Recently, attention in the statistical literature has turned towards joint estimation of graphical models. These methods utilize the joint structure to inform estimation across groups and improve group level inference. However, the inferential ability on the shared structures is generally lacking. We propose an extension of a recently developed approach which permits formal inference on the shared structures between graphs, and illustrate it's properties through theoretical results and a simulation study.
