Uncertainty in Artificial Intelligence
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Localized Partial Evaluation of Belief Networks
Denise Draper, Steve Hanks
Abstract:
Most algorithms for propagating evidence through belief networks have been exact and exhaustive: they produce an exact (point-valued) marginal probability for every node in the network. Often, however, an application will not need information about every n ode in the network nor will it need exact probabilities. We present the localized partial evaluation (LPE) propagation algorithm, which computes interval bounds on the marginal probability of a specified query node by examining a subset of the nodes in the entire network. Conceptually, LPE ignores parts of the network that are "too far away" from the queried node to have much impact on its value. LPE has the "anytime" property of being able to produce better solutions (tighter intervals) given more time to consider more of the network.
Keywords:
Pages: 170-177
PS Link:
PDF Link: /papers/94/p170-draper.pdf
BibTex:
@INPROCEEDINGS{Draper94,
AUTHOR = "Denise Draper and Steve Hanks",
TITLE = "Localized Partial Evaluation of Belief Networks",
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 = "170--177"
}


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