Learning the structure of dynamic probabilistic networks

Authors:
Nir Friedman;Kevin Murphy;Stuart Russell
Affiliations:
U. of California, Berkeley, CA;U. of California, Berkeley, CA;U. of California, Berkeley, CA
Venue:
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Year:
1998

Citing 18
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Abstract

Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden. Finally, we examine two applications where such a technology might be useful: predicting and classifying dynamic behaviors, and learning causal orderings in biological processes. We provide empirical results that demonstrate the applicability of our methods in both domains.