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Posterior Simulation and Monte Carlo Methods

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

Abstract

A beauty of the Bayesian approach is the principled nature of inference. There is a gold standard of how to proceed, and the basic principle is easily explained. However, the actual implementation often gives rise to many challenges. One of the challenges that remains an important research frontier of Bayesian inference is the problem of numerically evaluating the desired posterior summaries. In this chapter we review some related specific research problems.

Keywords

Markov Chain Monte Carlo Importance Sampling Marginal Likelihood Markov Chain Monte Carlo Algorithm Inclusion Probability 
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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