Inference Algorithms for Similarity Networks
  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.
