Uncertainty in Artificial Intelligence
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Inference Algorithms for Similarity Networks
Dan Geiger, David Heckerman
Abstract:
We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that works under the assumption that every event has a nonzero probability of occurrence. Another inference algorithm is developed for type 1 similarity networks that works under no restriction, albeit less efficiently.
Keywords:
Pages: 326-334
PS Link:
PDF Link: /papers/93/p326-geiger.pdf
BibTex:
@INPROCEEDINGS{Geiger93,
AUTHOR = "Dan Geiger and David Heckerman",
TITLE = "Inference Algorithms for Similarity Networks",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "326--334"
}


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