Doing bayesian data analysis : a tutorial with R and BUGS /
John K. Kruschke.
- Burlington, MA : Academic Press, c2011.
- xvii, 653 p. : ill. ; 25 cm.
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