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 modelling 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 into 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.