Uncertainty in Artificial Intelligence
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Advances in Probabilistic Reasoning
Dan Geiger, David Heckerman
This paper discuses multiple Bayesian networks representation paradigms for encoding asymmetric independence assertions. We offer three contributions: (1) an inference mechanism that makes explicit use of asymmetric independence to speed up computations, (2) a simplified definition of similarity networks and extensions of their theory, and (3) a generalized representation scheme that encodes more types of asymmetric independence assertions than do similarity networks.
Pages: 118-126
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PDF Link: /papers/91/p118-geiger.pdf
AUTHOR = "Dan Geiger and David Heckerman",
TITLE = "Advances in Probabilistic Reasoning",
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 = "118--126"

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