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 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.

• Publication year

  1998

• Venue

  Conference on Uncertainty in Artificial Intelligence

• Publication date

  1998-07-24

• Fields of study

  Biology, Mathematics, Computer Science

• Identifiers
  arXiv 1301.7374
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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