Incompletely Specified Probabilistic Networks
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:Probabilistic networks, used as an adjunct or alternative to the logical models used in artificial intelligence (AI) and decision support systems (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 that are simply unavailable. In this paper 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. The use of the technique in MIS settings is illustrated.Granularity
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Abstract We investigate simple endotrivial modules of finite quasi-simple groups and classify them in several important cases. This is motivated by a recent result of Robinson [Bull. Lond. Math. Soc. 43 (2011), 712–716] showing that simple endotrivial modules of most groups come from quasi-simple groups.
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