Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

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.

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

Now in its third edition, this classic book continues to take an applied approach to analysis using up-to-date Bayesian methods. Along with new and revised software code, this edition includes four new chapters on nonparametric modeling, updates the discussion of cross-validation and predictive information criteria, and improves convergence monitoring and effective sample size calculations for iterative simulation. It also covers weakly informative priors, boundary-avoiding priors, Hamiltonian Monte Carlo, variational Bayes, and expectation propagation. Data sets and other materials are available online.

Part I: Fundamentals of Bayesian inference 1 Probability and inference 2 Single-parameter models 3 Introduction to multiparameter models 4 Asymptotics and connections to non-Bayesian approaches 5 Hierarchical models Part II: Fundamentals of Bayesian data analysis 6 Model checking 7 Evaluating, comparing, and expanding models 8 Modeling accounting for data collection 9 Decision analysis Part III: Advanced computation 10 Introduction to Bayesian computation 11 Basics of Markov chain simulation 12 Computationally efficient Markov chain simulation 13 Modal and distributional approximations Part IV: Regression models 14 Introduction to regression models 15 Hierarchical linear models 16 Generalized linear models 17 Models for robust inference 18 Models for missing data Part V: Nonlinear and nonparametric models 19 Parametric nonlinear models 20 Basis function models 21 Gaussian process models 22 Finite mixture models 23 Dirichlet process models A. Standard probability distributions B. Outline of proofs of limit theorems C. Computation in R and Stan