"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models
Abstract
This paper examines the interdependence generated between two parent nodes with a common instantiated child node, such as two hypotheses sharing common evidence. The relation so generated has been termed "intercausal." It is shown by construction that inter-causal independence is possible for binary distributions at one state of evidence. For such "CICI" distributions, the two measures of inter-causal effect, "multiplicative synergy" and "additive synergy" are equal. The well known "noisy-or" model is an example of such a distribution. This introduces novel semantics for the noisy-or, as a model of the degree of conflict among competing hypotheses of a common observation.
Keywords
Cite
@article{arxiv.1303.5704,
title = {"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models},
author = {John Mark Agosta},
journal= {arXiv preprint arXiv:1303.5704},
year = {2013}
}
Comments
Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)