DOING BAYESIAN DATA ANALYSIS 3E

A Tutorial With R, Stan, brms, and the tidyverse
3. Auflage 2027
ISBN: 978-0-443-30112-4
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 235 mm

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.

Autoren/Hrsg.

Kruschke, John K.

Kurz, A. Solomon

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung

Inhaltsverzeichnis

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

Über Autor(innen)

Kruschke, John K.
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.

Kurz, A. Solomon
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.