Learning the Structure of Dynamic Probabilistic Networks

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 showhow to searchfor structure whensomeof the variables are hidden. Finally, we examine two applications where such a technology might be useful: predicting and classifyingdynamic behaviors, andlearning causal orderings in biological processes. We provide empirical results that demonstrate the applicability of our methods in both domains.

• Type: Conference
• Year: 1998
• Where: UAI
• Authors: Nir Friedman, Kevin P. Murphy, Stuart J. Russell
• Keywords: Complex Stochastic Processes, Dynamic Probabilistic Networks, Probabilistic Networks, UAI 1998, UAI 2008