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

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28173 stars · 7911 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

learning-resource · maturity stable

machine-learning data-science documentation machine-learning data-science tutorials education python jvm-scripting bayesian-inference probabilistic-programming pymc jupyter-notebook statistics open-textbook

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