cd6-a8a7-1f28e9879ff2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 94 95 96 97 98 99</div><input id='attrs-2ab37068-4d77-4ed0-a2bb-a44bc2f2671c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ab37068-4d77-4ed0-a2bb-a44bc2f2671c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3a02e7f-b2ec-45a5-b620-6298c4a9cfb5' class='xr-var-data-in' type='checkbox'><label for='data-b3a02e7f-b2ec-45a5-b620-6298c4a9cfb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-cc366699-f079-44c5-bef2-d59d557a0f82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc366699-f079-44c5-bef2-d59d557a0f82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4589ca21-5c08-4b0d-b812-650dd76698e3' class='xr-var-data-in' type='checkbox'><label for='data-4589ca21-5c08-4b0d-b812-650dd76698e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-11c344ab-5946-4a22-aa5e-95f84e390e6c' class='xr-section-summary-in' type='checkbox' checked /><label for='section-11c344ab-5946-4a22-aa5e-95f84e390e6c' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_mu</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3443 0.8572 1.0 ... 0.5904 1.887</div><input id='attrs-b2806766-0394-4608-8f11-3eb797d511fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2806766-0394-4608-8f11-3eb797d511fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f8a808a-2499-4a22-b5f6-634ef30d9475' class='xr-var-data-in' type='checkbox'><label for='data-8f8a808a-2499-4a22-b5f6-634ef30d9475' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.34425345],\n",
+       "        [ 0.85716678],\n",
+       "        [ 1.00010337],\n",
+       "        [-0.75157066],\n",
+       "        [ 0.42714637],\n",
+       "        [ 2.20746689],\n",
+       "        [-3.875701  ],\n",
+       "        [-0.27006723],\n",
+       "        [-1.6575751 ],\n",
+       "        [-0.4942124 ],\n",
+       "        [ 0.05888411],\n",
+       "        [-0.79243039],\n",
+       "        [-2.07126518],\n",
+       "        [-0.10953519],\n",
+       "        [-0.45739411],\n",
+       "        [-2.43051599],\n",
+       "        [ 1.59369129],\n",
+       "        [-1.43184475],\n",
+       "        [ 0.62250734],\n",
+       "        [ 0.33489053],\n",
+       "...\n",
+       "        [-0.34689413],\n",
+       "        [ 0.7638837 ],\n",
+       "        [-1.84543615],\n",
+       "        [-1.02096812],\n",
+       "        [ 0.98405724],\n",
+       "        [-1.68651232],\n",
+       "        [-0.56831236],\n",
+       "        [-0.5987834 ],\n",
+       "        [ 1.13748824],\n",
+       "        [-2.15986017],\n",
+       "        [ 1.77539038],\n",
+       "        [-2.97591625],\n",
+       "        [-2.4771739 ],\n",
+       "        [-0.9798143 ],\n",
+       "        [-0.40892253],\n",
+       "        [-1.51933564],\n",
+       "        [-0.61760309],\n",
+       "        [ 0.7694917 ],\n",
+       "        [ 0.59036071],\n",
+       "        [ 1.88726573]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.691 0.6152 ... 1.32 -0.09201</div><input id='attrs-95bf2a7a-436f-467f-8d1d-8d794b106b54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95bf2a7a-436f-467f-8d1d-8d794b106b54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd6cb927-b52e-4c1d-99c2-76314c4cb2b3' class='xr-var-data-in' type='checkbox'><label for='data-dd6cb927-b52e-4c1d-99c2-76314c4cb2b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 2.6908482 ],\n",
+       "        [ 0.61519366],\n",
+       "        [-2.06964736],\n",
+       "        [-2.05845135],\n",
+       "        [ 0.54756501],\n",
+       "        [-0.27726229],\n",
+       "        [ 0.69310087],\n",
+       "        [-0.14549792],\n",
+       "        [-1.48733857],\n",
+       "        [-1.44139403],\n",
+       "        [ 0.42778375],\n",
+       "        [-1.8727539 ],\n",
+       "        [-1.91180589],\n",
+       "        [-1.8262083 ],\n",
+       "        [ 0.32796362],\n",
+       "        [-1.24373363],\n",
+       "        [ 0.52380622],\n",
+       "        [-0.27068922],\n",
+       "        [-1.3884412 ],\n",
+       "        [-2.26207795],\n",
+       "...\n",
+       "        [-0.58180586],\n",
+       "        [ 0.78212745],\n",
+       "        [ 1.28859079],\n",
+       "        [-0.84823864],\n",
+       "        [ 0.6867604 ],\n",
+       "        [-3.76947437],\n",
+       "        [ 1.02980072],\n",
+       "        [-0.63290765],\n",
+       "        [-0.55425067],\n",
+       "        [ 0.12799011],\n",
+       "        [-0.61010167],\n",
+       "        [-0.0656147 ],\n",
+       "        [ 0.06525086],\n",
+       "        [ 0.0146984 ],\n",
+       "        [ 0.82171673],\n",
+       "        [ 0.06682759],\n",
+       "        [ 0.17877991],\n",
+       "        [ 1.00929052],\n",
+       "        [ 1.32001422],\n",
+       "        [-0.0920055 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6458 1.099 ... 0.418 1.735</div><input id='attrs-7e52eb0f-8abb-4d30-9c2f-aba6d87eb611' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e52eb0f-8abb-4d30-9c2f-aba6d87eb611' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa49bda-a53d-46d2-b523-f10d85153e34' class='xr-var-data-in' type='checkbox'><label for='data-faa49bda-a53d-46d2-b523-f10d85153e34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.64584607],\n",
+       "        [ 1.09850756],\n",
+       "        [ 0.85742452],\n",
+       "        [-1.18142946],\n",
+       "        [ 0.40506646],\n",
+       "        [ 2.21718619],\n",
+       "        [-3.80670516],\n",
+       "        [-0.38122795],\n",
+       "        [-1.81027187],\n",
+       "        [-0.33986327],\n",
+       "        [ 0.22918202],\n",
+       "        [-1.07580713],\n",
+       "        [-1.86003688],\n",
+       "        [-0.22858328],\n",
+       "        [-0.4908355 ],\n",
+       "        [-2.81446548],\n",
+       "        [ 1.38913203],\n",
+       "        [-1.2869734 ],\n",
+       "        [ 0.42748928],\n",
+       "        [ 0.39087821],\n",
+       "...\n",
+       "        [-0.31638952],\n",
+       "        [ 0.48387009],\n",
+       "        [-2.33807821],\n",
+       "        [-1.00028413],\n",
+       "        [ 0.77788868],\n",
+       "        [-1.74924468],\n",
+       "        [-0.62777148],\n",
+       "        [-0.74993477],\n",
+       "        [ 1.08988575],\n",
+       "        [-1.9644924 ],\n",
+       "        [ 1.72581991],\n",
+       "        [-3.0585356 ],\n",
+       "        [-2.55771785],\n",
+       "        [-0.96164971],\n",
+       "        [-0.74620191],\n",
+       "        [-1.2896128 ],\n",
+       "        [-0.67795317],\n",
+       "        [ 0.74108291],\n",
+       "        [ 0.41800602],\n",
+       "        [ 1.73450031]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5393 0.03546 ... 0.8255 0.7696</div><input id='attrs-7f9e1ad5-5f9a-4923-9cc5-989511208743' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f9e1ad5-5f9a-4923-9cc5-989511208743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fed0509a-af12-4195-9934-76333854b238' class='xr-var-data-in' type='checkbox'><label for='data-fed0509a-af12-4195-9934-76333854b238' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-5.39296776e-01],\n",
+       "        [ 3.54624135e-02],\n",
+       "        [ 1.18278065e+00],\n",
+       "        [-3.64443939e-01],\n",
+       "        [ 5.14604984e-01],\n",
+       "        [ 1.64112545e+00],\n",
+       "        [-2.27434041e+00],\n",
+       "        [-1.95478965e-01],\n",
+       "        [-8.15575203e-01],\n",
+       "        [ 1.45165403e-01],\n",
+       "        [-1.53438176e-01],\n",
+       "        [-1.37543215e-01],\n",
+       "        [-1.27292966e+00],\n",
+       "        [ 1.31067324e-01],\n",
+       "        [-6.57392035e-02],\n",
+       "        [-1.66759032e+00],\n",
+       "        [ 8.84521029e-01],\n",
+       "        [-8.26419027e-01],\n",
+       "        [ 7.21597635e-01],\n",
+       "        [-2.06928514e-01],\n",
+       "...\n",
+       "        [ 2.90774431e-01],\n",
+       "        [ 6.85837283e-01],\n",
+       "        [-9.53134662e-01],\n",
+       "        [-1.31529013e+00],\n",
+       "        [ 9.28316232e-03],\n",
+       "        [-4.32735286e-01],\n",
+       "        [-6.59890923e-01],\n",
+       "        [-4.42414631e-01],\n",
+       "        [ 3.47533405e-01],\n",
+       "        [-6.14756304e-01],\n",
+       "        [ 4.34115235e-01],\n",
+       "        [-1.86642517e+00],\n",
+       "        [-2.26950594e+00],\n",
+       "        [ 1.45993746e-01],\n",
+       "        [ 2.84860683e-01],\n",
+       "        [-1.04943152e+00],\n",
+       "        [ 1.11143658e-01],\n",
+       "        [-2.26869684e-04],\n",
+       "        [ 8.25496194e-01],\n",
+       "        [ 7.69616445e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>b</span></div><div class='xr-var-dims'>(chain, draw, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4894 0.8849 ... -1.491 1.086</div><input id='attrs-d5f6a787-1c3b-4828-8b11-472eae6da9c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5f6a787-1c3b-4828-8b11-472eae6da9c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef4bcfe-8291-4196-b612-be6f5f3c6693' class='xr-var-data-in' type='checkbox'><label for='data-1ef4bcfe-8291-4196-b612-be6f5f3c6693' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.48937535],\n",
+       "        [ 0.88487866],\n",
+       "        [-0.21881912],\n",
+       "        [ 0.58947236],\n",
+       "        [-0.72718659],\n",
+       "        [-0.24434657],\n",
+       "        [-0.96017235],\n",
+       "        [ 0.09542942],\n",
+       "        [ 0.01712754],\n",
+       "        [-0.39934614],\n",
+       "        [ 0.22958035],\n",
+       "        [-0.03469914],\n",
+       "        [ 0.58810153],\n",
+       "        [ 0.34532331],\n",
+       "        [-0.65310627],\n",
+       "        [ 0.63427082],\n",
+       "        [ 0.15681126],\n",
+       "        [-0.15858492],\n",
+       "        [-0.06193822],\n",
+       "        [ 1.89129526],\n",
+       "...\n",
+       "        [-0.86930574],\n",
+       "        [-0.71358637],\n",
+       "        [-1.14615913],\n",
+       "        [ 1.72348381],\n",
+       "        [ 1.09157768],\n",
+       "        [ 0.1334756 ],\n",
+       "        [ 0.16083402],\n",
+       "        [ 0.36387263],\n",
+       "        [ 1.11780476],\n",
+       "        [-1.82303249],\n",
+       "        [ 1.86749719],\n",
+       "        [-0.22370583],\n",
+       "        [ 1.29644251],\n",
+       "        [-1.71887777],\n",
+       "        [-1.5467551 ],\n",
+       "        [ 0.04966194],\n",
+       "        [-1.19707508],\n",
+       "        [ 0.66720692],\n",
+       "        [-1.49126836],\n",
+       "        [ 1.08634674]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3206b30f-9a19-42b7-96a3-5ce5d18d6abf' class='xr-section-summary-in' type='checkbox' checked /><label for='section-3206b30f-9a19-42b7-96a3-5ce5d18d6abf' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2026-03-05T12:47:20.381346+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.23.4</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.28.1</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_constant_data2c0ea02a-c090-47cd-b0cd-3f510092647b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_constant_data2c0ea02a-c090-47cd-b0cd-3f510092647b\" class = \"xr-section-summary\">constant_data</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 40B\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    beta_cy  float64 8B 0.3\n",
+       "    beta_by  float64 8B 0.7\n",
+       "    beta_ay  float64 8B 1.5\n",
+       "    beta_y0  float64 8B 0.0\n",
+       "    sigma_y  float64 8B 0.2\n",
+       "Attributes:\n",
+       "    created_at:                 2026-03-05T12:47:20.384199+00:00\n",
+       "    arviz_version:              0.23.4\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.28.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cfbb7d20-8ba6-4b17-afff-19e58aaf6d33' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-cfbb7d20-8ba6-4b17-afff-19e58aaf6d33' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'></div></li><li class='xr-section-item'><input id='section-8b7a20dd-1d82-4bf5-aae5-2d5c87850aa3' class='xr-section-summary-in' type='checkbox' checked /><label for='section-8b7a20dd-1d82-4bf5-aae5-2d5c87850aa3' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_cy</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3</div><input id='attrs-d5273356-5a9c-4eb2-8139-e08274246895' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5273356-5a9c-4eb2-8139-e08274246895' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-530c14d0-e03e-4cb4-b332-4d31c183880b' class='xr-var-data-in' type='checkbox'><label for='data-530c14d0-e03e-4cb4-b332-4d31c183880b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.3)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_by</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7</div><input id='attrs-50b1c5e3-14d0-4649-bb10-c08c38de272c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50b1c5e3-14d0-4649-bb10-c08c38de272c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9574c401-e78a-489b-9907-41b702063899' class='xr-var-data-in' type='checkbox'><label for='data-9574c401-e78a-489b-9907-41b702063899' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.7)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_ay</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-6994e82e-90a4-4b29-92fc-12482f48da24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6994e82e-90a4-4b29-92fc-12482f48da24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8e13bc1-f1c3-4c67-9228-273a1ba32409' class='xr-var-data-in' type='checkbox'><label for='data-f8e13bc1-f1c3-4c67-9228-273a1ba32409' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_y0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-485a0f43-2c8d-455a-b03b-6b9843b8b163' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-485a0f43-2c8d-455a-b03b-6b9843b8b163' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c71eba1-b581-47ae-a4c5-43f25dd6b089' class='xr-var-data-in' type='checkbox'><label for='data-0c71eba1-b581-47ae-a4c5-43f25dd6b089' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2</div><input id='attrs-ead4641d-eed0-44a7-842e-8c53f8e26896' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ead4641d-eed0-44a7-842e-8c53f8e26896' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f55059e-43b8-426e-b6e4-480097acf355' class='xr-var-data-in' type='checkbox'><label for='data-8f55059e-43b8-426e-b6e4-480097acf355' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.2)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ae14ac2-4f46-4e48-8315-fc97728d1be0' class='xr-section-summary-in' type='checkbox' checked /><label for='section-9ae14ac2-4f46-4e48-8315-fc97728d1be0' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2026-03-05T12:47:20.384199+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.23.4</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.28.1</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
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+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> prior\n",
+       "\t> constant_data"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cc7caf3-61d7-47ac-a207-fa22778a9f2a",
+   "metadata": {},
+   "source": [
+    "Extract the sampled prior data into a pandas dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "86e38344-28ad-4e0d-a987-4385ed320571",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 4)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.539297</td>\n",
+       "      <td>-0.489375</td>\n",
+       "      <td>2.690848</td>\n",
+       "      <td>-0.645846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.035462</td>\n",
+       "      <td>0.884879</td>\n",
+       "      <td>0.615194</td>\n",
+       "      <td>1.098508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.182781</td>\n",
+       "      <td>-0.218819</td>\n",
+       "      <td>-2.069647</td>\n",
+       "      <td>0.857425</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.364444</td>\n",
+       "      <td>0.589472</td>\n",
+       "      <td>-2.058451</td>\n",
+       "      <td>-1.181429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.514605</td>\n",
+       "      <td>-0.727187</td>\n",
+       "      <td>0.547565</td>\n",
+       "      <td>0.405066</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          a         b         c         y\n",
+       "0 -0.539297 -0.489375  2.690848 -0.645846\n",
+       "1  0.035462  0.884879  0.615194  1.098508\n",
+       "2  1.182781 -0.218819 -2.069647  0.857425\n",
+       "3 -0.364444  0.589472 -2.058451 -1.181429\n",
+       "4  0.514605 -0.727187  0.547565  0.405066"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "observed = {\n",
+    "    \"a\": simulate.prior[\"a\"].values.flatten(),\n",
+    "    \"b\": simulate.prior[\"b\"].values.flatten(),\n",
+    "    \"c\": simulate.prior[\"c\"].values.flatten(),\n",
+    "    \"y\": simulate.prior[\"y\"].values.flatten(),\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(observed)\n",
+    "print(df.shape)\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "410b2941-ee10-444b-ab5b-36f229a6dba7",
+   "metadata": {},
+   "source": [
+    "Ok, so now we are all set with a sample data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae564cd5-23e4-4225-9ee8-e642ae47eeb7",
+   "metadata": {},
+   "source": [
+    "### Step 3. Use observe-operator to assign generated data on the model skeleton\n",
+    "\n",
+    "Now, this is another cool feature of pymc newly introduced observe method. Observe method, takes in a model skeleton and the dictionary with the data for the variables we want to infuse into the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "17860fac-d25b-46bf-b4d7-8a920610a853",
+   "metadata": {},
+   "outputs": [
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"268.86\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- a&#45;&gt;y_mu -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>a&#45;&gt;y_mu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M294.59,-283.89C306.75,-269.28 321.37,-251.72 334.11,-236.41\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"336.69,-238.79 340.4,-228.86 331.31,-234.31 336.69,-238.79\"/>\n",
+       "</g>\n",
+       "<!-- b -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>b</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"368.86\" cy=\"-315.79\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"368.86\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">b</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"368.86\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"368.86\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- b&#45;&gt;y_mu -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>b&#45;&gt;y_mu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M367.47,-274.78C367.07,-263.08 366.62,-250.29 366.22,-238.64\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"369.73,-238.82 365.88,-228.95 362.73,-239.06 369.73,-238.82\"/>\n",
+       "</g>\n",
+       "<!-- beta_y0 -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>beta_y0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"469.86\" cy=\"-315.79\" rx=\"41.54\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"469.86\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta_y0</text>\n",
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+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"469.86\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_y0&#45;&gt;y_mu -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>beta_y0&#45;&gt;y_mu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M442.55,-284.8C428.91,-269.82 412.3,-251.58 397.95,-235.81\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"400.58,-233.5 391.26,-228.46 395.4,-238.21 400.58,-233.5\"/>\n",
+       "</g>\n",
+       "<!-- beta_cy -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>beta_cy</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"570.86\" cy=\"-315.79\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"570.86\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta_cy</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"570.86\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"570.86\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_cy&#45;&gt;y_mu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>beta_cy&#45;&gt;y_mu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M539.89,-288.53C533.76,-283.82 527.24,-279.14 520.86,-275.13 489.06,-255.15 451.19,-236.67 420.73,-222.98\"/>\n",
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+       "</g>\n",
+       "<!-- beta_by -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>beta_by</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"671.86\" cy=\"-315.79\" rx=\"41.54\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"671.86\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta_by</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"671.86\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"671.86\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_by&#45;&gt;y_mu -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>beta_by&#45;&gt;y_mu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M641,-287.88C634.63,-283.18 627.75,-278.67 620.86,-275.13 556.19,-241.95 474.67,-221.03 421.22,-209.8\"/>\n",
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+       "</g>\n",
+       "<!-- sigma_y -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>sigma_y</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"55.86\" cy=\"-198.48\" rx=\"55.86\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"55.86\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">sigma_y</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"55.86\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"55.86\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- sigma_y&#45;&gt;y -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>sigma_y&#45;&gt;y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M96.11,-169.93C102.63,-165.74 109.38,-161.57 115.86,-157.82 149.81,-138.19 189.49,-118.42 219.66,-104.02\"/>\n",
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+       "</g>\n",
+       "<!-- beta_ay -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>beta_ay</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"772.86\" cy=\"-315.79\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"772.86\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta_ay</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"772.86\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"772.86\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_ay&#45;&gt;y_mu -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>beta_ay&#45;&gt;y_mu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M742.88,-287.43C736.6,-282.77 729.78,-278.39 722.86,-275.13 623.13,-228.26 494.18,-210.17 421.4,-203.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"421.99,-199.92 411.72,-202.52 421.37,-206.89 421.99,-199.92\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x124b85640>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dict = {\"a\": df[\"a\"], \"b\": df[\"b\"], \"c\": df[\"c\"], \"y\": df[\"y\"]}\n",
+    "model_inference = pm.observe(model_generative, data_dict)\n",
+    "model_inference.set_dim(\"i\", N, coord_values=np.arange(N))\n",
+    "pm.model_to_graphviz(model_inference)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ad86c3a-ca67-406c-b114-1f5449b354a1",
+   "metadata": {},
+   "source": [
+    "See the gray matter again. This time we have observed data infused into the model, and we have to sample for the coefficient and other parameters.\n",
+    "\n",
+    "So, lets sample.\n",
+    "\n",
+    "### Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6ef56be2-06a9-49b8-8044-e789f1d254e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_cy, beta_by, beta_ay, beta_y0, sigma_y]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 0 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with model_inference:\n",
+    "    idata = pm.sample(random_seed=SEED)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "375287a3-068a-4815-b86b-f472676a5416",
+   "metadata": {},
+   "source": [
+    "Now, lets validate if model captured the infused coefficient values in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4a37b112",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2484x1104 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 1115,
+       "width": 2495
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(\n",
+    "    idata,\n",
+    "    var_names=list(true_values.keys()),\n",
+    "    ref_val={k: [{\"ref_val\": v}] for k, v in true_values.items()},\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c89e6a59-db38-499e-a6ac-73a53a4fca19",
+   "metadata": {},
+   "source": [
+    "Pretty nice fit!\n",
+    "\n",
+    "Now we can use the `do`-operator for interventional what-if analysis: generating predictions for the target variable under different hypothetical settings of the predictors.\n",
+    "\n",
+    "_What if all values of `b` were 0?_\n",
+    "\n",
+    "_What if all values of `b` were doubled?_\n",
+    "\n",
+    "Here's how.\n",
+    "\n",
+    "### Step 5. Use `do`-operator to generate target variable with different what-if scenarios\n",
+    "Since we want to experiment with `b`, let's first assign observed values to `a` and `c`. Not to `y`, because that's what we want to sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c0643453-0bb6-4ceb-9fb1-3adf57f031b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_counterfactual = pm.do(model_inference, {\"a\": df[\"a\"], \"c\": df[\"c\"]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e8067a3-6430-4cf9-b53e-a5bfbd8e4488",
+   "metadata": {},
+   "source": [
+    "Now, let’s begin the fun part — generating what-if predictions.\n",
+    "\n",
+    "### _Scenario 1 :- What if all values for 'b' were 0 ?_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f7cbeba3-d047-447f-bfe2-077604fd2fd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_b0 = pm.do(model_counterfactual, {\"b\": np.zeros(N, dtype=\"int32\")}, prune_vars=True)\n",
+    "model_b1 = pm.do(model_counterfactual, {\"b\": df[\"b\"]}, prune_vars=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3b23275-029d-40a2-8603-796c740bed29",
+   "metadata": {},
+   "source": [
+    "Just sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "edd9b175-5a4b-457a-b9d9-560251733600",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: []\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: []\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Sample when 'b' was 0: P(y | (a,c), do(b=0))\n",
+    "idata_b0 = pm.sample_posterior_predictive(\n",
+    "    idata,\n",
+    "    model=model_b0,\n",
+    "    predictions=True,\n",
+    "    var_names=[\"y_mu\"],\n",
+    "    random_seed=SEED,\n",
+    ")\n",
+    "# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))\n",
+    "idata_b1 = pm.sample_posterior_predictive(\n",
+    "    idata,\n",
+    "    model=model_b1,\n",
+    "    predictions=True,\n",
+    "    var_names=[\"y_mu\"],\n",
+    "    random_seed=SEED,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cabe3d1-ecab-4bb8-bcea-d45e91ca0b04",
+   "metadata": {},
+   "source": [
+    "Some basic Python and here we have the what-if predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4d853c28-4029-4372-a46d-132239918fe5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>y</th>\n",
+       "      <th>b_scenario_1</th>\n",
+       "      <th>y_scenario_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.539297</td>\n",
+       "      <td>-0.489375</td>\n",
+       "      <td>2.690848</td>\n",
+       "      <td>-0.645846</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.053240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.035462</td>\n",
+       "      <td>0.884879</td>\n",
+       "      <td>0.615194</td>\n",
+       "      <td>1.098508</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.198966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.182781</td>\n",
+       "      <td>-0.218819</td>\n",
+       "      <td>-2.069647</td>\n",
+       "      <td>0.857425</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.132075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.364444</td>\n",
+       "      <td>0.589472</td>\n",
+       "      <td>-2.058451</td>\n",
+       "      <td>-1.181429</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1.189579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.514605</td>\n",
+       "      <td>-0.727187</td>\n",
+       "      <td>0.547565</td>\n",
+       "      <td>0.405066</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.899032</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          a         b         c         y  b_scenario_1  y_scenario_1\n",
+       "0 -0.539297 -0.489375  2.690848 -0.645846             0     -0.053240\n",
+       "1  0.035462  0.884879  0.615194  1.098508             0      0.198966\n",
+       "2  1.182781 -0.218819 -2.069647  0.857425             0      1.132075\n",
+       "3 -0.364444  0.589472 -2.058451 -1.181429             0     -1.189579\n",
+       "4  0.514605 -0.727187  0.547565  0.405066             0      0.899032"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"b_scenario_1\"] = 0\n",
+    "df[\"y_scenario_1\"] = (\n",
+    "    idata_b0.predictions.y_mu.mean((\"chain\", \"draw\")).values.reshape(1, -1).flatten()\n",
+    ")\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ad2d911-2948-4553-9322-121f064696e0",
+   "metadata": {},
+   "source": [
+    "### _Scenario 2: What if ‘b’ was 5 times as observed_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1c22e18f-cb96-4ca9-93f4-e8323b1528b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_b0 = pm.do(model_counterfactual, {\"b\": 5 * df[\"b\"]}, prune_vars=True)\n",
+    "model_b1 = pm.do(model_counterfactual, {\"b\": df[\"b\"]}, prune_vars=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5422848d-e6cd-4f3e-8641-640ed227ea78",
+   "metadata": {},
+   "source": [
+    "Sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "97990259-7cd9-4801-8af0-b8e3a46ffa7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: []\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: []\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>y</th>\n",
+       "      <th>b_scenario_1</th>\n",
+       "      <th>y_scenario_1</th>\n",
+       "      <th>b_scenario_2</th>\n",
+       "      <th>y_scenario_2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.539297</td>\n",
+       "      <td>-0.489375</td>\n",
+       "      <td>2.690848</td>\n",
+       "      <td>-0.645846</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.053240</td>\n",
+       "      <td>-2.446877</td>\n",
+       "      <td>-1.836000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.035462</td>\n",
+       "      <td>0.884879</td>\n",
+       "      <td>0.615194</td>\n",
+       "      <td>1.098508</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.198966</td>\n",
+       "      <td>4.424393</td>\n",
+       "      <td>3.422517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.182781</td>\n",
+       "      <td>-0.218819</td>\n",
+       "      <td>-2.069647</td>\n",
+       "      <td>0.857425</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.132075</td>\n",
+       "      <td>-1.094096</td>\n",
+       "      <td>0.334933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.364444</td>\n",
+       "      <td>0.589472</td>\n",
+       "      <td>-2.058451</td>\n",
+       "      <td>-1.181429</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1.189579</td>\n",
+       "      <td>2.947362</td>\n",
+       "      <td>0.957828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.514605</td>\n",
+       "      <td>-0.727187</td>\n",
+       "      <td>0.547565</td>\n",
+       "      <td>0.405066</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.899032</td>\n",
+       "      <td>-3.635933</td>\n",
+       "      <td>-1.750058</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          a         b         c         y  b_scenario_1  y_scenario_1  \\\n",
+       "0 -0.539297 -0.489375  2.690848 -0.645846             0     -0.053240   \n",
+       "1  0.035462  0.884879  0.615194  1.098508             0      0.198966   \n",
+       "2  1.182781 -0.218819 -2.069647  0.857425             0      1.132075   \n",
+       "3 -0.364444  0.589472 -2.058451 -1.181429             0     -1.189579   \n",
+       "4  0.514605 -0.727187  0.547565  0.405066             0      0.899032   \n",
+       "\n",
+       "   b_scenario_2  y_scenario_2  \n",
+       "0     -2.446877     -1.836000  \n",
+       "1      4.424393      3.422517  \n",
+       "2     -1.094096      0.334933  \n",
+       "3      2.947362      0.957828  \n",
+       "4     -3.635933     -1.750058  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample when 'b' was 5 times b: P(y | (a,c), do(b=5*b))\n",
+    "idata_b0 = pm.sample_posterior_predictive(\n",
+    "    idata,\n",
+    "    model=model_b0,\n",
+    "    predictions=True,\n",
+    "    var_names=[\"y_mu\"],\n",
+    "    random_seed=SEED,\n",
+    ")\n",
+    "# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))\n",
+    "idata_b1 = pm.sample_posterior_predictive(\n",
+    "    idata,\n",
+    "    model=model_b1,\n",
+    "    predictions=True,\n",
+    "    var_names=[\"y_mu\"],\n",
+    "    random_seed=SEED,\n",
+    ")\n",
+    "\n",
+    "df[\"b_scenario_2\"] = 5 * df[\"b\"]\n",
+    "df[\"y_scenario_2\"] = (\n",
+    "    idata_b0.predictions.y_mu.mean((\"chain\", \"draw\")).values.reshape(1, -1).flatten()\n",
+    ")\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2005665d-300b-4a01-91e4-a0901dbbaa97",
+   "metadata": {},
+   "source": [
+    "Now you've got the idea. It's an open playground — intervene on whichever variable you like and see how the predicted outcome changes.\n",
+    "\n",
+    "This opens the door for many possibilities in causal analytics: comparing treatment policies, sensitivity checks, and programmatic A/B testing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b743d58b-2678-4e17-9947-a8fe4ed03e21",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "- Authored by [Shekhar Khandelwal](https://github.com/shekharkhandelwal1983) in August 2023\n",
+    "- Updated by Osvaldo Martin in February 2026\n",
+    "- Revised wording around interventions and counterfactuals, by [Benjamin T. Vincent](https://github.com/drbenvincent) in March 2026"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "closed-frank",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "- Pearl, J., Glymour, M., & Jewell, N. P. (2016). *Causal Inference in Statistics: A Primer*. Wiley. Sections 4.2.3–4.2.4.\n",
+    "- Khandelwal, S. (2023). [Counterfactuals for Causal Analysis via PyMC Do-Operator](https://medium.com/@khandelwal-shekhar/counterfactuals-for-causal-analysis-via-pymc-do-operator-234ba04e4e80). Medium.\n",
+    "- PyMC Labs. [Causal Analysis with PyMC: Answering \"What If\" with the New Do-Operator](https://www.pymc-labs.io/blog-posts/causal-analysis-with-pymc-answering-what-if-with-the-new-do-operator/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0717070c-04aa-4836-ab95-6b3eff0dcaaf",
+   "metadata": {},
+   "source": [
+    "## Watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "sound-calculation",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Thu, 05 Mar 2026\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.12.12\n",
+      "IPython version      : 9.11.0\n",
+      "\n",
+      "pytensor: 2.38.1\n",
+      "\n",
+      "arviz : 0.23.4\n",
+      "numpy : 2.4.2\n",
+      "pandas: 3.0.1\n",
+      "pymc  : 5.28.1\n",
+      "\n",
+      "Watermark: 2.6.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w -p pytensor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e4386fc-4de9-4535-a160-d929315633ef",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymc-examples",
+   "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
+}
\ No newline at end of file
diff --git a/examples/causal_inference/counterfactuals_do_operator.myst.md b/examples/causal_inference/interventional_what_if_do_operator.myst.md
similarity index 61%
rename from examples/causal_inference/counterfactuals_do_operator.myst.md
rename to examples/causal_inference/interventional_what_if_do_operator.myst.md
index 41f03d1e6..e1731b559 100644
--- a/examples/causal_inference/counterfactuals_do_operator.myst.md
+++ b/examples/causal_inference/interventional_what_if_do_operator.myst.md
@@ -5,16 +5,16 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: eabm
+  display_name: pymc-examples
   language: python
   name: python3
 ---
 
-(counterfactuals_do_operator)=
-# Counterfactual generation using pymc do-operator
+(interventional_what_if_do_operator)=
+# Interventional what-if analysis using the PyMC do-operator
 
 :::{post} August, 2023
-:tags: causality, causal inference, do-operator, counterfactuals
+:tags: causal inference, do-operator, interventional analysis
 :category: beginner, reference
 :author: Shekhar Khandelwal
 :::
@@ -22,7 +22,7 @@ kernelspec:
 ```{code-cell} ipython3
 import warnings
 
-import arviz.preview as az
+import arviz as az
 import numpy as np
 import pandas as pd
 import pymc as pm
@@ -31,24 +31,39 @@ warnings.filterwarnings("ignore")
 ```
 
 ```{code-cell} ipython3
-%config InlineBackend.figure_format = 'retina'  # high resolution figures
-az.style.use("arviz-variat")
+%config InlineBackend.figure_format = "retina"
+az.style.use("arviz-darkgrid")
 rng = np.random.default_rng(42)
 SEED = 8927
 ```
 
 # Introduction
 
-In the realm of data science and analytics, understanding the causal relationships between variables is paramount. While traditional statistical methods have provided insights into these relationships, the advent of probabilistic programming has ushered in a new era of causal analysis. In this article, we will explore the power of counterfactuals in causal analysis using the PyMC framework, with a special focus on the “do-operator.”
-Counterfactuals are essentially “what-if” scenarios that allow us to understand the potential outcomes had a different action been taken or a different condition been present. By leveraging the PyMC framework and its “do-operator,” we can programmatically simulate these scenarios, giving us a deeper understanding of the relationships between predictors and target variables.
+In the realm of data science and analytics, understanding the causal relationships between variables is paramount. While traditional statistical methods have provided insights into these relationships, the advent of probabilistic programming has ushered in a new era of causal analysis. In this article, we will explore the power of interventional what-if analysis using the PyMC framework, with a special focus on the `do`-operator.
 
-Through a step-by-step guide, we will delve into the process of building a PyMC model skeleton, generating data using the do-operator, and validating the relationships captured by the model. Furthermore, we will explore the magic of the do-operator in simulating different ‘what-if’ scenarios, akin to programmatic A/B testing.
+The `do`-operator lets us answer interventional "what-if" questions: *What would happen to the outcome if we forced a variable to a specific value?* By leveraging `pm.do`, we can programmatically simulate these scenarios, giving us a deeper understanding of the causal relationships between predictors and target variables.
 
-- Step 1. Build a pymc model skeleton
-- Step 2. Use model skeleton and generate data using do-operator to infuse relationship between predictors and target variable (ssshhh, that’s a hidden superhero feature of do-operator ;) )
-- Step 3. Use observe-operator to assign generated data on the model skeleton
-- Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples (isn’t that what we expect a predictive model to do ;) )
-- Step 5. Use do-operator to time travel, and generate target variable with different ‘what-if’ scenarios (basically mimic A/B testing…programatically)
+Through a step-by-step guide, we will delve into the process of building a PyMC model skeleton, generating data using the `do`-operator, and validating the relationships captured by the model. Furthermore, we will explore how the `do`-operator can simulate different what-if scenarios, akin to programmatic A/B testing.
+
+:::{admonition} Interventions vs. counterfactuals: where does this notebook sit on the causal ladder?
+:class: important
+
+Pearl's *causal ladder* distinguishes three levels of causal reasoning:
+
+- **L1 — Association (seeing)**: What do we observe? $P(Y \mid X)$
+- **L2 — Intervention (doing)**: What happens if we force a variable to a value? $P(Y \mid \operatorname{do}(X = x))$
+- **L3 — Counterfactual (imagining)**: What *would have happened* to *this specific unit* under a different action, given what we already observed? $Y_x \mid X = x', Y = y'$
+
+`pm.do` directly supports **L2 queries**. This notebook demonstrates interventional what-if analysis — we set a variable to a new value and predict the outcome. True unit-level counterfactuals (L3) require an additional **abduction** step: inferring unit-specific exogenous terms ($U$) from observed evidence before predicting in the intervened world. That step is not demonstrated here.
+
+For readers interested in L3 counterfactual reasoning, see Pearl, Glymour & Jewell (2016), *Causal Inference in Statistics: A Primer*, Sections 4.2.3–4.2.4.
+:::
+
+- Step 1. Build a PyMC model skeleton
+- Step 2. Use model skeleton and generate data using `do`-operator to infuse relationship between predictors and target variable
+- Step 3. Use `observe`-operator to assign generated data on the model skeleton
+- Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples
+- Step 5. Use `do`-operator to generate target variable with different what-if scenarios (programmatic A/B testing)
 
 +++
 
@@ -89,7 +104,13 @@ pm.model_to_graphviz(model_generative)
 Let’s first define the predictors relationship with target variable.
 
 ```{code-cell} ipython3
-true_values = {"beta_ay": 1.5, "beta_by": 0.7, "beta_cy": 0.3, "sigma_y": 0.2, "beta_y0": 0.0}
+true_values = {
+    "beta_ay": 1.5,
+    "beta_by": 0.7,
+    "beta_cy": 0.3,
+    "sigma_y": 0.2,
+    "beta_y0": 0.0,
+}
 ```
 
 Basically what we are saying here is, we are intentionally defining the coefficient values, which we expect predictive model to predict later on.
@@ -169,35 +190,33 @@ with model_inference:
 Now, lets validate if model captured the infused coefficient values in the data.
 
 ```{code-cell} ipython3
-pc = az.plot_dist(
+az.plot_posterior(
     idata,
     var_names=list(true_values.keys()),
-)
-az.add_lines(pc, true_values);
+    ref_val={k: [{"ref_val": v}] for k, v in true_values.items()},
+);
 ```
 
-BAM ! Pretty nice fit !
+Pretty nice fit!
 
-Now, lets do what we are supposed to do ! Counterfactuals.
+Now we can use the `do`-operator for interventional what-if analysis: generating predictions for the target variable under different hypothetical settings of the predictors.
 
-Basically, this is about generating target variable values with different predictor values. Basically, answering what if questions !
+_What if all values of `b` were 0?_
 
-_What-if there was all ‘b’ values as 0 ?_
+_What if all values of `b` were doubled?_
 
-_What-if all ‘b’ values were double ?_
+Here's how.
 
-How to do this ? Here you go..
-
-### Step 5. Use do-operator to time travel, and generate target variable with different ‘what-if’ scenarios.
-Since, we want to experiment with ‘b’, lets first assign observed values to ‘a’ and ‘c’. Not to ‘y’, because that’s what we want to sample. Correct !
+### Step 5. Use `do`-operator to generate target variable with different what-if scenarios
+Since we want to experiment with `b`, let's first assign observed values to `a` and `c`. Not to `y`, because that's what we want to sample.
 
 ```{code-cell} ipython3
 model_counterfactual = pm.do(model_inference, {"a": df["a"], "c": df["c"]})
 ```
 
-Now, lets begin the fun part. Let’s generate counterfactuals.
+Now, let’s begin the fun part — generating what-if predictions.
 
-### _Scenario 1 :- What if all values for ‘b’ were 0 ?_
+### _Scenario 1 :- What if all values for 'b' were 0 ?_
 
 ```{code-cell} ipython3
 model_b0 = pm.do(model_counterfactual, {"b": np.zeros(N, dtype="int32")}, prune_vars=True)
@@ -225,7 +244,7 @@ idata_b1 = pm.sample_posterior_predictive(
 )
 ```
 
-Some basic python and here we have the counterfactuals.
+Some basic Python and here we have the what-if predictions.
 
 ```{code-cell} ipython3
 df["b_scenario_1"] = 0
@@ -269,23 +288,24 @@ df["y_scenario_2"] = (
 df.head(5)
 ```
 
-Ok, so now you got the idea. It's an open playground. Go back in time, change whatever you want to change, and see how output changes.
+Now you've got the idea. It's an open playground — intervene on whichever variable you like and see how the predicted outcome changes.
 
-This opens the door for many more possibilities in various use cases. Especially, Causal Analytics !
+This opens the door for many possibilities in causal analytics: comparing treatment policies, sensitivity checks, and programmatic A/B testing.
 
 +++
 
 ## Authors
 - Authored by [Shekhar Khandelwal](https://github.com/shekharkhandelwal1983) in August 2023
-- Updated by Osvaldo Martin in February 2026 
+- Updated by Osvaldo Martin in February 2026
+- Revised wording around interventions and counterfactuals, by [Benjamin T. Vincent](https://github.com/drbenvincent) in March 2026
 
 +++
 
 ## References
 
-https://medium.com/@khandelwal-shekhar/counterfactuals-for-causal-analysis-via-pymc-do-operator-234ba04e4e80
-
-https://www.pymc-labs.io/blog-posts/causal-analysis-with-pymc-answering-what-if-with-the-new-do-operator/
+- Pearl, J., Glymour, M., & Jewell, N. P. (2016). *Causal Inference in Statistics: A Primer*. Wiley. Sections 4.2.3–4.2.4.
+- Khandelwal, S. (2023). [Counterfactuals for Causal Analysis via PyMC Do-Operator](https://medium.com/@khandelwal-shekhar/counterfactuals-for-causal-analysis-via-pymc-do-operator-234ba04e4e80). Medium.
+- PyMC Labs. [Causal Analysis with PyMC: Answering "What If" with the New Do-Operator](https://www.pymc-labs.io/blog-posts/causal-analysis-with-pymc-answering-what-if-with-the-new-do-operator/).
 
 +++
 
diff --git a/requirements-docs.txt b/requirements-docs.txt
index 817b556b5..d8ff3bc7b 100644
--- a/requirements-docs.txt
+++ b/requirements-docs.txt
@@ -11,3 +11,4 @@ sphinxcontrib-bibtex
 sphinxext-opengraph
 sphinxext-rediraffe
 sphinx-sitemap
+watermark