The difficulty of learning the underlying structure between processes is a common task found throughout the sciences, however not much work is dedicated towards this problem. In this paper, we attempt to use the language of structure learning to address learning the dynamic influence network between partially observable processes represented as dynamic Bayesian networks. The…