{"product_id":"bayesian-workflow-paperback","title":"Bayesian Workflow - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eAndrew Gelman\u003c\/b\u003e (Author), \u003cb\u003eAki Vehtari\u003c\/b\u003e (Author), \u003cb\u003eRichard McElreath\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eBayesian statistics and statistical practice have evolved over the years, driven by advancements in theory, methods, and computational tools. \u003cstrong\u003eBayesian Workflow\u003c\/strong\u003e explores the intricate workflows of applied Bayesian statistics, aiming to uncover the tacit knowledge often overlooked in published papers and textbooks. By systematizing the process of Bayesian model development, the book seeks to improve applied analyses and inspire future innovations in theory, methods, and software. It emphasizes the importance of iterative model building, model checking, computational troubleshooting, and simulated-data experimentation, offering a comprehensive perspective on statistical analysis.\u003c\/p\u003e\u003cp\u003eThrough detailed examples and practical guidance, the book bridges the gap between theory and application, empowering practitioners and researchers to navigate the complexities of Bayesian inference. It is not a checklist or cookbook but a flexible framework for understanding and resolving challenges in statistical modeling and decision-making under uncertainty.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFeatures\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eCovers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understanding\u003c\/li\u003e \u003cli\u003eDemonstrates iterative model development and computational problem-solving through real-world case studies\u003c\/li\u003e \u003cli\u003eExplores computational challenges, calibration checking, and connections between modeling and computation\u003c\/li\u003e \u003cli\u003eHighlights the importance of checking models under diverse conditions to understand their limitations and improve their robustness\u003c\/li\u003e \u003cli\u003eDiscusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learning\u003c\/li\u003e \u003cli\u003eIncludes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and Julia\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eThis book is designed for practitioners of applied Bayesian statistics, particularly users of probabilistic programming languages such as Stan, as well as developers of methods and software tailored to these users. It also targets researchers in Bayesian theory and methods, offering insights into understudied aspects of statistical workflows. Instructors and students will find adaptable exercises and case studies to enhance their learning experience. Beyond Bayesian inference, the book's principles are relevant to users of non-Bayesian methods, making it a valuable resource for statisticians, data scientists, and machine learning professionals seeking to improve their modeling and decision-making processes.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAndrew Gelman\u003c\/b\u003e is a professor of statistics and political science at Columbia University\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAki Vehtari\u003c\/b\u003e is a professor of computer science at Aalto University\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRichard McElreath\u003c\/b\u003e is the director of the Max Planck Institute for Evolutionary Anthropology\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDaniel Simpson\u003c\/b\u003e is a machine learning engineer at dottxt\u003c\/p\u003e\u003cp\u003e\u003cb\u003eCharles Margossian\u003c\/b\u003e is an assistant professor of statistics at the University of British Columbia\u003c\/p\u003e\u003cp\u003e\u003cb\u003eYuling Yao\u003c\/b\u003e is an assistant professor of statistics at the University of Texas\u003c\/p\u003e\u003cp\u003e\u003cb\u003eLauren Kennedy\u003c\/b\u003e is a senior lecturer in mathematical science at the University of Adelaide\u003c\/p\u003e\u003cp\u003e\u003cb\u003eJonah Gabry\u003c\/b\u003e is an applied statistics researcher at Columbia University\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePaul-Christian Bürkner\u003c\/b\u003e is a professor of statistics at TU Dortmund University\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMartin Modrák\u003c\/b\u003e is a researcher in bioinformatics at Charles University\u003c\/p\u003e\u003cp\u003e\u003cb\u003eVianey Leos Barajas\u003c\/b\u003e is an assistant professor of statistical sciences at the University of Toronto\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 538\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.12 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 26, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":48265275211997,"sku":"9780367490140","price":116.62,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0811\/9867\/8237\/files\/FSvwRrlfUq9780367490140.webp?v=1785166044","url":"https:\/\/handfulofbooks.com\/products\/bayesian-workflow-paperback","provider":"Handful of Books","version":"1.0","type":"link"}