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arXiv · 1206.3236

Learning Inclusion-Optimal Chordal Graphs

Vincent Auvray·Louis Wehenkel
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Abstract

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 hill-climbing 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.

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Vincent Auvray, Louis Wehenkel. 2012-06-13. Learning Inclusion-Optimal Chordal Graphs. https://arxiv.org/abs/1206.3236

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