Kruschke, John K.
Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / John Kruschke. - 2nd ed. - xii, 759 p. : ill. (some col.) ; 24 cm.
Includes index.
Bibliography : p. 737-745.
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.
9780124058880 (hbk)
GBB4C8028 bnb GBB4C5542 bnb GBB753606 bnb
016941136 Uk 016938650 Uk 017990610 Uk
Bayesian statistical decision theory.
R (Computer program language).
Electronic books.
QA279.5
519.542 / KRD
Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / John Kruschke. - 2nd ed. - xii, 759 p. : ill. (some col.) ; 24 cm.
Includes index.
Bibliography : p. 737-745.
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.
9780124058880 (hbk)
GBB4C8028 bnb GBB4C5542 bnb GBB753606 bnb
016941136 Uk 016938650 Uk 017990610 Uk
Bayesian statistical decision theory.
R (Computer program language).
Electronic books.
QA279.5
519.542 / KRD