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Recent advances in imprecise-probabilistic graphical models
Gert de Cooman, Jasper De Bock, Arthur Van Camp
27 - 32
10.3233/978-1-61499-098-7-27
Frontiers in Artificial Intelligence and Applications
Volume 242: ECAI 2012
Abstract

We summarise and provide pointers to recent advances in inference and identification for specific types of probabilistic graphical models using imprecise probabilities. Robust inferences can be made in so-called credal networks when the local models attached to their nodes are imprecisely specified as conditional lower previsions, by using exact algorithms whose complexity is comparable to that for the precise-probabilistic counterparts.

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