TY - BOOK AU - Kruschke,John K. TI - Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan SN - 9780124058880 (hbk) U1 - 000SA.161 23 PY - 2015/// CY - Boston : PB - Academic Press, KW - Bayesian statistical decision theory KW - R (Computer program language) N1 - Includes bibliographical references and index; 1. What's in this book (Read this first!) -- Part I The basics: models, probability, Bayes' rule and r: 2. Introduction: credibility, models, and parameters; 3. The R programming language; 4. What is this stuff called probability?; 5. Bayes' rule -- Part II All the fundamentals applied to inferring a binomila probability: 6. Inferring a binomial probability via exact mathematical analysis; 7. Markov chain Monte Carlo; JAGS; 8. JAGS; 9. Hierarchical models; 10. Model comparison and hierarchical modeling; 11. Null hypothesis significance testing; 12. Bayesian approaches to testing a point ("Null") hypothesis; 13. Goals, power, and sample size; Stan -- Part III The generalized linear model: 14. Stan; 15. Overview of the generalized linear model; 16. Metric-predicted variable on one or two groups; 17. Metric predicted variable with one metric predictor; 18. Metric predicted variable with multiple metric predictors; 19. Metric predicted variable with one nominal predictor; 20. Metric predicted variable with multiple nominal predictors; 21. Dichotomous predicted variable; 22. Nominal predicted variable; 23. Ordinal predicted variable; 24. Count predicted variable; 25. Tools in the trunk -- Bibliography -- Index N2 - 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 ER -