A lot of research in graphical models has been devoted to developing correct and efficient 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.
@article{urn:eudml:doc:39275,
title = {Knowledge revision in Markov networks.},
journal = {Mathware and Soft Computing},
volume = {11},
year = {2004},
pages = {93-107},
zbl = {1129.68510},
mrnumber = {MR2139291},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39275}
}
Gebhardt, Jörg; Borgelt, Christian; Kruse, Rudolf; Detmer, Heinz. Knowledge revision in Markov networks.. Mathware and Soft Computing, Tome 11 (2004) pp. 93-107. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39275/