Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets.
The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment.
This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.
By:
John Kruschke (Indiana University Bloomington USA)
Imprint: Academic Press Inc
Country of Publication: United States
Edition: 2nd Revised edition
Dimensions:
Height: 234mm,
Width: 191mm,
Spine: 43mm
Weight: 1.740kg
ISBN: 9780124058880
ISBN 10: 0124058884
Pages: 776
Publication Date: 03 November 2014
Audience:
College/higher education
,
A / AS level
Format: Hardback
Publisher's Status: Active
"1. What’s in This Book (Read This First!) PART I The Basics: Models, Probability, Bayes’ Rule, and R 2. Introduction: Credibility, Models, and Parameters 3. The R Programming Language 4. What Is This Stuff Called Probability? 5. Bayes’ Rule PART II All the Fundamentals Applied to Inferring a Binomial Probability 6. Inferring a Binomial Probability via Exact Mathematical Analysis 7. Markov Chain Monte Carlo 8. JAGS 9. Hierarchical Models 10. Model Comparison and Hierarchical Modeling 11. Null Hypothesis Significance Testing 12. Bayesian Approaches to Testing a Point (""Null"") Hypothesis 13. Goals, Power, and Sample Size 14. Stan PART III The Generalized Linear Model 15. Overview of the Generalized Linear Model 16. Metric-Predicted Variable on One or Two Groups 17. Metric Predicted Variable with One Metric Predictor 18. Metric Predicted Variable with Multiple Metric Predictors 19. Metric Predicted Variable with One Nominal Predictor 20. Metric Predicted Variable with Multiple Nominal Predictors 21. Dichotomous Predicted Variable 22. Nominal Predicted Variable 23. Ordinal Predicted Variable 24. Count Predicted Variable 25. Tools in the Trunk"
Reviews for Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
I think it fills a gaping hole in what is currently available, and will serve to create its own market as researchers and their students transition towards the routine application of Bayesian statistical methods. -Prof. Michael lee, University of California, Irvine, and president of the Society for Mathematical Psychology Kruschke's text covers a much broader range of traditional experimental designs...has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments -Prof. Geoffrey Iverson, University of California, Irvine, and past president of the Society for Mathematical Psychology John Kruschke has written a book on Statistics. It's better than others for reasons stylistic. It also is better because itis Bayesian. To find out why, buy it -- it's truly amazin'! -James L. (Jay) McClelland, Lucie Stern Professor & Chair, Dept. Of Psychology, Standford University
