# markdregan/Bayesian-Modelling-in-Python

A python tutorial on bayesian modeling techniques (PyMC3)

Repository: https://github.com/markdregan/Bayesian-Modelling-in-Python
Canonical: https://ross.abutalabs.com/products/bayesian-modelling-in-python
Language: Jupyter Notebook
License Family: other
Topics: bayesian-statistics, tutorial, pymc, python
Last push: 2017-04-29T20:30:18+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": 4010, "days_push": 3413, "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 2502, forks 405 (observed 2026-08-28T04:06:56.948435+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, data-science
- domain: tutorials, data-science, machine-learning
- platform: python
- tags: bayesian-statistics, pymc3, jupyter-notebook, mcmc, tutorial

## Member repositories
- markdregan/Bayesian-Modelling-in-Python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:56.948435+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 2026-08-30T02:26:49.895615+00:00, confidence not recorded.
  - readme: https://github.com/markdregan/Bayesian-Modelling-in-Python (fetched 2026-08-28T04:06:56.948435+00:00, sha 317868c6d65b)
- Data as of 2026-08-30T08:39:29.467469+00:00.