Local Score Computation in Learning Belief Networks

Authors:
Y. Xiang;J. Lee
Affiliations:
-;-
Venue:
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Year:
2001

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Abstract

We propose an improved scoring metrics for learning belief networks driven by issues arising from learning in pseudoindependent domains. We identify a small subset of variables called a crux, which is sufficient to compute the incremental improvement of alternative belief network structures. We prove formally that such local computation, while improving efficiency, does not introduce any error to the evaluation of alternative structures.