Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models
John Agosta
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: This paper examines the interdependence generated between two parent nodes with a com
Pages: 9-16
PS Link: 7
PDF Link: /papers/91/p9-agosta.pdf
BibTex:
@INPROCEEDINGS{Agosta91,
AUTHOR = "John Agosta ",
TITLE = ""Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "9--16"
}


hosted by DSL   •   site info   •   help