Presents an accessible introduction to Bayesian statistics
Focuses on the use of Bayesian inference in practice, with many examples of real statistical analyses throughout
Includes plenty of exercises and bibliographic notes at the end of each chapter
Provides data sets, solutions to selected exercises, and other material online
Summary

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors all leaders in the statistics community introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition

Four new chapters on nonparametric modeling
Coverage of weakly informative priors and boundary-avoiding priors
Updated discussion of cross-validation and predictive information criteria
Improved convergence monitoring and effective sample size calculations for iterative simulation
Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
New and revised software code
The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book's web page.

Table of Contents

FUNDAMENTALS OF BAYESIAN INFERENCE
Probability and Inference
Single-Parameter Models
Introduction to Multiparameter Models
Asymptotics and Connections to Non-Bayesian Approaches
Hierarchical Models

FUNDAMENTALS OF BAYESIAN DATA ANALYSIS
Model Checking
Evaluating, Comparing, and Expanding Models
Modeling Accounting for Data Collection
Decision Analysis

ADVANCED COMPUTATION
Introduction to Bayesian Computation
Basics of Markov Chain Simulation
Computationally Efficient Markov Chain Simulation
Modal and Distributional Approximations

REGRESSION MODELS
Introduction to Regression Models
Hierarchical Linear Models
Generalized Linear Models
Models for Robust Inference
Models for Missing Data

NONLINEAR AND NONPARAMETRIC MODELS
Parametric Nonlinear Models
Basic Function Models
Gaussian Process Models
Finite Mixture Models
Dirichlet Process Models

APPENDICES
A: Standard Probability Distributions
B: Outline of Proofs of Asymptotic Theorems
C: Computation in R and Stan

Bibliographic Notes and Exercises appear at the end of each chapter.