  In this paper, we tackle structure learning of Directed Acyclic Graphs
(DAGs), with the idea of exploiting available prior knowledge of the domain at
hand to guide the search of the best structure. In particular, we assume to
know the topological ordering of variables in addition to the given data. We
study a new algorithm for learning the structure of DAGs, proving its
theoretical consistence in the limit of infinite observations. Furthermore, we
experimentally compare the proposed algorithm to a number of popular
competitors, in order to study its behavior in finite samples.
