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Algorithms for the Nearest Assignment Problem

2018
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Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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We consider the following nearest assignment problem (NAP): given a Bayesian network B and probability value q, find a configuration w of variables in B such that difference between q and the probability of w is minimized. NAP is much harder than conventional inference problems such as finding the most probable explanation and is NP-hard even on independent Bayesian networks (IBNs), which are networks having no edges. Therefore, in order to solve NAP on IBNs, we show how to encode it as a

doi:10.24963/ijcai.2018/707
dblp:conf/ijcai/RouhaniRG18
fatcat:tqhooc3eljaihanrxqdotmp5qq