PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python, Abril-Pla O, Andreani V, Carroll C, Dong L, Fonnesbeck CJ, Kochurov M, Kumar R, Lao J, Luhmann CC, Martin OA, Osthege M, Vieira R, Wiecki T, Zinkov R. To install PyMC on your system, follow the instructions on the installation guide. Bonus: it’s hosted by Alex Andorra, one of the PyMC core devs! The “Learning Bayesian Statistics” podcast helps you discover and stay up-to-date with the vast Bayesian community. You can also find all the talks given at PyMCon 2020 here. Here is a YouTube playlist gathering several talks on PyMC. PyMC port of the book “Bayesian Cognitive Modeling” by Michael Lee and EJ Wagenmakers: Focused on using Bayesian statistics in cognitive modeling.īayesian Analysis with Python (second edition) by Osvaldo Martin: Great introductory book. PyMC port of the book “Statistical Rethinking A Bayesian Course with Examples in R and Stan” by Richard McElreath PyMC port of the book “Doing Bayesian Data Analysis” by John Kruschke as well as the first edition. Probabilistic Programming and Bayesian Methods for Hackers: Fantastic book with many applied code examples. Learn Bayesian statistics with a book together with PyMC Getting started If you already know about Bayesian statistics: Transparent support for missing value imputation With thousands of parameters with little specialized knowledge ofįor fast approximate posterior estimation as well as mini-batch ADVIĬomputation optimization and dynamic C or JAX compilation Powerful sampling algorithms, such as the No U-Turn Intuitive model specification syntax, for example, x ~ N(0,1) Its flexibility and extensibility make it applicable to aįor questions on PyMC, head on over to our PyMC Discourse forum. PyMC (formerly PyMC3) is a Python package for Bayesian statistical modelingįocusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI)Īlgorithms.
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