Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / John Kruschke.
Material type: Computer filePublisher: San Diego, CA : Academic Press, 2014Edition: 2E [2nd edition]Description: electronic textISBN:- 9780124059160
- 519.5/42 23
- QA279.5 .K79 2014
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|
Book | Kozhikode | 519.226 KRU/D (Browse shelf(Opens below)) | Available | IIMKO-33990 |
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.
Includes bibliographical references (p.737-745).
eBook access via Internet. ANU/ANV.
There are no comments on this title.