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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

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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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

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Frequently bought together

$49.99

Transformers for Natural Language Processing and Computer Vision

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Total $ 157.97

RP Aug 13, 2024

If you had to buy just one book on Bayesian analysis, this is the one to get. It takes a lot of skill to write a concise, readable book on such a complicated topic.

Amazon Verified review

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.

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Banachan Mar 06, 2024

This is a great practical guide to probabilistic modeling using Python, especially for those interested in or working with Bayesian data analysis. It covers a broad range of topics, from basic concepts to more advanced modeling techniques, so I can see it being an invaluable resource to both practitioners and students.The book provides a thorough exploration, specifically on addressing Bayesian analysis techniques. It's quite an enjoyable and comprehensive guide for both beginners and advanced practitioners. Good emphasis on fundamentals, then transitions to more complex concepts and application. I thought it provided a good balance between theory and practical skills.Summary of pros are that it encourages hands-on learning, with wide coverage on the subject. Doesn't hurt that it's an updated edition with up-to-date approaches. Some of the cons are that it might be complex in some sections for pure beginners. Code examples are mostly in Python (so I guess this could be a con or pro depending on how look at it).I still like that it has a lot of examples, code snippets, and real-world scenarios included, with good explanations. Covers model construction, prior selection, model comparison, to predictive analysis. I surmise that this allows for a variety of learning needs for most folks. Author style is both authoritative and accessible. I would recommend this book for academia, professional development, or just for personal interest in data science.

Amazon Verified review

Nicole M Radziwill May 07, 2024

While Python is my go-to language for things like NLP, I usually use R for everything else. After spending a solid long weekend with Martin's new book "Bayesian Analysis with Python" I can confirm that this book will be just what ONE audience needs, but may disappoint others. As a gentle introduction to Bayesian approaches for people who are well versed in intro statistics and have a solid foundation in Python, it's perfect. But if you're missing that mathematical statistics background (or if you're rusty on Python) this book may present a struggle.As a result, this is five stars for the target audience and four for the other audiences.The writing is clear and easy to follow, but sometimes encourages you to "review the code for understanding" where the text could have explained each of the lines of code in sequence. The book also assumes that the reader has a fundamental understanding of distributions and mathematical notation, which may not be the case for all programmers or data analysts. As a professor this would have been a great book to use from an introductory Bayesian methods course for juniors or seniors in STEM with at least one or two semesters of Python. For this group, the book is particularly strong, because it takes a computation-first approach but fills in the gaps with just enough theory.Highlights include:- There is a simple discussion on ROPE and loss functions that is valuable- There is a good discussion about how to do linear regression the Bayesian way (hint: all parameters treated as priors)- Some interesting mixture models using the Palmer Penguins dataset- The best part was the MCMC with Metropolis-Hastings to calculate the value of piDO buy this book if you have a solid foundation in Python (and a Python environment already set up) and want to spend a few weeks (or a couple months) expanding your understanding into building and running simple Bayesian models. If you have the time to spend, this will deepen your understanding.DO NOT buy this book if you are a programmer who needs to start building Bayesian models at work within the next couple days! It's not going to help you work that next ticket in the queue.

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.

Amazon Verified review

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

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

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