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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.
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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Take 20% off your first order
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