Inference Algorithms for Similarity Networks
Artificial Intelligence
2015-05-19 v2
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.
Cite
@article{arxiv.1303.1493,
title = {Inference Algorithms for Similarity Networks},
author = {Dan Geiger and David Heckerman},
journal= {arXiv preprint arXiv:1303.1493},
year = {2015}
}
Comments
Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)