Pre-processing for Triangulation of Probabilistic Networks
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 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.
Keywords
Cite
@article{arxiv.1301.2256,
title = {Pre-processing for Triangulation of Probabilistic Networks},
author = {Hans L. Bodlaender and Arie M. C. A. Koster and Frank van den Eijkhof and Linda C. van der Gaag},
journal= {arXiv preprint arXiv:1301.2256},
year = {2013}
}
Comments
Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)