Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python provides a hands-on approach to learning Bayesian inference, covering topics such as exploratory analysis, linear regressions, splines, time series, and more. It is written by contributors from PyMC3, ArviZ, Bambi, and Tensorflow Probability and includes case studies and a reference chapter.

Format: Hardback
Length: 398 pages
Publication date: 29 December 2021
Publisher: Taylor & Francis Ltd

Bayesian Modeling and Computation in Python is a comprehensive guide designed to assist beginner Bayesian practitioners in advancing their skills to become intermediate modelers. It adopts a hands-on approach, utilizing popular libraries such as PyMC3, Tensorflow Probability, ArviZ, and others, to focus on practical applications of statistics with references to the underlying mathematical theory.

The book begins by providing a refresher on key Bayesian Inference concepts, laying the foundation for the subsequent chapters. In the second chapter, modern methods for Exploratory Analysis of Bayesian Models are introduced, enabling readers to gain insights into the analysis of complex Bayesian models.

With a solid understanding of these fundamentals, the subsequent chapters delve into various models, including linear regressions, splines, time series, Bayesian additive regression trees, and more. Each chapter offers detailed explanations and examples, helping readers to apply Bayesian modeling in diverse settings.

Approximate Bayesian Computation, end-to-end case studies, and a chapter on the internals of probabilistic programming languages are included in the final chapters. These chapters provide practical insights into applying Bayesian modeling in real-world scenarios and offer a deeper understanding of the mathematical aspects of probabilistic programming.

The book is authored by contributors from PyMC3, ArviZ, Bambi, and Tensorflow Probability, among other prominent libraries. Their expertise and contributions ensure that the content is up-to-date and relevant to the field of Bayesian modeling and computation.

Whether you are a data scientist, statistician, or researcher seeking to enhance your understanding of Bayesian inference and apply it to real-world problems, Bayesian Modeling and Computation in Python is an invaluable resource. With its comprehensive coverage, practical examples, and extensive references, this book will empower you to become a proficient Bayesian modeler and make informed decisions based on data.

Weight: 1010g
Dimension: 182 x 366 x 27 (mm)
ISBN-13: 9780367894368