Rerun survival (pymc-devs#865)

* rerun weibull

* rerun censored

* rerun param

Author: aloctavodia

examples/survival_analysis/bayes_param_survival.ipynb

@@ -5,7 +5,7 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: pymc
+  display_name: arviz_1
   language: python
   name: python3
 ---
@@ -17,7 +17,7 @@ kernelspec:
 ```{code-cell} ipython3
 import warnings
 
-import arviz.preview as az
+import arviz as az
 import numpy as np
 import pymc as pm
 import pytensor.tensor as pt
@@ -231,7 +231,7 @@ az.plot_energy(weibull_trace);
 The $\hat{R}$ statistics also indicate convergence.
 
 ```{code-cell} ipython3
-az.rhat(weibull_trace).to_array().max()
+az.rhat(weibull_trace).ds.to_array().max()
 ```
 
 Below we plot posterior distributions of the parameters.
@@ -341,7 +341,7 @@ az.plot_energy(log_logistic_trace);
 ```
 
 ```{code-cell} ipython3
-az.rhat(log_logistic_trace).to_array().max()
+az.rhat(log_logistic_trace).ds.to_array().max()
 ```
 
 Again, we calculate the posterior expected survival functions for this model.
@@ -396,6 +396,7 @@ This post has been a short introduction to implementing parametric survival regr
 - Updated by [George Ho](https://eigenfoo.xyz/) on July 18, 2018.
 - Updated by @fonnesbeck on September 11, 2024.
 - Updated by Osvaldo Martin on December 2025.
+- Updated by Osvaldo Martin on April 2026.
 
 ```{code-cell} ipython3
 %load_ext watermark

examples/survival_analysis/bayes_param_survival.myst.md

@@ -5,9 +5,9 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: Python [conda env:base] *
+  display_name: arviz_1
   language: python
-  name: conda-base-py
+  name: python3
 ---
 
 (censored_data)=
@@ -22,7 +22,7 @@ kernelspec:
 ```{code-cell} ipython3
 from copy import copy
 
-import arviz.preview as az
+import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pymc as pm
@@ -36,42 +36,22 @@ rng = default_rng(1234)
 az.style.use("arviz-variat")
 ```
 
-[This example notebook on Bayesian survival
-analysis](https://www.pymc.io/projects/examples/en/latest/survival_analysis/survival_analysis.html) touches on the
-point of censored data. _Censoring_ is a form of missing-data problem, in which
-observations greater than a certain threshold are clipped down to that
-threshold, or observations less than a certain threshold are clipped up to that
-threshold, or both. These are called right, left and interval censoring,
-respectively. In this example notebook we consider interval censoring.
+[This example notebook on Bayesian survival analysis](https://www.pymc.io/projects/examples/en/latest/survival_analysis/survival_analysis.html) touches on the point of censored data. _Censoring_ is a form of missing-data problem, in which observations greater than a certain threshold are clipped down to that threshold, or observations less than a certain threshold are clipped up to that threshold, or both. These are called right, left and interval censoring,respectively. In this example notebook we consider interval censoring.
 
 Censored data arises in many modelling problems. Two common examples are:
 
-1. _Survival analysis:_ when studying the effect of a certain medical treatment
-   on survival times, it is impossible to prolong the study until all subjects
-   have died. At the end of the study, the only data collected for many patients
-   is that they were still alive for a time period $T$ after the treatment was
-   administered: in reality, their true survival times are greater than $T$.
+1. _Survival analysis:_ when studying the effect of a certain medical treatment on survival times, it is impossible to prolong the study until all subjects have died. At the end of the study, the only data collected for many patients is that they were still alive for a time period $T$ after the treatment was administered: in reality, their true survival times are greater than $T$.
 
-2. _Sensor saturation:_ a sensor might have a limited range and the upper and
-   lower limits would simply be the highest and lowest values a sensor can
-   report. For instance, many mercury thermometers only report a very narrow
-   range of temperatures.
+2. _Sensor saturation:_ a sensor might have a limited range and the upper and lower limits would simply be the highest and lowest values a sensor can report. For instance, many mercury thermometers only report a very narrow range of temperatures.
 
 This example notebook presents two different ways of dealing with censored data
 in PyMC:
 
-1. An imputed censored model, which represents censored data as parameters and
-   makes up plausible values for all censored values. As a result of this
-   imputation, this model is capable of generating plausible sets of made-up
-   values that would have been censored. Each censored element introduces a
-   random variable.
+1. An imputed censored model, which represents censored data as parameters and makes up plausible values for all censored values. As a result of this imputation, this model is capable of generating plausible sets of made-up values that would have been censored. Each censored element introduces a random variable.
 
-2. An unimputed censored model, where the censored data are integrated out and
-   accounted for only through the log-likelihood. This method deals more
-   adequately with large amounts of censored data and converges more quickly.
+2. An unimputed censored model, where the censored data are integrated out and accounted for only through the log-likelihood. This method deals more adequately with large amounts of censored data and converges more quickly.
 
-To establish a baseline we compare to an uncensored model of the uncensored
-data.
+To establish a baseline we compare to an uncensored model of the uncensored data.
 
 ```{code-cell} ipython3
 # Produce normally distributed samples
@@ -238,6 +218,7 @@ As we can see, both censored models appear to capture the mean and variance of t
 - Updated by [Benjamin Vincent](https://github.com/drbenvincent) in May 2021.
 - Updated by [Benjamin Vincent](https://github.com/drbenvincent) in May 2022.
 - Updated by [Osvaldo Martin](https://github.com/aloctavodia) in Dec 2025.
+- Updated by [Osvaldo Martin](https://github.com/aloctavodia) in Apr 2026.
 
 +++
 

examples/survival_analysis/censored_data.ipynb

@@ -5,7 +5,7 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: pymc_env
+  display_name: arviz_1
   language: python
   name: python3
 myst:
@@ -24,7 +24,7 @@ myst:
 :::
 
 ```{code-cell} ipython3
-import arviz.preview as az
+import arviz as az
 import numpy as np
 import pymc as pm
 import pytensor.tensor as pt
@@ -125,7 +125,7 @@ with model_1:
 ```
 
 ```{code-cell} ipython3
-az.plot_trace_dist(idata_param1, var_names=["alpha", "beta"])
+az.plot_trace_dist(idata_param1, var_names=["alpha", "beta"]);
 ```
 
 ```{code-cell} ipython3
@@ -155,7 +155,7 @@ with model_2:
 ```
 
 ```{code-cell} ipython3
-az.plot_trace_dist(idata_param2, var_names=["r", "beta"])
+az.plot_trace_dist(idata_param2, var_names=["r", "beta"]);
 ```
 
 ```{code-cell} ipython3
@@ -190,7 +190,7 @@ with model_3:
 ```
 
 ```{code-cell} ipython3
-az.plot_trace_dist(idata_param3)
+az.plot_trace_dist(idata_param3);
 ```
 
 ```{code-cell} ipython3
@@ -203,6 +203,7 @@ az.summary(idata_param3, round_to=2)
 - Authored and ported to Jupyter notebook by [George Ho](https://eigenfoo.xyz/) on Jul 15, 2018.
 - Updated for compatibility with PyMC v5 by Chris Fonnesbeck on Jan 16, 2023.
 - Updated to replace `pm.Potential` with `pm.Censored` by Jonathan Dekermanjian on Nov 25, 2024.
+- Updated by Osvaldo Martin in April 2026.
 
 ```{code-cell} ipython3
 %load_ext watermark