Bayesian network learning with cutting planes

Understand

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.