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

by: Osvaldo Martin (Author)

Publisher: Packt Publishing

Publication Date: 31 Jan. 2024

Language: English

Print Length: 394 pages

ISBN-10: 1805127160

ISBN-13: 9781805127161

Book Description

Lea 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 librariesKey FeaturesConduct Bayesian data analysis with step-by-step guidanceGain insight into a mode, practical, and computational approach to Bayesian statistical modelingEnhance your leaing with best practices through sample problems and practice exercisesPurchase of the print or Kindle book includes a free PDF eBook.Book DescriptionThe 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 leaing jouey 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 leaBuild probabilistic models using PyMC and BambiAnalyze and interpret probabilistic models with ArviZAcquire the skills to sanity-check models and modify them if necessaryBuild better models with prior and posterior predictive checksLea the advantages and caveats of hierarchical modelsCompare models and choose between alteative onesInterpret results and apply your knowledge to real-world problemsExplore common models from a unified probabilistic perspectiveApply the Bayesian framework's flexibility for probabilistic thinkingWho this book is forIf 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 ContentsThinking ProbabilisticallyProgramming ProbabilisticallyHierarchical ModelsModeling with LinesComparing ModelsModeling with BambiMixture ModelsGaussian ProcessesBayesian Additive Regression TreesInference EnginesWhere to Go Next