Uncertainty in Artificial Intelligence
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Belief Update in CLG Bayesian Networks With Lazy Propagation
Anders Madsen
Abstract:
In recent years Bayesian networks (BNs) with a mixture of continuous and discrete variables have received an increasing level of attention. We present an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG BNs). The architecture is an extension of lazy propagation using operations of Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator potentials into sets of factors, the proposed architecture takes advantage of independence and irrelevance properties induced by the structure of the graph and the evidence. The resulting benefits are illustrated by examples. Results of a preliminary empirical performance evaluation indicate a significant potential of the proposed architecture.
Keywords:
Pages: 306-313
PS Link:
PDF Link: /papers/06/p306-madsen.pdf
BibTex:
@INPROCEEDINGS{Madsen06,
AUTHOR = "Anders Madsen ",
TITLE = "Belief Update in CLG Bayesian Networks With Lazy Propagation",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "306--313"
}


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