Advances in Probabilistic Reasoning
arXiv:1303.5718
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.
Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)