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Jupyter NotebookPyMC educational resources
bayesian-inferencebayesian-statisticsdata-sciencedata-analysis
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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
- PyMC port of the book "Statistical Rethinking" by Richard McElreath (first edition)
- PyMC port of the book "Statistical Rethinking" by Richard McElreath (second edition)
- PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers
- PyMC port of the book "Bayesian Statistical Methods" by Brian J. Reich and Sujit K. Ghosh
- 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
- PyMC port of the book "Bayes Rules" by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu
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
- Reading the contributing guide for pymc 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.
- In this repo the environments are defined per resource. Look into each directory to find the environment file and use that
- When ready to contribute open a draft PR stating the scope of work as early as possible. This helps avoid duplicate work early.
- 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.
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