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DOING BAYESIAN DATA ANALYSIS 3E,

Edition 3 A Tutorial With R, Stan, brms, and the tidyverse

Editors: By John K. Kruschke and A. Solomon Kurz

Publication Date: 01 Apr 2027

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Doing Bayesian Data Analysis: A Tutorial with R, Stan, brms, and the tidyverse, Third Edition, provides a carefully scaffolded tutorial from beginning concepts to advanced, realistic data analyses. The book uses a proven sequence of topics unique to Doing Bayesian Data Analysis. The first part covers foundational concepts of statistical models, probability, Bayesian reasoning, and computer programming in R and the tidyverse. The second part introduces all the concepts and methods of Bayesian data analysis, including the Stan modeling language, by using the simplest possible data structures and statistical models. The third part covers the generalized linear model, including multilevel (a.k.a. hierarchical) versions of regression and analysis of variance, for a variety of data types (metric, ordinal, nominal, dichotomous, count), using the convenient computer package called brms. Every concept and case is illustrated with detailed examples, and all computer code is available at the book’s website. This book is intended for self-learners or classroom learning, for first-year graduate students, advanced undergraduates, and professionals. The methods apply to any field, including social sciences, biological sciences, and physical sciences, for any setting, including academia, government, business, and industry.

Key Features

Accessible to beginners, introducing basic concepts of probability and computer programming
Carefully scaffolds to advanced models for realistic data analysis, using a proven progression of topics unique to Doing Bayesian Data Analysis
Numerous complete examples with the computer software R, Stan, brms, and the tidyverse
Comprehensive coverage of the generalized linear model, including multilevel (a.k.a. hierarchical) versions of regression and traditional analysis of variance
Coverage of experiment sample-size planning (analogous to traditional power analysis) and model-comparison techniques
Examples abide by the Bayesian Analysis Reporting Guidelines

About the author

By John K. Kruschke, Professor of Psychological and Brain Sciences, Indiana University, Bloomington, USA and A. Solomon Kurz, Research Psychologist, Veterans Integrated Services Networks 17 Center of Excellence, USA

1. What’s In This Book Part I
2. Intro to Bayesian Concepts
3. R
4. Probability
5. Bayes’ Rule Part II
6. Binomial Probability via Math
7. MCMC
8. JAGS
9. Hierarchical / Multi-Level Models
10. Model Comparison
11. Frequentist Null-Significance Testing
12. Bayesian Model Comparison and Hypothesis Testing
13. Goals, Power, Sample Size
14. Stan Part III
15. The Generalized Linear Model
16. Y Metric, X One or Two Groups
17. Y Metric, X Single Metric
18. Y Metric, X Multiple Metric
19. Y Metric, X Single Nominal
20. Y Metric, X Multiple Nominal
21. Y Dichotmous (Logistic Regression)
22. Y Nominal
23. Y Ordinal
24. Y Count
25. Tools

Title Reviews

Review of the previous edition:
"Both textbook and practical guide, this work is an accessible account of Bayesian data analysis starting from the basics…This edition is truly an expanded work and includes all new programs in JAGS and Stan designed to be easier to use than the scripts of the first edition, including when running the programs on your own data sets."—MAA Reviews

"fills a gaping hole in what is currently available, and will serve to create its own market"—Prof. Michael Lee, U. of Cal., Irvine; pres. Society for Mathematical Psych

"has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments"—Prof. Geoffrey Iverson, U. of Cal., Irvine; past pres. Society for Mathematical Psych.

"better than others for reasons stylistic....buy it — it’s truly amazin’!"—James L. (Jay) McClelland, Lucie Stern Prof. & Chair, Dept. of Psych., Stanford U.

"the best introductory textbook on Bayesian MCMC techniques"—J. of Mathematical Psych.

"potential to change the methodological toolbox of a new generation of social scientists"—J. of Economic Psych.

"revolutionary"—British J. of Mathematical and Statistical Psych.

"writing for real people with real data. From the very first chapter, the engaging writing style will get readers excited about this topic"—PsycCritiques

DOING BAYESIAN DATA ANALYSIS 3E,

Editors: By John K. Kruschke and A. Solomon Kurz

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About the author

Title Reviews

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