Bayesian modeling provides an elegant approach to many data science and decision-making problems. However, it can be hard to make it work well in practice. In particular, although there are many software packages that make it easy to specify complex hierarchical models such as Stan, PyMC3, TensorFlow Probability (TFP), and Pyro, users still need additional tools to diagnose whether the results of their computations are correct or not. They may also need advice on what to do when things do go wrong.

This book focuses on the ArviZ library, which enables users to perform exploratory analysis of Bayesian models, for example, diagnostics of posterior samples generated by any inference method. This can be used to diagnose a variety of failure modes in Bayesian inference. The book also discusses various modeling strategies (such as centering) that can be employed to eliminate many of the most common problems. Most of the examples in the book use PyMC3, although some also use TFP; a brief comparison of other probabilistic programming languages is also included.

The authors are all experts in the area of Bayesian software and are major contributors to the PyMC3, ArviZ, and TFP libraries. They also have significant experience applying Bayesian data analysis in practice, and this is reflected in the practical approach adopted in this book. Overall, I think this is a valuable addition to the literature, which should hopefully further the adoption of Bayesian methods.

—Kevin P. Murphy