Bayesian Workflow

Part 1: From Bayesian inference to Bayesian workflow

1 Bayesian theory and Bayesian practice

2 Statistical modeling and workflow

3 Computational tools

4 Introduction to workflow: Modeling performance on a multiple choice exam

Part 2: Statistical workflow

5 Building statistical models

6 Using simulations to capture uncertainty

7 Prediction, generalization, and causal inference

8 Visualizing and checking fitted models

9 Comparing and improving models

10 Statistical inference and scientific inference

Part 3: Computational workflow

11 Fitting statistical models

12 Diagnosing and fixing problems with fitting

13 Approximate algorithms and approximate models

14 Simulation-based calibration checking

15 Statistical modeling as software development

Part 4: Case studies

16 Coding a series of models: Simulated data of movie ratings

17 Prior specification for regression models: Reanalysis of a sleep study

18 Predictive model checking and comparison: Clinical trial

19 Building up to a hierarchical model: Coronavirus testing

20 Using a fitted model for decision analysis: Mixture model for time series competition

21 Posterior predictive checking: Stochastic learning in dogs

22 Incremental development and testing: Black cat adoptions

23 Debugging a model: World Cup football

24 Leave-one-out cross validation model checking and comparison: Roaches

25 Model building and expansion: Golf putting

26 Model building with latent variables: Markov models for animal movement

27 Model building: Time-series decomposition for birthdays

28 Models for regression coefficients and variable selection: Student grades

29 Funnel problem with latent variables: No vehicles in the park

30 Computational challenge of multimodality: Differential equation for planetary motion

31 Simulation-based calibration checking in model development workflow

Biography

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

“An outstanding, protocol-driven guide for Bayesian data analysis, Bayesian Workflow by Gelman, Vehtari, McElreath and co-authors delivers a practical and comprehensive framework for iterative modeling, emphasizing simulation, diagnostic checks, and rigorous empirical validation, and with a long and impressive list of case studies. By treating data analysis as a structured, verifiable workflow, it provides an indispensable toolkit for diagnosing model failures, refining priors, and building reliable data analysis systems for reproducible conclusions, useful for beginning and veteran data analysts alike.”
~ Bin Yu, CDSS Chancellor’s Distinguished Professor of Statistics, Electrical Engineering and Computer Sciences, and Center for Computational Biology, UC Berkeley, USA

“This is not a typical methods textbook, but instead it guides the reader through the whole process of fitting, critiquing and adapting statistical models to real-world problems.  It is full of the accumulated wisdom of skilled practitioners, teaching through demonstration rather than theory, with both basic and highly sophisticated examples. I strongly recommend this book to statisticians who really want to understand what they can learn from their data.”
~ Sir David Spiegelhalter, University of Cambridge, UK

"A bravura performance...Gelman, Vehtari, McElreath and friends develop in detail a practical Bayesian data analysis workflow, from acquisition to final report, including full computational guidance.”
~Brad Efron, Stanford University, USA

"This original, thought-provoking, and transformative book is much much more than an implementation manual for Bayesian Data Analysis, even though it shares almost the same perspective. (The first sentence of the book states that the authors' "conceptions of statistical practice, and of Bayesian statistics, have changed over the years".) By providing a modus vivendi for undertaking Bayesian modelling from scratch in realistic settings where models are not magicked out of the blue, the authors explicit and rationalise the many steps required by such a bottom-up modelling protocol ("not a checklist, not a cookbook", and not a flowchart!) in real situations. The contents read very well and very smoothly, with a seamless conjunction of intuition, modelling advices, computational details, and comparison tools. While unsurprisingly Bayesian, the perspective adopted therein remains both open and inclusive, with a welcome humility about the limitations and challenges of Bayesian workflows. This book should thus appeal to and profit a wide variety of readers, as providing guidance through an extensive collection of highly detailed examples, with shared code and exercises.”
~Christian P. Robert, Université Paris Dauphine PSL, Paris, France

“Some statistics books show you how to beat an egg, others are recipe books: if this, then that style. This book teaches you how to cook. Written by authors who established so much of how we do Bayesian statistics, this new book is an indispensable guide for analyzing data in a trustworthy way. It walks you through the actual steps involved in building models to explore and understand datasets. Part 4 is particularly excellent – the authors provide many end-to-end case studies that will be useful for both practitioners and students. It highlights the value of their workflow-based approach. Filled with chatty asides, the book introduces the Bayesian workflow to a broad audience. It embraces the frustrations and complexities of actually doing Bayesian statistics and provides specific guidance throughout. Each chapter contains exercises and it could be the basis of an upper-year undergraduate course, or a first-year grad course, in applied statistics. It will be used for many years to come.”
~Rohan Alexander, University of Toronto, Canada