{"product_id":"bayesian-analysis-with-python-third-edition-a-practical-guide-to-probabilistic-modeling-paperback","title":"Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling - Paperback","description":"\u003cp\u003eby \u003cb\u003eOsvaldo Martin\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eLearn 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\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eConduct Bayesian data analysis with step-by-step guidance\u003c\/li\u003e\n\u003cli\u003eGain insight into a modern, practical, and computational approach to Bayesian statistical modeling\u003c\/li\u003e\n\u003cli\u003eEnhance your learning with best practices through sample problems and practice exercises\u003c\/li\u003e\n\u003cli\u003ePurchase of the print or Kindle book includes a free PDF eBook.\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003eIn 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.\u003c\/p\u003e\u003cp\u003eBy 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.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eBuild probabilistic models using PyMC and Bambi\u003c\/li\u003e\n\u003cli\u003eAnalyze and interpret probabilistic models with ArviZ\u003c\/li\u003e\n\u003cli\u003eAcquire the skills to sanity-check models and modify them if necessary\u003c\/li\u003e\n\u003cli\u003eBuild better models with prior and posterior predictive checks\u003c\/li\u003e\n\u003cli\u003eLearn the advantages and caveats of hierarchical models\u003c\/li\u003e\n\u003cli\u003eCompare models and choose between alternative ones\u003c\/li\u003e\n\u003cli\u003eInterpret results and apply your knowledge to real-world problems\u003c\/li\u003e\n\u003cli\u003eExplore common models from a unified probabilistic perspective\u003c\/li\u003e\n\u003cli\u003eApply the Bayesian framework's flexibility for probabilistic thinking\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf 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.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eThinking Probabilistically\u003c\/li\u003e\n\u003cli\u003eProgramming Probabilistically\u003c\/li\u003e\n\u003cli\u003eHierarchical Models\u003c\/li\u003e\n\u003cli\u003eModeling with Lines\u003c\/li\u003e\n\u003cli\u003eComparing Models\u003c\/li\u003e\n\u003cli\u003eModeling with Bambi\u003c\/li\u003e\n\u003cli\u003eMixture Models\u003c\/li\u003e\n\u003cli\u003eGaussian Processes\u003c\/li\u003e\n\u003cli\u003eBayesian Additive Regression Trees\u003c\/li\u003e\n\u003cli\u003eInference Engines\u003c\/li\u003e\n\u003cli\u003eWhere to Go Next\u003c\/li\u003e\n\u003c\/ol\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 394\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.81 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 31, 2024\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42704110125119,"sku":"9781805127161","price":86.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/26c670c5e7b4ec678d5968df9549cdcc.webp?v=1765032060","url":"https:\/\/dhlswag.com\/products\/bayesian-analysis-with-python-third-edition-a-practical-guide-to-probabilistic-modeling-paperback","provider":"BBB","version":"1.0","type":"link"}