1996 | OriginalPaper | Chapter
On Test Selection Strategies for Belief Networks
Authors : David Madigan, Russell G. Almond
Published in: Learning from Data
Publisher: Springer New York
Included in: Professional Book Archive
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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.