On Functions of Markov Random Fields
Information Theory
2021-07-01 v2 math.IT
Abstract
We derive two sufficient conditions for a function of a Markov random field (MRF) on a given graph to be a MRF on the same graph. The first condition is information-theoretic and parallels a recent information-theoretic characterization of lumpability of Markov chains. The second condition, which is easier to check, is based on the potential functions of the corresponding Gibbs field. We illustrate our sufficient conditions at the hand of several examples and discuss implications for practical applications of MRFs. As a side result, we give a partial characterization of functions of MRFs that are information-preserving.
Cite
@article{arxiv.2005.13908,
title = {On Functions of Markov Random Fields},
author = {Bernhard C. Geiger and Ali Al-Bashabsheh},
journal= {arXiv preprint arXiv:2005.13908},
year = {2021}
}
Comments
7 pages, submitted to IEEE Information Theory Workshop