Doing bayesian data analysis : a tutorial with R and BUGS / John K. Kruschke.

CONTENTS

Chapter 1: This Book's Organization: Read Me first

Part 1: The basics: parameters, probability, Bayes'rule and R
chapter 2: Introduction: models we believe in
chapter 3: what is this stufff called probability
chapter 4: Baye's rule

Part 2: All the fundamentals applied to inferring a binomial proportion
chapter 5: inferring a binomial proportion via exact mathematical analysis
chapter 6: Inferring a binomial proportion via Grid approximation
chapter 7: Inferring a binomial proportion via the metropolis algorithm
chapter 8: inferring two binomial proportions via Glibbs sampling
chapter 9: Bernouili likelihood with hierarchical prior
chapter 10: Hierarchical modeling and model comparison
chapter 11: Null hypothesis significance testing
chapter 12: Bayesian approaches to testing a point Null hypothesis
chapter 13: Goals, power and sample size

Part 3: Applied to the generalized linear model
chapter 14: overview of the generalized linear model
chapter 15: Metric predicted variable on a single group
chapter 16: Metric predicted variable with one metric
chapter 17: Metric predicted variable with multiple metric predictors
chapter 18: Metric predicted variable with one mominal predictor
chapter 19: Mtric predicted variable with multiple nominal predictors
chapter 20: Dichotomous predicted variable
chapter 21: Ordinal predicted variable
chapter 22: Contengecy table analysis
chapter 23: Tools in the trunk

Includes bibliographical references and index.