A first approach to closeness distributions
This work addresses a specific modeling challenge in probabilistic graphical models, but it appears incremental as it builds on and reinterprets existing approaches.
The paper tackles the problem of incorporating similarity between smaller distributions in probabilistic graphical models, providing an information geometric approach that reinterprets existing models.
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.