avehtari/BDA_py_demos resource

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What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebook demos in Python accompanying the textbook Bayesian Data Analysis (3rd edition) by Gelman et al. The demos cover Bayesian inference topics such as priors, grid sampling, hierarchical models, posterior predictive checking, and MCMC methods including Gibbs sampling and importance sampling, with some demos using CmdStanPy and ArviZ.

Use cases

• learn bayesian data analysis with python
• reproduce BDA3 book examples in jupyter notebooks
• learn MCMC methods like gibbs sampling and importance sampling
• practice bayesian inference with stan and cmdstanpy
• supplement a university bayesian statistics course
• see worked examples of hierarchical models and posterior predictive checking

When to choose

• you are studying the BDA3 textbook or taking the Aalto Bayesian Data Analysis course
• you want runnable Python notebooks demonstrating Bayesian concepts chapter by chapter
• you prefer Python over the Matlab/Octave or R versions of the demos

When to avoid

• you need a production Bayesian inference library rather than educational demos
• you want a comprehensive tutorial covering every chapter of the book
• you work outside Python and should use the R or Matlab/Octave demo repositories instead

Facets

learning-resource · maturity active

machine-learning data-science developer-tools data-science education tutorials python cross-platform bayesian-statistics mcmc stan jupyter-notebooks statistics bda3

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