The currently most efficient algorithm for inference with a \ud probabilistic network builds upon a triangulation of a networks graph. In this \ud paper, we show that pre-processing can help in finding good triangulations for \ud probabilistic networks, that is, triangulations with a minimal maximum clique \ud size. We provide a set of rules for stepwise reducing a graph, without losing \ud optimality. This reduction allows us to solve the triangulation problem on a \ud smaller graph. From the smaller graphs triangulation, a triangulation of the \ud original graph is obtained by reversing the reduction steps. Our experimental \ud results show that the graphs of some well-known real-life probabilistic networks \ud can be triangulated optimally just by preprocessing; for other networks, huge \ud reductions in their graphs size are obtained
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.