"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.
Cite
Text
Agosta. ""Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models." Conference on Uncertainty in Artificial Intelligence, 1991.Markdown
[Agosta. ""Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models." Conference on Uncertainty in Artificial Intelligence, 1991.](https://mlanthology.org/uai/1991/agosta1991uai-conditional/)BibTeX
@inproceedings{agosta1991uai-conditional,
title = {{"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models}},
author = {Agosta, John Mark},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1991},
url = {https://mlanthology.org/uai/1991/agosta1991uai-conditional/}
}