# avehtari/BDA_py_demos

Bayesian Data Analysis demos for Python

Repository: https://github.com/avehtari/BDA_py_demos
Canonical: https://ross.abutalabs.com/products/bda_py_demos
Homepage: https://avehtari.github.io/BDA_course_Aalto/demos.html#BDA_Python_demos
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Topics: python, bayesian-data-analysis, bayesian-inference, bayesian, mcmc, stan
Last push: 2025-09-19T08:05:57+00:00

## Health v2 (maintenance only)
Score: 51/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 42, release rhythm 35, longevity 100
- inputs: {"age_days": 4200, "days_push": 348, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1041, forks 304 (observed 2026-08-28T04:03:20.591393+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, developer-tools
- domain: data-science, education, tutorials
- platform: python, cross-platform
- tags: bayesian-statistics, mcmc, stan, jupyter-notebooks, statistics, bda3

## Member repositories
- avehtari/BDA_py_demos (main) score 51

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.591393+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:02:53.294739+00:00, confidence not recorded.
  - readme: https://github.com/avehtari/BDA_py_demos (fetched 2026-08-28T04:03:20.591393+00:00, sha d789dc352df3)
  - homepage: https://avehtari.github.io/BDA_course_Aalto/demos.html#BDA_Python_demos (fetched 2026-08-29T13:04:12.743400+00:00, sha 3f21dd282d3c)
- Data as of 2026-08-30T08:39:29.467469+00:00.