Table of Contents

Objective Bayesian Inference with Applications.- Bayesian Decision Based Estimation and Predictive Inference.- Bayesian Model Selection and Hypothesis Tests.- Bayesian Inference for Complex Computer Models.- Bayesian Nonparametrics and Semi-parametrics.- Bayesian Influence and Frequentist Interface.- Bayesian Clinical Trials.- Bayesian Methods for Genomics, Molecular and Systems Biology.- Bayesian Data Mining and Machine Learning.- Bayesian Inference in Political Science, Finance, and Marketing Research.- Bayesian Categorical Data Analysis.- Bayesian Geophysical, Spatial and Temporal Statistics.- Posterior Simulation and Monte Carlo Methods.

About the Author

Ming-Hui Chen is Professor of Statistics at the University of Connecticut; Dipak K. Dey is Head and Professor of Statistics at the University of Connecticut; Peter Müller is Professor of Biostatistics at the University of Texas M. D. Anderson Cancer Center; Dongchu Sun is Professor of Statistics at the University of Missouri- Columbia; and Keying Ye is Professor of Statistics at the University of Texas at San Antonio.

Reviews

From the reviews:“The book is a ‘Festschrift’ in honour of Jim Berger’s 60th birthday that was celebrated at a conference in spring 2010 in Texas. … All the papers are written by experts in their fields and represent the current state of the art in Bayesian modelling. … for those who are interested in Bayesian modelling, there are some interesting aspects to be detected. … the book is aimed for advanced researchers in Bayesian analyses.” (Wolfgang Polasek, International Statistical Review, Vol. 79 (3), 2011)“This collection contains invited papers by statisticians to honor and acknowledge the contributions of James O. Berger to Bayesian statistics. These papers present recent surveys and developments within the area of statistical decision theory and Bayesian statistics and related topics. … Each chapter … provides a detailed treatment of the topic under consideration. … can be useful for graduate students and researchers from diverse fields of statistics and related disciplines. … this edited volume contains a wealth of knowledge, wisdom and information on Bayesian statistics.” (Technometrics, Vol. 53 (2), May, 2011)