Introduction

Ming-Hui Chen (), Dipak K. Dey (), Peter Müller (), Dongchu Sun () and Keying Ye ()
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Ming-Hui Chen: University of Connecticut, Department of Statistics
Dipak K. Dey: University of Connecticut, Department of Statistics
Peter Müller: The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics
Dongchu Sun: University of Missouri-Columbia, Department of Statistics
Keying Ye: University of Texas at San Antonio, Department of Management Science and Statistics, College of Business

Chapter Chapter 1 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 1-30 from Springer

Abstract: Abstract In the years since the 1985 publication of Statistical Decision Theory and Bayesian Analysis by James Berger, there has been an enormous increase in the use of Bayesian analysis and decision theory in statistics and science. The rapid expansion in the use of Bayesian methods is due in part to substantial advances in computational and modeling techniques, and Bayesian methods are now central in many branches of science. The aim of this book is to review current research frontiers in Bayesian analysis and decision theory. It is impossible to provide an exhaustive discussion of all current research in Bayesian statistics, so the book instead summarizes current research frontiers by providing representative examples of research challenges chosen from a wide variety of areas.

Keywords: Markov Chain Monte Carlo; Monte Carlo; Bayesian Inference; Bayesian Analysis; Latent Variable Model (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-6944-6_1

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