Categorical Aspects of Parameter Learning

TL;DR

This paper offers a categorical framework for understanding parameter learning in Bayesian networks, analyzing MLE and Bayesian methods through monads and natural transformations.

Contribution

It introduces a novel categorical perspective on parameter learning, connecting multisets, probability distributions, and natural transformations.

Findings

Categorical analysis of MLE and Bayesian learning techniques

Use of monads and natural transformations to formalize learning processes

Provides a unified theoretical framework for probabilistic parameter estimation

Abstract

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.