markdregan/Bayesian-Modelling-in-Python resource

Health v2 · maintenance only

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2502 stars · 405 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Jupyter Notebook tutorial teaching Bayesian modeling techniques in Python using PyMC3. It covers parameter estimation, model checking, hierarchical modeling, Bayesian regression, survival analysis, and A/B testing.

Use cases

• learn bayesian modeling in python
• pymc3 tutorial with notebooks
• bayesian regression example code
• hierarchical bayesian modeling cookbook
• bayesian a/b testing example
• bayesian survival analysis in python

When to choose

• you know bayesian statistics fundamentals and want practical PyMC3 code
• you want worked notebook examples of MCMC, hierarchical models, and Bayesian A/B tests

When to avoid

• you need a maintained library or up-to-date PyMC APIs (last updated 2017, uses PyMC3)
• you want a statistics theory course rather than a programming cookbook
• you need a licensed project for redistribution (no license specified)

Facets

learning-resource · maturity abandoned

machine-learning data-science tutorials data-science machine-learning python bayesian-statistics pymc3 jupyter-notebook mcmc tutorial

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markdown · JSON · MCP: product_card(name="markdregan/Bayesian-Modelling-in-Python")