Learning Bayesian networks: the combination of knowledge and statistical data

Authors:
David Heckerman;Dan Geiger;David M. Chickering
Affiliations:
Microsoft Research, Redmond, WA;Computer Science Department, Technion, Haifa, Israel and Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA
Venue:
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Year:
1994

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Abstract

We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simplify the encoding of a user's prior knowledge. In particular, a user can express his knowledge--for the most part--as a single prior Bayesian network for the domain.