On Test Selection Strategies for Belief Networks

David Madigan, Russell G. Almond
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:342-353, 1995.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-madigan95a, title = {On Test Selection Strategies for Belief Networks}, author = {Madigan, David and Almond, Russell G.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {342--353}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/madigan95a/madigan95a.pdf}, url = {https://proceedings.mlr.press/r0/madigan95a.html}, 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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T On Test Selection Strategies for Belief Networks %A David Madigan %A Russell G. Almond %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-madigan95a %I PMLR %P 342--353 %U https://proceedings.mlr.press/r0/madigan95a.html %V R0 %X 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. %Z Reissued by PMLR on 01 May 2022.
APA
Madigan, D. & Almond, R.G.. (1995). On Test Selection Strategies for Belief Networks. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:342-353 Available from https://proceedings.mlr.press/r0/madigan95a.html. Reissued by PMLR on 01 May 2022.

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