Bayesian network learning with cutting planes
  The problem of learning the structure of Bayesian networks from complete
discrete data with a limit on parent set size is considered. Learning is cast
explicitly as an optimisation problem where the goal is to find a BN structure
which maximises log marginal likelihood (BDe score). Integer programming,
specifically the SCIP framework, is used to solve this optimisation problem.
Acyclicity constraints are added to the integer program (IP) during solving in
the form of cutting planes. Finding good cutting planes is the key to the
success of the approach -the search for such cutting planes is effected using a
sub-IP. Results show that this is a particularly fast method for exact BN
learning.
