Structural learning of Gaussian directed acyclic graphs (DAGs) or Bayesian networks has been studied extensively under the assumption that data are independent. But in real applications such as in biology and social studies observations generated from a Bayesian network model are often mutually dependent and their dependence can be model by a second network model.