Uncertainty in Artificial Intelligence
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Algorithms for Irrelevance-Based Partial MAPs
Solomon Shimony
Abstract:
Irrelevance-based partial MAPs are useful constructs for domain-independent explanation using belief networks. We look at two definitions for such partial MAPs, and prove important properties that are useful in designing algorithms for computing them effectively. We make use of these properties in modifying our standard MAP best-first algorithm, so as to handle irrelevance-based partial MAPs.
Keywords:
Pages: 370-377
PS Link:
PDF Link: /papers/91/p370-shimony.pdf
BibTex:
@INPROCEEDINGS{Shimony91,
AUTHOR = "Solomon Shimony ",
TITLE = "Algorithms for Irrelevance-Based Partial MAPs",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "370--377"
}


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