Bayesian Nonparametrics and Semi-parametrics

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 6 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 185-217 from Springer

Abstract: Abstract One of the fastest growing research areas in Bayesian inference is the study of prior probability models for random distributions, also known as nonparametric Bayesian models. While the literature goes back to the 1970s, nonparametric Bayes remained a highly specialized field until the 1990s when new computational methods facilitated the use of such models for actual data analysis. This eventually led to a barrage of new nonparametric Bayesian literature over the last 10 years. In this chapter we highlight some of the current research challenges in nonparametric Bayes.

Keywords: Random Measure; Dirichlet Process; Beta Process; Hierarchical Dirichlet Process; Nonhomogeneous Poisson Process (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-6944-6_6

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