Inference Algorithms for Similarity Networks

2013/03/06 by Dan Geiger, Geiger, Dan, David Heckerman +1
Computer Science · #AI-based Problem Solving and Planning #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #cs.AI

paper · pdf · doi:10.48550/arxiv.1303.1493

Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)

openalex publication_date 2013/03/06 · arxiv created 2015/05/16 · arxiv updated 2015/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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