Max Engardt: Structure Learning of Bayesian Networks with Bounded Treewidth Using Mixed Integer Linear Programming

When given a Bayesian network, a common use of it is calculating conditional probabilities. This is known as inference. In order to be able to infer effectively, the structure of the Bayesian network is required to have low treewidth. Therefore, the problem of learning the structure of Bayesian networks with bounded treewidth is studied in this thesis. This is solved by reducing the problem to a mixed integer linear problem using several formulation for the structure of the Bayesian network as well as for bounding the treewidth of the structure. Solving the problem in this way gives an algorithm known as an anytime algorithm which can be aborted during the run and return a solution as well as an upper bound for the value of the best possible solution. Tests show that several of these formulations are of practical use as implementations of them prove solutions to be optimal or nearly optimal for several data sets.