Advances in probabilistic reasoning

Authors:
Dan Geiger;David Heckerman
Affiliations:
Northrop Research and Technology Center, Palos Verdes, CA;Departments of Computer Science and Pathology, University of Southern California, LA, CA
Venue:
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Year:
1991

Citing 5
Cited 0

Evaluating influence diagrams

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separable and transitive graphoids

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Probabilistic similarity networks

Probabilistic similarity networks

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Abstract

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.