Bayesian Workflow
Andrew Gelman
Sold by THE SAINT BOOKSTORE, Southport, United Kingdom
AbeBooks Seller since 14 June 2006
New - Soft cover
Condition: New
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Add to basketSold by THE SAINT BOOKSTORE, Southport, United Kingdom
AbeBooks Seller since 14 June 2006
Condition: New
Quantity: Over 20 available
Add to basketThis item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Seller Inventory # C9780367490140
Bayesian statistics and statistical practice have evolved over the years, driven by advancements in theory, methods, and computational tools. Bayesian Workflow 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.
Through 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.
Features
This 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.
Andrew Gelman is a professor of statistics and political science at Columbia University
Aki Vehtari is a professor of computer science at Aalto University
Richard McElreath is the director of the Max Planck Institute for Evolutionary Anthropology
Daniel Simpson is a machine learning engineer at dottxt
Charles Margossian is an assistant professor of statistics at the University of British Columbia
Yuling Yao is an assistant professor of statistics at the University of Texas
Lauren Kennedy is a senior lecturer in mathematical science at the University of Adelaide
Jonah Gabry is an applied statistics researcher at Columbia University
Paul-Christian Bürkner is a professor of statistics at TU Dortmund University
Martin Modrák is a researcher in bioinformatics at Charles University
Vianey Leos Barajas is an assistant professor of statistical sciences at the University of Toronto
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