概率网络三角剖分的预处理
Pre-processing for Triangulation of Probabilistic Networks
摘要：
目前最有效的概率网络推理算法建立在网络图的三角剖分上。在本文中，我们表明，预处理可以帮助找到良好的三角剖分概率网络，即三角剖分的最小最大团大小。我们提供了一套规则逐步减少一个图，而不会失去最优性。这种简化使我们能够在更小的图上解决三角剖分问题。从较小图的三角剖分，通过反转缩减步骤获得原始图的三角剖分。我们的实验结果表明，一些著名的现实生活中的概率网络的图形可以三角化最佳的预处理;对于其他网络，他们的图的大小得到了巨大的减少。
英文摘要：
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.
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