Bayesian Analysis with Python

Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, like PyMC, ArviZ, Bambi and others, from one of its contributors and seasoned Bayesian modelers. The third edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC, a state-of-the-art probabilistic programming library, and ArviZ, a library for exploratory analysis of Bayesian models. Additionally, readers will also become familiar with other Bayesian libraries such as Bambi, PreliZ, and Kulprit. This fully updated edition includes a new short introduction to concepts from probability theory, making your learning journey even smoother. There are a few new topics, such as Bayesian additive regression trees (BART), variable selection, and prior elicitation, along with updated examples. Many of the explanations have been improved based on the feedback and experience from previous editions. As you progress, you’ll learn about Bayesian statistics with a strong practical and computational approach. Synthetic and real data sets will introduce you to several types of models, such as hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes and BART. By the end of this book, you will have a working knowledge of probabilistic modeling and be able to design and implement Bayesian models for your own data science problems, ready to tackle more advanced material or specialized statistical modeling if you need to. If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.