Markov Chain Monte Carlo Methods for Hierarchical Bayesian Expert Systems

  • Jeremy C. York
  • David Madigan
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 89)

Abstract

In a hierarchical Bayesian expert system, the probabilities relating the variables are not known precisely; rather, imprecise knowledge of these probabilities is described by placing prior distributions on them. After obtaining data, one would like to update those distributions to reflect the new information gained; however, this can prove difficult computationally if the observed data are incomplete. This paper describes a way around these difficulties—use of Markov chain Monte Carlo methods.

Keywords

Mellon Papilledema 

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Copyright information

© Springer-Verlag New York 1994

Authors and Affiliations

  • Jeremy C. York
    • 1
  • David Madigan
    • 2
  1. 1.Dept of StatisticsCarnegie Mellon UniversityUSA
  2. 2.Dept of StatisticsUniversity of WashingtonUSA

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