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

By:

Kruschke, John K

Material type: Text

TextLanguage: English Publication details: United States Academic Press 2014Edition: 2nd editionDescription: 776pISBN:

9780124058880

Subject(s):

DDC classification:

279.5 KRU 2014

Summary: 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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Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Book	Symbiosis International University, Dubai	279.5 KRU 2014 (Browse shelf(Opens below))	Available		SIU00131

Paperback / softback

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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