一般注記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.