A simple approach for finding the globally optimal Bayesian network
  structure
  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. It can be
easily parallelized, and offers a possibility for efficient exploration of the
best networks consistent with different variable orderings. In the experimental
part of the paper we compare the performance of the algorithm to the previous
state-of-the-art algorithm. Free source-code and an online-demo can be found at
http://b-course.hiit.fi/bene.
