# AllenDowney/ThinkBayes2

Text and code for the second edition of Think Bayes, by Allen Downey.

Repository: https://github.com/AllenDowney/ThinkBayes2
Canonical: https://ross.abutalabs.com/products/thinkbayes2
Homepage: http://allendowney.github.io/ThinkBayes2/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: jupyter, python, bayes, bayesian-statistics
Last push: 2026-07-27T19:18:46+00:00

## Health v2 (maintenance only)
Score: 75/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 94, release rhythm 35, longevity 100
- inputs: {"age_days": 4394, "days_push": 37, "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 2075, forks 1560 (observed 2026-08-28T04:06:10.726518+00:00)

## What it is
Think Bayes 2 is a free book by Allen Downey introducing Bayesian statistics through computational methods, with text and supporting Python code. Each chapter has a Jupyter notebook with examples and exercises, runnable locally or on Colab.

## Use cases
- learn bayesian statistics with python
- free book on bayes theorem and probability
- jupyter notebooks for bayesian data analysis
- computational approach to statistics exercises
- learn pymc through worked examples
- self-study statistics textbook with code

## When to choose
- you want to learn Bayesian statistics hands-on with runnable Python code
- you prefer computational over mathematical derivations
- you need a free, openly licensed textbook with exercise solutions

## When to avoid
- you need a production Bayesian inference library rather than teaching material
- you want a purely theoretical or proof-heavy treatment
- you need commercial redistribution, since the book is CC BY-NC-SA

## Facets
- artifact type: learning-resource
- maturity: stable
- function: data-science, math
- domain: education, data-science, tutorials
- platform: python, cross-platform
- tags: bayesian-statistics, jupyter-notebooks, free-book, statistics, probabilistic-programming

## Member repositories
- AllenDowney/ThinkBayes2 (main) score 75

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:10.726518+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 2026-08-30T02:56:15.768294+00:00, confidence not recorded.
  - readme: https://github.com/AllenDowney/ThinkBayes2 (fetched 2026-08-28T04:06:10.726518+00:00, sha 4181e32e830f)
  - homepage: http://allendowney.github.io/ThinkBayes2/ (fetched 2026-08-29T10:36:26.921236+00:00, sha 9172d4e1d155)
  - registry_pypi: https://pypi.org/pypi/thinkbayes2/json (fetched 2026-08-29T10:36:26.930695+00:00, sha 14b0c8b9c8a0)
- Data as of 2026-08-30T08:39:29.467469+00:00.