Inference algorithms for similarity networks

  • Authors:
  • Dan Geiger;David Heckerman

  • Affiliations:
  • Department of Computer Science, Technion Israel Institute of Technology, Technion City, Haifa, Israel;Microsoft Research Center and Department of Computer Science, UCLA, Redmond, WA

  • Venue:
  • UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
  • Year:
  • 1993

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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.