Categorical Aspects of Parameter Learning
This work offers a theoretical framework for understanding parameter learning, but it is incremental as it applies existing categorical concepts to known techniques.
The paper tackles the problem of parameter learning in Bayesian networks by providing a categorical analysis of maximal likelihood estimation and Bayesian learning, describing them in terms of monads and natural transformations.
Parameter learning is the technique for obtaining the probabilistic parameters in conditional probability tables in Bayesian networks from tables with (observed) data --- where it is assumed that the underlying graphical structure is known. There are basically two ways of doing so, referred to as maximal likelihood estimation (MLE) and as Bayesian learning. This paper provides a categorical analysis of these two techniques and describes them in terms of basic properties of the multiset monad M, the distribution monad D and the Giry monad G. In essence, learning is about the reltionships between multisets (used for counting) on the one hand and probability distributions on the other. These relationsips will be described as suitable natural transformations.