"This well-written book by an established authority should be in any well-stocked undergraduate/graduate mathematics/statistics library."
-Choice (American Library Association)

A Classic of Statistical Science, Now Thoroughly Revised and Updated

S. James Press's Bayesian Statistics: Principles, Models, and Applications set the standard for references in Bayesian statistics. It has stood as the classic introduction to the subject for practitioners, researchers, and students alike. Since the publication of the First Edition, the field of Bayesian statistical science has grown so substantially that it has become necessary to rewrite the story. New methodologies have been developed, new techniques have emerged for implementing the Bayesian paradigm, and advances in computer science, numerical analysis, artificial intelligence, and machine learning-including data mining and Bayesian neural networks-have tremendously impacted the field of Bayesian learning. Applications using the Bayesian approach have multiplied as well to span most of the disciplines in the biological, physical, and social sciences.

Subjective and Objective Bayesian Statistics: Principles, Models, and Applications, Second Edition has been rewritten from the bottom up to encompass these changes and to make the text even more useful to the reader. Greatly expanded and revised, this new edition discusses Bayesian theory and principles in depth, expands coverage of many topics to include multivariate procedures, and references applications in many fields to support the usefulness of the subject matter.

Chapters cover:
* Subjective Probability
* Prior Distribution Families
* Approximations, Numerical Methods (Including Markov Chain Monte Carlo Sampling), and Computer Programs
* Assessing Multivariate Prior Distributions (Illustrated by Assessing the Probability of Nuclear War)
* Bayesian Estimation, Hypothesis Testing, Decision Making, and Prediction
* Bayesian Model Averaging
* Bayesian Hierarchical Modeling
* Bayesian Inference in Univariate and Multivariate Regression
* Bayesian Inference in Univariate and Multivariate Analysis of Variance and Covariance
* Bayesian Inference in Classification and Discrimination
* Bayesian Factor Analysis

New to this edition are numerous answers to chapter problems at the rear of the book, greatly expanded coverage, as well as a unique discussion of the de Finetti Transform and other rare topics such as Bayesian model averaging, Bayesian Hierarchical modeling, and Bayesian factor analysis. Experienced statisticians and students alike will be fascinated by the "Bayesian Hall of Fame," with portraits of important contributors to the development of the field, that graces this edition.

Blending theory and application, this Second Edition ensures that this highly-respected reference will remain an essential tool for statisticians for years to come.