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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
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Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan Hardback - 2014 - 2nd Edition

by Kruschke, John

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Academic Press. 2. Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Details

  • Title Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
  • Author Kruschke, John
  • Binding Hardback
  • Edition number 2nd
  • Edition 2
  • Condition Used - Very good
  • Pages 776
  • Volumes 1
  • Language ENG
  • Publisher Academic Press
  • Publication date 2014-11
  • Illustrated Yes
  • Features Bibliography, Illustrated
  • Bookseller's Inventory # 0124058884-11-1
  • ISBN 9780124058880 / 0124058884
  • Weight 3.9 lbs (1.77 kg)
  • Dimensions 9.3 x 7.8 x 1.7 in (23.62 x 19.81 x 4.32 cm)
  • Category Mathematics
  • Library of Congress subjects Bayesian statistical decision theory, R (Computer program language)
  • Library of Congress Catalogue Number 2014011293
  • Dewey Decimal Code 519.54
  • Quantity available 2

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From the publisher

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.

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