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On Test Selection Strategies for Belief Networks

  • David Madigan
  • Russell G. Almond
Part of the Lecture Notes in Statistics book series (LNS, volume 112)

Abstract

Decision making under uncertainty typically requires an iterative process of information acquisition. At each stage, the decision maker chooses the next best test (or tests) to perform, and re-evaluates the possible decisions. Value-of-information analyses provide a formal strategy for selecting the next test(s). However, the complete decision-theoretic approach is impractical and researchers have sought approximations.

In this paper, we present strategies for both myopic and limited non-myopic (working with known test groups) test selection in the context of belief networks. We focus primarily on utility-free test selection strategies. However, the methods have immediate application to the decision-theoretic framework.

Keywords

Markov Chain Markov Chain Monte Carlo Method Test Selection Influence Diagram Junction Tree 
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-Verlag New York, Inc. 1996

Authors and Affiliations

  • David Madigan
    • 1
    • 2
  • Russell G. Almond
    • 3
  1. 1.Department of StatisticsUniversity of WashingtonUSA
  2. 2.Fred Hutchinson Cancer Research CenterUniversity of WashingtonUSA
  3. 3.StatSci DivisionMathSoftUSA

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