Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples largely from the authors' own experiences, the book focuses on modern computational tools and obtains inferences using computer simulations. Its unique features include thorough discussions of the methods for

part |2 pages

Part I: Fundamentals of Bayesian Inference

chapter 1|29 pages


chapter 2|43 pages

2Single-parameter models

chapter 3|29 pages

Introduction to multiparameter models

part |2 pages

Part II: Fundamentals of Bayesian Data Analysis

chapter 5|41 pages

Hierarchical models

chapter 6|41 pages

Model checking and improvement

chapter 7|52 pages

Modeling accounting for data collection

chapter 8|12 pages

Connections and challenges

chapter 9|14 pages

General advice

part |2 pages

Part III: Advanced Computation

chapter 10|8 pages

Overview of computation

chapter 11|29 pages

Posterior simulation

chapter 12|25 pages

Approximations based on posterior modes

chapter 13|15 pages

Special topics in computation

part |2 pages

Part IV: Regression Models

chapter 14|37 pages

Introduction to regression models

chapter 15|26 pages

Hierarchical linear models

chapter 16|29 pages

Generalized linear models

chapter 17|19 pages

Models for robust inference

part |2 pages

Part V: Specific Models and Problems

chapter 18|18 pages

Mixture models

chapter 19|17 pages

Multivariate models

chapter 20|21 pages

Nonlinear models

chapter 21|24 pages

Models for missing data

chapter 22|29 pages

Decision analysis