  A significant theoretical advantage of search-and-score methods for learning
Bayesian Networks is that they can accept informative prior beliefs for each
possible network, thus complementing the data. In this paper, a method is
presented for assigning priors based on beliefs on the presence or absence of
certain paths in the true network. Such beliefs correspond to knowledge about
the possible causal and associative relations between pairs of variables. This
type of knowledge naturally arises from prior experimental and observational
data, among others. In addition, a novel search-operator is proposed to take
advantage of such prior knowledge. Experiments show that, using path beliefs
improves the learning of the skeleton, as well as the edge directions in the
network.
