Inference Algorithms for Similarity Networks
Dan Geiger, David E. Heckerman · arXiv (Cornell University) · 2013
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.