"Conditional Inter-Causally Independent" Node Distributions, a Property
  of "Noisy-Or" Models
  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.
