Doing Bayesian Data Analysis
A Tutorial With R, Stan, brms, and the tidyverse
- 3rd Edition - April 1, 2027
- Latest edition
- Authors: John K. Kruschke, A. Solomon Kurz
- Language: English
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, realis… Read more
End of Summer Sale
Save up to 30% off
Bright savings for research, study, and discovery
Description
Description
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
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
Readership
Readership
First-year Graduate Students and Advanced Undergraduate Students in Statistics, Data Analysis, Psychology, Cognitive Science, Social Sciences, Clinical Sciences and Consumer Sciences in Business
Table of contents
Table of contents
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
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
Review quotes
Review quotes
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
"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
Product details
Product details
- Edition: 3
- Latest edition
- Published: April 1, 2027
- Language: English
About the authors
About the authors
JK
John K. Kruschke
John K. Kruschke is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics, at Indiana University in Bloomington, Indiana, USA. He is eight-time winner of Teaching Excellence Recognition Awards from Indiana University. He won the Troland Research Award from the National Academy of Sciences (USA), and the Remak Distinguished Scholar Award from Indiana University. He has been on the editorial boards of various scientific journals, including Psychological Review, the Journal of Experimental Psychology: General, and the Journal of Mathematical Psychology, among others.
After attending the Summer Science Program as a high school student and considering a career in astronomy, Kruschke earned a bachelor's degree in mathematics (with high distinction in general scholarship) from the University of California at Berkeley. As an undergraduate, Kruschke taught self-designed tutoring sessions for many math courses at the Student Learning Center. During graduate school he attended the 1988 Connectionist Models Summer School, and earned a doctorate in psychology also from U.C. Berkeley. He joined the faculty of Indiana University in 1989. Professor Kruschke's publications can be found at his Google Scholar page. His current research interests focus on moral psychology.
Professor Kruschke taught traditional statistical methods for many years until reaching a point, circa 2003, when he could no longer teach corrections for multiple comparisons with a clear conscience. The perils of p values provoked him to find a better way, and after only several thousand hours of relentless effort, the 1st and 2nd editions of Doing Bayesian Data Analysis emerged.
Affiliations and expertise
Professor of Psychological and Brain Sciences, Indiana University, Bloomington, USAAK
A. Solomon Kurz
A. Solomon Kurz is a research psychologist at the Veterans Integrated Services Networks 17 Center of Excellence (VISN 17 CoE). He earned his PhD from the University of Mississippi, and completed a postdoctoral fellowship at the CoE, after which he became a full-time researcher at the CoE as a specialist in statistics and research methodology. Kurz has released ebook translations of four statistics textbooks into R code using brms and the tidyverse. Kurz also produces a website where he regularly posts on topics such as Bayesian power analysis and causal inference with the GLM. At the VISN 17 CoE, Kurz has worked on projects from a variety of domains, such as psychometric evaluation, clinical program evaluation, randomized psychiatric trials, longitudinal panel designs, and experimental neuroscience. He serves as the primary statistics and research methods consultant at the VISN 17 CoE. Kurz has a secondary appointment as an associate professor in the Department of Applied Behavior Analysis at The Chicago School of Professional Psychology, Los Angeles, CA.
Affiliations and expertise
Research Psychologist, Veterans Integrated Services Networks 17 Center of Excellence, USA