  In this work, we demonstrate the Empirical Bayes approach to learning a
Dynamic Bayesian Network. By starting with several point estimates of structure
and weights, we can use a data-driven prior to subsequently obtain a model to
quantify uncertainty. This approach uses a recent development of Generalized
Variational Inference, and indicates the potential of sampling the uncertainty
of a mixture of DAG structures as well as a parameter posterior.
