  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.
