Properties of Bayesian belief network learning algorithms

Authors:
Remco R. Bouckaert
Affiliations:
Utrecht University, Department of Computer Science, Utrecht, The Netherlands
Venue:
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Year:
1994

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Abstract

In this paper the behavior of various belief network learning algorithms is studied. Selecting belief networks with certain minimallity properties turns out to be NP-hard, which justifies the use of search heuristics. Search heuristics based on the Bayesian measure of Cooper and Herskovits and a minimum description length (MDL) measure are compared with respect to their properties for both limiting and finite database sizes. It is shown that the MDL measure has more desirable properties than the Bayesian measure. Experimental results suggest that for learning probabilities of belief networks smoothing is helpful.