5 research outputs found
Pre-processing for Triangulation of Probabilistic Networks
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
forprobabilistic 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.Comment: Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001
Pre-Processing Rules for Triangulation of Probabilistic Networks
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
Safe Reduction Rules For Weighted Treewidth
Several sets of reductions rules are known for preprocessing a graph when computing its treewidth. In this paper, we give reduction rules for a weighted variant of treewidth, motivated by the analysis of algorithms for probabilistic networks. We present two general reduction rules that are safe for weighted treewidth. They generalise many of the existing reduction rules for treewidth. Experimental results show that these reduction rules can significantly reduce the problem size for several instances of real-life probabilistic networks
Pre-processing for Triangulation of Probabilistic Networks
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. The 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 pre-processing; for other networks, huge reductions in size are obtained