Uncertainty in Artificial Intelligence
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Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependencies
Uffe Kjærulff
Abstract:
The paper presents a method for reducing the computational complexity of Bayesian networks through identification and removal of weak dependencies (removal of links from the (moralized) independence graph). The removal of a small number of links may reduce the computational complexity dramatically, since several fill-ins and moral links may be rendered superfluous by the removal. The method is described in terms of impact on the independence graph, the junction tree, and the potential functions associated with these. An empirical evaluation of the method using large real-world networks demonstrates the applicability of the method. Further, the method, which has been implemented in Hugin, complements the approximation method suggested by Jensen & Andersen (1990).
Keywords: Belief network, causal probabilistic network, graphical chain model, junction tree, c
Pages: 374-382
PS Link: ftp://ftp.iesd.auc.dk/pub/reports/papers/kjaerulff94c.ps.Z
PDF Link: /papers/94/p374-kjaerulff.pdf
BibTex:
@INPROCEEDINGS{Kjærulff94,
AUTHOR = "Uffe Kjærulff ",
TITLE = "Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependencies",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "374--382"
}


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