Learning Inclusion-Optimal Chordal Graphs

Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hillclimbing search algorithm that uses the inclusion boundary neighborhood over chordal graphs. In the limit of a large sample size and under appropriate hypotheses on the scoring criterion, we prove that the algorithm will find a structure that is inclusion-optimal when the dependency model of the data-generating distribution can be represented exactly by an undirected graph. The algorithm is evaluated on simulated datasets.

• Type: Conference
• Year: 2008
• Where: UAI
• Authors: Vincent Auvray, Louis Wehenkel
• Keywords: Algorithm, Artificial Intelligence, Chordal Graphs, UAI 2008, Undirected Graph