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Intl. Ed.

Doing Bayesian Data Analysis: A Tutorial with R and BUGS

Intl. Ed.

Doing Bayesian Data Analysis: A Tutorial with R and BUGS Papercover - 2014 - 2nd Edition

by KRUSCHKE, JOHN

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***INTERNATIONAL EDITION*** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments contain tracking numbers. Great professional textbook selling experience and expedite shipping service.

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Details

  • Title Doing Bayesian Data Analysis: A Tutorial with R and BUGS
  • Author KRUSCHKE, JOHN
  • Binding Paperback
  • Edition number 2nd
  • Edition Notice: 1st edition
  • Condition New
  • Pages 776
  • Volumes 1
  • Language ENG
  • Publisher John Kruschke
  • Publication date 2014-11
  • Illustrated Yes
  • Features Bibliography, Illustrated
  • Bookseller's Inventory # MIG 732EU
  • 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 28

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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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