Alexandre Andorra
PyMC Developer, Cofounder of PyMC Labs, and Host of the Learning Bayesian Statistics podcast
Book Recommendations:
Recommended by Alexandre Andorra
“I think this book is particularly appropriate for Master's degree levels classes and intermediate level users in general. The topics can be quite advanced and are definitely original--a lot of them are not dealt with in the other books I know on the market. For instance, if I want to learn about Bayesian additive models with PyMC or time series with TFP, there are no other books on these topics yet. Chapter 8, about approximate Bayesian computation is also very novel, as it draws on the latest and most advanced research on the topic (as do chapters 6 and 7 for splines and BARTs). The focus the authors have on graphs, decision making under uncertainty, and the technical appendices are very useful. The first two allow for more concrete courses that alternate with more theoretical chapters and courses. The technical appendices allow students to concentrate on the substance during the chapters, and then to dive into the details of the implementation when it becomes necessary. In short, I think this book hits two targets that have not been hit yet: an intermediate-level book, written in Python.” (from Amazon)
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.