a tutorial with R, JAGS, and Stan Kruschke, John K. creator text bibliography Electronic books. ne 2011 2nd ed. monographic eng xii, 759 p. : ill. (some col.) ; 24 cm. 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. John Kruschke. Includes index. Bibliography : p. 737-745. Bayesian statistical decision theory R (Computer program language) QA279.5 519.542 KRD London, UK ; San Diego, CA : Academic Press, [2015] Edition 2. (DLC) 2014011293 (OCoLC)897342420 9780124058880 (hbk) http://www.sciencedirect.com/science/book/9780124058880 http://www.sciencedirect.com/science/book/9780124058880 UKMGB 140821 20210621113420.0 ocn896850630 eng