Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / John K. Kruschke.

Author/creator Kruschke, John K.
Format Book
EditionEdition 2.
PublicationBoston : Academic Press, 2015.
Descriptionxii, 759 pages : illustrations ; 25 cm
Subjects
Bayesian statistical decision theory.
R (Computer program language)

Contents What's in this book (Read this first!) -- Part I The basics: models, probability, Bayes' rule and r: Introduction: credibility, models, and parameters; The R programming language; What is this stuff called probability?; Bayes' rule -- Part II All the fundamentals applied to inferring a binomila probability: Inferring a binomial probability via exact mathematical analysis; Markov chain Monte Carlo; JAGS; Hierarchical models; Model comparison and hierarchical modeling; Null hypothesis significance testing; Bayesian approaches to testing a point ("Null") hypothesis; Goals, power, and sample size; Stan -- Part III The generalized linear model: Overview of the generalized linear model; 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; Metric predicted variable with multiple nominal predictors; Dichotomous predicted variable; Nominal predicted variable; Ordinal predicted variable; Count predicted variable; Tools in the trunk -- Bibliography -- Index.
Abstract Provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data.
Bibliography noteIncludes bibliographical references (pages 737-745).
LCCN 2014011293
ISBN9780124058880 (hbk.)
ISBN0124058884 (hbk.)

Availability

Library Location Call Number Status Item Actions
Joyner General Stacks QA279.5 .K79 2015 ✔ Available Place Hold