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- W1564550339 abstract "The currently most efficient algorithm for inference with a probabilistic network builds upon a triangulation of a network’s graph. In this paper, we show that pre-processing can help in finding good triangulations for probabilistic networks, that is, triangulations with a minimal maximum clique size. We provide a set of rules for stepwise reducing a graph, without losing optimality. This reduction allows us to solve the triangulation problem on a smaller graph. From the smaller graph’s triangulation, a triangulation of the original graph is obtained by reversing the reduction steps. Our experimental results show that the graphs of some well-known real-life probabilistic networks can be triangulated optimally just by preprocessing; for other networks, huge reductions in their graph’s size are obtained." @default.
- W1564550339 created "2016-06-24" @default.
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- W1564550339 date "2003-01-01" @default.
- W1564550339 modified "2023-09-27" @default.
- W1564550339 title "Pre-Processing Rules for Triangulation of Probabilistic Networks" @default.
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