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Bayesian Nonparametrics and Semi-parametrics

  • Ming-Hui Chen
  • Dipak K. Dey
  • Peter Müller
  • Dongchu Sun
  • Keying Ye
Chapter

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer New York 2010

Authors and Affiliations

  • Ming-Hui Chen
    • 1
  • Dipak K. Dey
    • 1
  • Peter Müller
    • 2
  • Dongchu Sun
    • 3
  • Keying Ye
    • 4
  1. 1.Department of StatisticsUniversity of ConnecticutStorrsUSA
  2. 2.Department of BiostatisticsThe University of Texas, M. D. Anderson Cancer CenterHoustonUSA
  3. 3.Department of StatisticsUniversity of Missouri-ColumbiaColumbiaUSA
  4. 4.Department of Management Science and Statistics, College of BusinessUniversity of Texas at San AntonioSan AntonioUSA

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