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Revisiting MAP Estimation, Message Passing and Perfect Graphs
Journal of machine learning research
Given a graphical model, one of the most useful queries is to find the most likely configuration of its variables. This task, known as the maximum a posteriori (MAP) problem, can be solved efficiently via message passing techniques when the graph is a tree, but is NPhard for general graphs. Jebara (2009) shows that the MAP problem can be converted into the stable set problem, which can be solved in polynomial time for a broad class of graphs known as perfect graphs via a linear programmingdblp:journals/jmlr/FouldsNSI11 fatcat:pzfiftckszfxdd7qulattuse7m