Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets

@article{Geiger1996KnowledgeRA,
title={Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets},
author={Dan Geiger and David Heckerman},
journal={Artif. Intell.},
year={1996},
volume={82},
pages={45-74}
}

We examine two representation schemes for uncertain knowledge: the similarity network (Heckerman, 1991) and the Bayesian multinet. These schemes are extensions of the Bayesian network model in that they represent asymmetric independence assertions. We explicate the notion of relevance upon which similarity networks are based and present an efficient inference algorithm that works under the assumption that every event has a nonzero probability. Another inference algorithm is developed that works… CONTINUE READING