Abstract
Probabilistic networks, used as an adjunct or alternative to the logical models used in AI and DSS, offer a way to compactly represent a distribution over a set of random variables. Nonetheless, the specification of a given network may require conditional probabilities which are simply unavailable. A means for analyzing incompletely specified networks is presented, and some general rules are derived from the application of the method to some simple networks.