Learning the Structure of Dynamic Probabilistic Networks
This work addresses structure learning in dynamic probabilistic networks, which is incremental as it adapts existing methods to the dynamic case.
The paper tackles the problem of learning the structure of dynamic probabilistic networks from data, extending scoring rules and handling hidden variables, with empirical results showing applicability in predicting dynamic behaviors and learning biological causal orderings.
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.