Duality for Continuous Graphical Models
Methodology
2021-11-04 v1 Information Theory
math.IT
Computation
Machine Learning
Abstract
The dual normal factor graph and the factor graph duality theorem have been considered for discrete graphical models. In this paper, we show an application of the factor graph duality theorem to continuous graphical models. Specifically, we propose a method to solve exactly the Gaussian graphical models defined on the ladder graph if certain conditions on the local covariance matrices are satisfied. Unlike the conventional approaches, the efficiency of the method depends on the position of the zeros in the local covariance matrices. The method and details of the dualization are illustrated on two toy examples.
Cite
@article{arxiv.2111.01938,
title = {Duality for Continuous Graphical Models},
author = {Mehdi Molkaraie},
journal= {arXiv preprint arXiv:2111.01938},
year = {2021}
}
Comments
Proc. of the 2021 IEEE Information Theory Workshop (ITW2021), Kanazawa, Japan