Algorithms for Learning Decomposable Models and Chordal Graphs
  Decomposable dependency models and their graphical counterparts, i.e.,
chordal graphs, possess a number of interesting and useful properties. On the
basis of two characterizations of decomposable models in terms of independence
relationships, we develop an exact algorithm for recovering the chordal
graphical representation of any given decomposable model. We also propose an
algorithm for learning chordal approximations of dependency models isomorphic
to general undirected graphs.
