@techreport{geiger1993inference,
author = {Geiger, Dan and Heckerman, David},
title = {Inference Algorithms for Similarity Networks},
year = {1993},
month = {July},
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.},
publisher = {Morgan Kaufmann Publishers},
url = {http://approjects.co.za/?big=en-us/research/publication/inference-algorithms-for-similarity-networks/},
pages = {326-334},
edition = {Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC},
number = {MSR-TR-93-09},
note = {Proceedings of the IEEE International Conference on Data Mining (ICDM)},
}