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dc.contributor.authorGebhardt, Jörg
dc.contributor.authorBogerlt, Christian
dc.contributor.authorKruse, Rudolf
dc.contributor.authorDetmer, Heinz
dc.description.abstractA lot of research in graphical models has been devoted to developing correct and eficient evidence propagation methods, like join tree propagation or bucket elimination. With these methods it is possible to condition the represented probability distribution on given evidence, a reasoning process that is sometimes also called focusing. In practice, however, there is the additional need to revise the represented probability distribution in order to reflect some knowledge changes by satisfying new frame conditions. Pure evidence propagation methods, as implemented in the known commercial tools for graphical models, are unsuited for this task. In this paper we develop a consistent scheme for the important task of revising a Markov network so that it satisfies given (conditional) marginal distributions for some of the variables. This task is of high practical relevance as we demonstrate with a complex application for item planning and capacity management in the automotive industry at Volkswagen Group.
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 2004 Vol. 11 Núm. 3
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.subject.otherMarkov networks
dc.titleKnwoledge revision in Markov networks
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacProcessos de Markov
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access

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