ML Anthology

Inference Algorithms for Similarity Networks

Dan Geiger, David Heckerman
UAI 1993 pp. 326-334
doi:10.1016/B978-1-4832-1451-1.50044-5 /uai/1993/geiger1993uai-inference/

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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Text

Geiger and Heckerman. "Inference Algorithms for Similarity Networks." Conference on Uncertainty in Artificial Intelligence, 1993. doi:10.1016/B978-1-4832-1451-1.50044-5

Markdown

[Geiger and Heckerman. "Inference Algorithms for Similarity Networks." Conference on Uncertainty in Artificial Intelligence, 1993.](https://mlanthology.org/uai/1993/geiger1993uai-inference/) doi:10.1016/B978-1-4832-1451-1.50044-5

BibTeX

@inproceedings{geiger1993uai-inference,
  title     = {{Inference Algorithms for Similarity Networks}},
  author    = {Geiger, Dan and Heckerman, David},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1993},
  pages     = {326-334},
  doi       = {10.1016/B978-1-4832-1451-1.50044-5},
  url       = {https://mlanthology.org/uai/1993/geiger1993uai-inference/}
}