Uncertainty in Artificial Intelligence
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Learning the Structure of Dynamic Probabilistic Networks
Nir Friedman, Kevin Murphy, Stuart Russell
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.
Keywords: Learning structure, incomplete data, dynamic probabilistic networks.
Pages: 139-147
PS Link: http://www.cs.berkeley.edu/~nir/Papers/FMR1.ps
PDF Link: /papers/98/p139-friedman.pdf
BibTex:
@INPROCEEDINGS{Friedman98,
AUTHOR = "Nir Friedman and Kevin Murphy and Stuart Russell",
TITLE = "Learning the Structure of Dynamic Probabilistic Networks",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "139--147"
}


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