Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic recognition networks: an application of influence diagrams to visual recognition
Probabilistic recognition networks: an application of influence diagrams to visual recognition
The structure of bayes networks for visual recognition
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
separable and transitive graphoids
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic construction of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
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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 "inter-causal." 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.