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Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques.

New to the Third Edition

New data examples, corresponding R and WinBUGS code, and homework problems
Explicit descriptions and illustrations of hierarchical modeling-now commonplace in Bayesian data analysis
A new chapter on Bayesian design that emphasizes Bayesian clinical trials
A completely revised and expanded section on ranking and histogram estimation
A new case study on infectious disease modeling and the 1918 flu epidemic
A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem-available both electronically and in print
Ideal for Anyone Performing Statistical Analyses

Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.



Approaches for statistical inference

Introduction

Motivating Vignettes

Defining the Approaches

The Bayes-Frequentist Controversy

Some Basic Bayesian Models

The Bayes approach

Introduction

Prior Distributions

Bayesian Inference

Hierarchical Modeling

Model Assessment

Nonparametric Methods

Bayesian computation

Introduction

Asymptotic Methods

Noniterative Monte Carlo Methods

Markov Chain Monte Carlo Methods

Model criticism and selection

Bayesian Modeling

Bayesian Robustness

Model Assessment

Bayes Factors via Marginal Density Estimation

Bayes Factors via Sampling over the Model Space

Other Model Selection Methods

The empirical Bayes approach

Introduction

Parametric EB Point Estimation

Nonparametric EB Point Estimation

Interval Estimation

Bayesian Processing and Performance

Frequentist Performance

Empirical Bayes Performance

Bayesian design

Principles of Design

Bayesian Clinical Trial Design

Applications in Drug and Medical Device Trials

Special methods and models

Estimating Histograms and Ranks

Order Restricted Inference

Longitudinal Data Models

Continuous and Categorical Time Series

Survival Analysis and Frailty Models

Sequential Analysis

Spatial and Spatio-Temporal Models

Case studies

Analysis of Longitudinal AIDS Data

Robust Analysis of Clinical Trials

Modeling of Infectious Diseases

Appendices

Distributional Catalog

Decision Theory

Answers to Selected Exercises

References

Author Index

Subject Index

Index

Exercises appear at the end of each chapter.