# pymc-devs/pymc-resources

PyMC educational resources

Repository: https://github.com/pymc-devs/pymc-resources
Canonical: https://ross.abutalabs.com/products/pymc-resources
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: bayesian-inference, bayesian-statistics, data-science, data-analysis
Last push: 2024-12-23T21:26:01+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3215, "days_push": 618, "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 2102, forks 762 (observed 2026-08-28T04:06:13.715989+00:00)

## What it is
A collection of educational resources for PyMC, including Python ports of popular Bayesian statistics textbooks such as Statistical Rethinking, Bayesian Data Analysis, and Bayes Rules. The materials are provided as Jupyter Notebooks with models originally written in STAN, BUGS, or JAGS translated to PyMC.

## Use cases
- learn bayesian inference with pymc
- find pymc examples from statistical rethinking book
- study bayesian data analysis in python
- port stan or bugs models to pymc
- teach a bayesian statistics course with notebooks
- practice bayesian modeling and computation in python

## When to choose
- you are learning Bayesian statistics with PyMC and want worked examples
- you want to follow a textbook like Statistical Rethinking using PyMC instead of R or Stan
- you need reference notebooks for Bayesian cognitive modeling or Bayesian data analysis

## When to avoid
- you need a production probabilistic programming library rather than educational material
- you are looking for PyMC itself or its documentation rather than learning resources
- you need non-Bayesian machine learning tutorials

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning
- domain: data-science, education, tutorials
- platform: python, cross-platform
- tags: bayesian-statistics, pymc, jupyter-notebooks, probabilistic-programming, educational-resources

## Member repositories
- pymc-devs/pymc-resources (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:13.715989+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 2026-08-30T02:54:17.058883+00:00, confidence not recorded.
  - readme: https://github.com/pymc-devs/pymc-resources (fetched 2026-08-28T04:06:13.715989+00:00, sha de635e78537a)
- Data as of 2026-08-30T08:39:29.467469+00:00.