Uncertainty in Artificial Intelligence
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Lazy Propagation in Junction Trees
Anders Madsen, Finn Jensen
Abstract:
The efficiency of algorithms using secondary structures for probabilistic inference in Bayesian networks can be improved by exploiting independence relations induced by evidence and the direction of the links in the original network. In this paper we present an algorithm that on-line exploits independence relations induced by evidence and the direction of the links in the original network to reduce both time and space costs. Instead of multiplying the conditional probability distributions for the various cliques, we determine on-line which potentials to multiply when a message is to be produced. The performance improvement of the algorithm is emphasized through empirical evaluations involving large real world Bayesian networks, and we compare the method with the HUGIN and Shafer-Shenoy inference algorithms.
Keywords: Bayesian networks, junction trees, inference algorithms. the HUGIN and Shafer-Shenoy
Pages: 362-369
PS Link: http://www.cs.auc.dk/~anders/papers/lp.ps
PDF Link: /papers/98/p362-madsen.pdf
BibTex:
@INPROCEEDINGS{Madsen98,
AUTHOR = "Anders Madsen and Finn Jensen",
TITLE = "Lazy Propagation in Junction Trees",
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 = "362--369"
}


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