"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models
This provides a new interpretation of noisy-or models for analyzing conflict among competing hypotheses in probabilistic reasoning, but it is incremental as it builds on existing concepts.
The paper shows that binary distributions can achieve conditional inter-causal independence (CICI) at a specific evidence state, with multiplicative and additive synergies being equal, using the noisy-or model as an example.
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.