Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / John K. Kruschke.

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.

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.