# pymc_resources

**Repository Path**: mirrors_pymc-devs/pymc_resources

## Basic Information

- **Project Name**: pymc_resources
- **Description**: PyMC educational resources
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-26
- **Last Updated**: 2026-04-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PyMC Resources
PyMC educational resources, including the PyMC port of the following books (original models in STAN/BUGS/JAGS etc,.):

-  ["Bayesian Modeling and Computation in Python" by Osvaldo A. Martin,  Ravin Kumar, Junpeng Lao](https://bayesiancomputationbook.com/welcome.html)
-  [PyMC port of the book "Statistical Rethinking" by Richard McElreath (first edition)](https://github.com/pymc-devs/resources/tree/main/Rethinking)
-  [PyMC port of the book "Statistical Rethinking" by Richard McElreath (second edition)](https://github.com/pymc-devs/resources/tree/main/Rethinking_2)
-  [PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers](https://github.com/pymc-devs/resources/tree/main/BCM)
-  [PyMC port of the book "Bayesian Statistical Methods" by Brian J. Reich and Sujit K. Ghosh](https://github.com/pymc-devs/resources/tree/main/BSM)
-  [PyMC port of the book "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin](https://github.com/pymc-devs/resources/tree/main/BDA3)
-  [PyMC port of the book "Bayes Rules" by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu](https://github.com/pymc-devs/resources/tree/main/Bayes_Rules)

## How to contribute
Thanks wanting to contribute! These resources are a community effort and we, and all future resource users, appreciate your help.

If just starting
1. Reading the [contributing guide for pymc]( https://docs.pymc.io/en/latest/contributing/index.html) is a good place to start. The guide will familiarize you with the high level tools and workflow.
  * Some of the instructions will differ so read the below steps first.
2. In this repo the environments are defined per resource. Look into each directory to find the environment file and use that
3. When ready to contribute open a draft PR stating the scope of work as early as possible. This helps avoid duplicate work early.
4. If you have further questions don't hesitate to ask on https://discourse.pymc.io/.

# License
Unless otherwise stated in the directory containing the codes, all codes are copyrighted by their author(s) under MIT license.