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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

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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.

Who is this book 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.

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

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Jon Barley Nov 08, 2024

An excellent introduction into practical Bayesian analysis with many illuminating examples.

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ben Jun 18, 2024

Osvaldo Martin’s “Bayesian Analysis with Python” is an exceptional resource for anyone looking to delve into the world of Bayesian inference using Python. The book is tailored for readers who possess a basic understanding of Python but may not have extensive knowledge of statistics or Bayesian methods. This accessibility makes it an ideal starting point for beginners while still offering depth for more experienced readers.One of the book’s standout features is its practical approach. Each chapter concludes with exercises that reinforce the concepts covered, and there is even a dedicated Discord space provided by the publisher for further discussion and learning. The introductory chapters lay a strong foundation in both theoretical and computational aspects of Bayesian inference, with “Thinking Probabilistically” and “Programming Probabilistically” offering a seamless blend of theory and hands-on coding with PyMC, one of the leading probabilistic programming languages.For those new to the subject, reading the first two chapters in tandem can be particularly beneficial, combining conceptual understanding with computational implementation. Subsequent chapters delve into specific modeling approaches, such as hierarchical models, generalized linear models, mixture models, Gaussian processes, and Bayesian adaptive regression trees (BART). Each of these chapters provides valuable insights and practical knowledge that can be directly applied to real-world problems.The book also covers essential topics in practice, including model comparison and evaluation, which are crucial for any data scientist. The chapter on Bambi is especially noteworthy, demonstrating how formula syntax can be used to efficiently build PyMC models, accompanied by clear visual representations of the models using Graphviz.Additionally, the chapter on inference engines serves as a comprehensive reference for understanding the mechanics behind Bayesian samplers and inference methods, making it a valuable resource for both teaching and practical application.Overall, “Bayesian Analysis with Python” is an excellent book for anyone interested in Bayesian inference. It successfully bridges the gap between core concepts and practical implementation, and it does so using the robust Bayesian “tech stack” of PyMC, ArviZ, and Bambi. The book also provides an excellent list of further resources, including books, code repositories, paid courses, and open-source community hangouts, making it a well-rounded and highly recommended read for aspiring Bayesian analysts.

Amazon Verified review

Thomas M. Feb 25, 2024

The 3rd edition of Bayesian Analysis with Python is a major step up compared to previous editions and IMHO is a must read for any practicing data scientist looking to apply the latest advancements in probabilistic modeling. For anyone familiar with the PyMC community, Osvaldo Martin is a well respected advocate and developer of the framework. He not only advances the field in making PyMC more useful, but also possesses a gift for pedagogy. This book is a comprehensive and accessible guide to Bayesian methods and probabilistic programming with Python. The book is well-written and engaging, and it covers a wide range of topics, from the basics of Bayesian statistics to advanced topics such as Markov Chain Monte Carlo sampling and Bayesian model selection. Martin does an excellent job of explaining complex concepts in a clear and concise manner, and he provides plenty of real-world examples to illustrate the practical application of Bayesian methods.One of the things that I appreciate most about this book is the way that Martin emphasizes the practical aspects of Bayesian modeling. He discusses how to develop and evaluate Bayesian models, and he provides a wealth of tips and tricks for working with PyMC. The book includes an extensive collection of exercises, which are a great way for readers to test their understanding of the material.Another strength of the book is its coverage of the latest advances in the field. Martin discusses new developments in MCMC sampling, Bayesian model selection, and other areas. In particular, we see the traditional stats and ML worlds converging, which Martin captures in his discussion of Bayesian Additive Regression Trees and related topics. This makes the book a valuable reference for practitioners who are interested in staying up-to-date on the latest developments in the field.Overall, Bayesian Analysis with Python is an excellent read for anyone who wants to learn about probabilistic programming. The book is well-written, engaging, and comprehensive, and it covers a wide range of topics, from the basics of Bayesian statistics to advanced topics. I highly recommend this book to anyone who is interested in learning more about Bayesian methods and probabilistic programming.

Orduz Feb 23, 2024

I had the privilege to review the latest edition of the great book: "Bayesian Analysis with Python - Third Edition: A Practical Guide to Probabilistic Modeling" by Osvaldo Martin. I can only recommend this book to anyone interested in modern Bayesian practical methods with PyMC.

Roberto Williams Batista Jun 06, 2024

The book "Bayesian Analysis with Python" excels at covering a foundational subject in the field of machine learning. It strikes a perfect balance by providing sufficient theory for readers to grasp the essential concepts, enabling them to explore further in-depth. Osvaldo's illustrations, mathematical references, citations, and exercises are particularly commendable, as such thoroughness is often rare in technical publications.One chapter is entirely dedicated to explaining BART (Bayesian Additive Regression Trees) in detail, showcasing its great potential applications. This updated material is highly beneficial for academic use.

Bayesian Analysis with Python: A practical guide to probabilistic modeling , Third Edition

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