  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.
