Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
Inference Algorithms for Similarity Networks
Dan Geiger, David Heckerman
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.
Pages: 326-334
PS Link:
PDF Link: /papers/93/p326-geiger.pdf
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"

hosted by DSL   •   site info   •   help