In this paper we deal with conditional independence models closed with respect to graphoid properties. Such models come from different uncertainty measures, in particular in a probabilistic coherent setting. We study some inferential rules and describe methods and algorithms to compute efficiently the closure of a set of conditional independence statements.

Conditional independence structure and its closure: inferential rules and algorithms

BAIOLETTI, Marco;
2009

Abstract

In this paper we deal with conditional independence models closed with respect to graphoid properties. Such models come from different uncertainty measures, in particular in a probabilistic coherent setting. We study some inferential rules and describe methods and algorithms to compute efficiently the closure of a set of conditional independence statements.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/122459
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