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.

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