Uncertainty in Artificial Intelligence
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Nested Junction Trees
Uffe Kjærulff
The efficiency of inference in both the Hugin and, most notably, the Shafer-Shenoy architectures can be improved by exploiting the independence relations induced by the incoming messages of a clique. That is, the message to be sent from a clique can be computed via a factorization of the clique potential in the form of a junction tree. In this paper we show that by exploiting such nested junction trees in the computation of messages both space and time costs of the conventional propagation methods may be reduced. The paper presents a structured way of exploiting the nested junction trees technique to achieve such reductions. The usefulness of the method is emphasized through a thorough empirical evaluation involving ten large real-world Bayesian networks and the Hugin inference algorithm.
Keywords: Bayesian network, junction tree, inference, Shafer-Shenoy architecture.
Pages: 294-301
PS Link: ftp://ftp.cs.auc.dk/pub/reports/papers/kjaerulff97a.ps.gz
PDF Link: /papers/97/p294-kjaerulff.pdf
AUTHOR = "Uffe Kjærulff ",
TITLE = "Nested Junction Trees",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "294--301"

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