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 significance of learning an influence network is to promote knowledge discovery and improve on density estimation in the temporal space. We learn the influence network, defined by this paper, by learning the optimal structure for each process first, and thereafter apply redefined structure learning algorithms for temporal models. Our procedure builds on the language of probabilistic graphical model representation and learning. This paper provides the following contributions: we (a) provide a definition of influence between stochastic processes represented by dynamic Bayesian networks; (b) expand on the conventional structure learning literature by providing a structure score and learning procedure for temporal models; and (c) introduce the notion of a structural assemble which is used to associate two stochastic processes represented by dynamic Bayesian networks.