Advances in Probabilistic Reasoning
Artificial Intelligence
2015-05-19 v2
Abstract
This paper discuses multiple Bayesian networks representation paradigms for encoding asymmetric independence assertions. We offer three contributions: (1) an inference mechanism that makes explicit use of asymmetric independence to speed up computations, (2) a simplified definition of similarity networks and extensions of their theory, and (3) a generalized representation scheme that encodes more types of asymmetric independence assertions than do similarity networks.
Cite
@article{arxiv.1303.5718,
title = {Advances in Probabilistic Reasoning},
author = {Dan Geiger and David Heckerman},
journal= {arXiv preprint arXiv:1303.5718},
year = {2015}
}
Comments
Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)