Martin O. et al. Bayesian Modeling and Computation in Python

CRC, 2021. — 421 p. — ISBN: 9780367894368.Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands-on approach with PyMC3, Tensorflow Probability, ArviZ, and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory.The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals, the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modeling in different settings, and a chapter about the internals of probabilistic programming languages. Finally, the last chapter serves as a reference for the rest of the book by getting closer to mathematical aspects or by extending the discussion of certain topics.This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.