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On Test Selection Strategies for Belief Networks

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Book cover Learning from Data

Part of the book series: Lecture Notes in Statistics ((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.

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Washington, USA

    David Madigan

  2. Fred Hutchinson Cancer Research Center, University of Washington, USA

    David Madigan

  3. StatSci Division, MathSoft, USA

    Russell G. Almond

Authors
  1. David Madigan
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  2. Russell G. Almond
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Vanderbilt University, Box 1679, Station B, Nashville, Tennessee, 37235, USA

    Doug Fisher

  2. Department of Economics Institute of Statistics and Econometrics, Free University of Berlin, 14185, Berlin, Garystre 21, Germany

    Hans-J. Lenz

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© 1996 Springer-Verlag New York, Inc.

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Madigan, D., Almond, R.G. (1996). On Test Selection Strategies for Belief Networks. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_9

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  • DOI: https://doi.org/10.1007/978-1-4612-2404-4_9

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94736-5

  • Online ISBN: 978-1-4612-2404-4

  • eBook Packages: Springer Book Archive

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