Bayesian Data Analysis, Third Edition
By Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
To Be Published November 26th 2013 by Chapman and Hall/CRC – 800 pages
Description
This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various disciplines. This third edition provides two new chapters on Bayesian nonparametrics and covers computation systems BUGS and R. It also offers enhanced computing advice. The book’s website includes solutions to the problems, data sets, software advice, and other ancillary material.
Reviews
Praise for the Second Edition
… it is simply the best all-around modern book focused on data analysis currently available. … There is enough important additional material here that those with the first edition should seriously consider updating to the new version. … when students or colleagues ask me which book they need to start with in order to take them as far as possible down the road toward analyzing their own data, Gelman et al. has been my answer since 1995. The second edition makes this an even more robust choice.
—Lawrence Joseph, Montreal General Hospital and McGill University, Statistics in Medicine, Vol. 23, 2004
… I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems.
—John Grego, University of South Carolina, USA
… easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods
—David Blackwell, University of California, Berkeley, USA
Contents
From the Second Edition:
FUNDAMENTALS OF BAYESIAN INFERENCE
Background
Single-Parameter Models
Introduction to Multiparameter Models
Large-Sample Inference and Connections to Standard Statistical Methods
FUNDAMENTALS OF BAYESIAN DATA ANALYSIS
Hierarchical Models
Model Checking and Improvement
Modeling Accounting for Data Collection
Connections and Controversies
General Advice
ADVANCED COMPUTATION
Overview of Computation
Posterior Simulation
Approximations Based on Posterior Modes
Topics in Computation
REGRESSION MODELS
Introduction to Regression Models
Hierarchical Linear Models
Generalized Linear Models
Models for Robust Inference and Sensitivity Analysis
Analysis of Variance
SPECIFIC MODELS AND PROBLEMS
Mixture Models
Multivariate Models
Nonlinear Models
Models for Missing Data
Decision Analysis
APPENDICES
A: Standard Probability Distributions
B: Outline of Proofs of Asymptotic Theorems
C: Example of Computation in R and Bugs
References
Name: Bayesian Data Analysis, Third Edition (Hardback) – Chapman and Hall/CRC
Description: By Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin. This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as...
Categories: Statistical Theory & Methods, Regression Analysis and Multivariate Statistics, Statistics & Computing, Quantitative Methods