Bayesian Analysis with Python Third Edition
This third edition of Bayesian Analysis with Python provides a comprehensive and practical introduction to Bayesian modeling using the PyMC library. Authored by Osvaldo Martin, a core PyMC developer and applied statistician, the book guides readers through probabilistic programming with real-world examples. It covers key topics such as expressing problems as probabilistic programs, model validation, and model comparison, updated to reflect the latest methodological improvements and the expanded PyMC ecosystem, including new libraries like Bambi, Kulprit, PreliZ, and PyMC-BART.
The book is designed for data scientists and analysts who want to apply Bayesian methods in their work. It starts with foundational concepts and progresses to advanced techniques, emphasizing a sound workflow and best practices. Readers will learn how to build, fit, and evaluate Bayesian models using Python, leveraging modern computational tools for efficient inference.
With clear explanations and hands-on examples, this edition serves as both a tutorial and a reference for probabilistic programming. It reflects the evolution of Bayesian analysis from a niche methodology to a mainstream approach in science and industry, making it an essential resource for anyone looking to harness the power of Bayesian statistics with Python.
