(H/B) DOING BAYESIAN DATA ANALYSIS

Παρουσίαση

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. 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. Included are step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs. This book is intended for first-year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Knowledge of algebra and basic calculus is a prerequisite. (From the publisher)

Περιεχόμενα

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 Binomial Probability
6. Inferring a Binomial Probability via Exact Mathematical Analysis
7. Markov Chain Monte Carlo
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
14. Stan
PART III The Generalized Linear Model
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