# AllenDowney/ThinkBayes

Code repository for Think Bayes.

Repository: https://github.com/AllenDowney/ThinkBayes
Canonical: https://ross.abutalabs.com/products/thinkbayes
Language: TeX
License Family: other
Last push: 2021-03-12T01:26:06+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": 4804, "days_push": 2001, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1702, forks 1902 (observed 2026-08-28T04:05:24.399625+00:00)

## What it is
The companion code repository for 'Think Bayes: Bayesian Statistics Made Simple' by Allen B. Downey, a book published by O'Reilly Media. It contains Python examples and exercises that teach Bayesian statistics through hands-on computation.

## Use cases
- learn bayesian statistics with python
- find worked examples of bayes theorem in code
- supplement reading the Think Bayes book
- teach an intro bayesian statistics course
- practice probability modeling with python

## When to choose
- you are reading or teaching from the Think Bayes book
- you want code-first, computational introductions to Bayesian methods
- you prefer learning statistics through runnable Python examples

## When to avoid
- you need a production Bayesian inference library like PyMC or Stan
- you want actively maintained statistical software
- you need a license-clear codebase for redistribution

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, math
- domain: education, data-science
- platform: python
- tags: bayesian-statistics, book, textbook, probability, statistics

## Member repositories
- AllenDowney/ThinkBayes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:24.399625+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 2026-08-30T03:37:39.294202+00:00, confidence not recorded.
  - readme: https://github.com/AllenDowney/ThinkBayes (fetched 2026-08-28T04:05:24.399625+00:00, sha ed2709b41050)
- Data as of 2026-08-30T08:39:29.467469+00:00.