A simple approach for finding the globally optimal Bayesian network structure

Understand

We study the problem of learning the best Bayesian network structure with respect to a decomposable score such as BDe, BIC or AIC.

• This problem is known to be NP-hard, which means that solving it becomes quickly infeasible as the number of variables increases.
• Nevertheless, in this paper we show that it is possible to learn the best Bayesian network structure with over 30 variables, which covers many practically interesting cases.
• Our algorithm is less complicated and more efficient than the techniques presented earlier.