Learning the Structure of Dynamic Probabilistic Networks

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.

Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)

References in corpus (4)

• The Bayesian Structural EM Algorithm
• Tractable Inference for Complex Stochastic Processes
• Stochastic Simulation Algorithms for Dynamic Probabilistic Networks
• Structure and Parameter Learning for Causal Independence and Causal Interaction Models

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