Title :
Incompletely specified probabilistic networks
Author :
Roehrig, Stephen F.
Author_Institution :
Heinz Sch., Carnegie Mellon Univ., Pittsburgh, PA, USA
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
Keywords :
decision support systems; inference mechanisms; probabilistic logic; uncertainty handling; AI; DSS; conditional probabilities; incompletely specified probabilistic networks; logical models; random variables; Artificial intelligence; Availability; Buildings; Databases; Decision support systems; Diseases; Finance; Probability distribution; Random variables;
Conference_Titel :
System Sciences, 1995. Proceedings of the Twenty-Eighth Hawaii International Conference on
Conference_Location :
Wailea, HI
Print_ISBN :
0-8186-6930-6
DOI :
10.1109/HICSS.1995.375614
