  Probabilistic graphical models allow us to encode a large probability
distribution as a composition of smaller ones. It is oftentimes the case that
we are interested in incorporating in the model the idea that some of these
smaller distributions are likely to be similar to one another. In this paper we
provide an information geometric approach on how to incorporate this
information, and see that it allows us to reinterpret some already existing
models.
