New search methods for the optimal triangulation problem
To perform efficient inference in Bayesian networks by means of a Junction Tree method, the network graph needs to be triangulated. The quality of this triangulation largely determines the efficiency of the subsequent inference, but the triangulation problem is unfortunately NP-hard. It is common for existing methods to use the treewidth criterion for optimality of a triangulation. However, this criterion may lead to a somewhat harder inference problem than the total table size criterion. We therefore investigate new methods for depth-first search and best-first search for finding optimal total table size triangulations. The search methods are made faster by efficient dynamic maintenance of the cliques of a graph. This problem was investigated by Stix, and in this paper we derive a new simple method based on the Bron-Kerbosch algorithm that compares favourably to Stix’ approach. The new approach is generic in the sense that it can be used with other algorithms than just Bron-Kerbosch. The algorithms for finding optimal triangulations are mainly supposed to be off-line methods, but they may form the basis for efficient any-time heuristics. Furthermore, the methods make it possible to evaluate the quality of heuristics precisely and allow us to discover parts of the search space that are most important to direct randomized sampling to.
International Journal of Approximate Reasoning, 2012, Vol 53, Issue 9, p. 1350-1366
Bayesian networks; Optimal triangulation; Probabilistic inference; Cliques in a graph