John Kruschke

Doing Bayesian Data Analysis (eBook, ePUB)

A Tutorial with R, JAGS, and Stan

• Format: ePub

Produktbeschreibung

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. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and JAGS software - Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) - Coverage of experiment planning - R and JAGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment - Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 776
• Erscheinungstermin: 11. November 2014
• Englisch
• ISBN-13: 9780124059160
• Artikelnr.: 41868910

Autorenporträt

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.

Inhaltsangabe

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

Rezensionen

"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