# CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Repository: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
Canonical: https://ross.abutalabs.com/products/probabilistic-programming-and-bayesian-methods-for-hackers
Homepage: http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: bayesian-methods, pymc, mathematical-analysis, jupyter-notebook, data-science, statistics
Last push: 2024-06-25T20:42:58+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": 4979, "days_push": 799, "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 28173, forks 7911 (observed 2026-08-28T04:11:48.163501+00:00)

## What it is
An open-source introductory book on Bayesian inference and probabilistic programming using Python and PyMC, presented as Jupyter notebooks. It takes a computation-first, mathematics-second approach to teaching Bayesian methods.

## Use cases
- learn bayesian inference from scratch
- introduction to probabilistic programming with pymc
- understand mcmc and posterior sampling with python examples
- free textbook on bayesian statistics for programmers
- learn bayesian methods without heavy math background
- jupyter notebook tutorials for bayesian data analysis

## When to choose
- you want a hands-on, code-first introduction to Bayesian inference
- you prefer learning through runnable Jupyter notebooks rather than derivations
- you are a Python user wanting practical PyMC examples

## When to avoid
- you need rigorous mathematical treatment of Bayesian theory
- you need a comprehensive reference rather than an introductory text
- you use probabilistic programming languages other than PyMC

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science, documentation
- domain: machine-learning, data-science, tutorials, education
- platform: python, jvm-scripting
- tags: bayesian-inference, probabilistic-programming, pymc, jupyter-notebook, statistics, open-textbook

## Member repositories
- CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:48.163501+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 2026-08-29T16:54:26.193243+00:00, confidence not recorded.
  - readme: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers (fetched 2026-08-28T04:11:48.163501+00:00, sha 30092b646710)
  - homepage: http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ (fetched 2026-08-29T07:51:04.388363+00:00, sha f91d8f9768c6)
- Data as of 2026-08-30T08:39:29.467469+00:00.