Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

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 samplingExamples with R programming language and JAGS softwareComprehensive 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 planningR and JAGS computer programming code on websiteExercises have explicit purposes and guidelines for accomplishmentProvides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs

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Editorial Reviews

Review

This work is an accessible account of Bayesian data analysis starting from the basics and includes all new programs in JAGS and Stan designed to be easier to use than the script of the first edition. The reviews of MAA.

fills a gaping hole in what is currently available, and will serve to create its own market There is a professor. Michael Lee is a pres. There is a society for mathematicians.

The potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments is significant. There is a professor. The U. of Cal. Irvine has past pres. There is a society for mathematicians.

It’s better than others for a number of reasons. James L. (Jay) McClelland is a professor.

It’s the best introductory textbook on MCMC. J. is a mathematician.

There is a chance to change the methodological toolbox of a new generation of social scientists. J. of economics.

revolutionary British J is a mathematician.

Writing for real people with data. Readers will be excited about this topic from the very first chapter. PsycCritiques.

–This text refers to the hardcover edition.

Review

An introduction to data analysis.

–This text refers to the hardcover edition.

From the Back Cover

The explosion of interest in Bayesian statistics is due to the fact that it is now accessible to a wide audience. There is an accessible approach to Bayesian Data Analysis that is explained clearly with concrete examples. The basic concepts of probability and random sampling are included in the beginning of the book. All scenarios addressed by non-Bayesian textbooks are covered in the text. First year graduate students or advanced undergraduates should read this book. It bridges the gap between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of math.

–This text refers to the hardcover edition.

About the Author

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 from the University of California at Berkeley. At the Student Learning Center, Kruschke taught self-designed tutoring sessions for many math courses. He earned a doctorate in psychology from U.C. Berkeley after attending the Connectionist Models Summer School. He joined the Indiana University faculty in 1989. Professor Kruschke’s publications can be found on his website. His research focuses on moral psychology.

Professor Kruschke taught traditional statistical methods for many years until he reached a point in 2003 where 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 the 1st and 2nd editions of Doing Bayesian Data Analysis emerged.

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