On the Use of Skeletons when Learning in Bayesian Networks
  In this paper, we present a heuristic operator which aims at simultaneously
optimizing the orientations of all the edges in an intermediate Bayesian
network structure during the search process. This is done by alternating
between the space of directed acyclic graphs (DAGs) and the space of skeletons.
The found orientations of the edges are based on a scoring function rather than
on induced conditional independences. This operator can be used as an extension
to commonly employed search strategies. It is evaluated in experiments with
artificial and real-world data.
