Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries

Key Features

• Conduct Bayesian data analysis with step-by-step guidance
• Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling
• Enhance your learning with best practices through sample problems and practice exercises

Book Description

The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.

In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.

By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.

What you will learn

• Build probabilistic models using PyMC and Bambi
• Analyze and interpret probabilistic models with ArviZ
• Acquire the skills to sanity-check models and modify them if necessary
• Build better models with prior and posterior predictive checks
• Learn the advantages and caveats of hierarchical models
• Compare models and choose between alternative ones
• Interpret results and apply your knowledge to real-world problems
• Explore common models from a unified probabilistic perspective
• Apply the Bayesian framework's flexibility for probabilistic thinking

Who this book is for

If you are a student, data scientist, researcher, or 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 scientific libraries like NumPy is expected.

Table of Contents

1. Thinking Probabilistically
2. Programming Probabilistically
3. Hierarchical Models
4. Modeling with Lines
5. Comparing Models
6. Modeling with Bambi
7. Mixture Models
8. Gaussian Processes
9. Bayesian Additive Regression Trees
10. Inference Engines
11. Where to Go Next

Review

“As we present this new edition of Bayesian Analysis with Python, it's essential to recognize the profound impact this book has had on advancing the growth and education of the probabilistic programming user community. Osvaldo Martin, a teacher, applied statistician, and long-time core PyMC developer, is the perfect guide to help readers navigate this complex landscape. He provides a clear, concise, and comprehensive introduction to Bayesian methods and the PyMC library. We trust that this book will be a valuable companion in your exploration of Bayesian modeling and a catalyst for your contributions to this dynamic field.”

Christopher Fonnesbeck, PyMC's original author

Thomas Wiecki, CEO & Founder of PyMC Labs

About the Author

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.