Faithfulness in Chain Graphs: The Gaussian Case
Machine Learning
2012-06-27 v1 Artificial Intelligence
Statistics Theory
Statistics Theory
Abstract
This paper deals with chain graphs under the classic Lauritzen-Wermuth-Frydenberg interpretation. We prove that the regular Gaussian distributions that factorize with respect to a chain graph with parameters have positive Lebesgue measure with respect to , whereas those that factorize with respect to but are not faithful to it have zero Lebesgue measure with respect to . This means that, in the measure-theoretic sense described, almost all the regular Gaussian distributions that factorize with respect to are faithful to it.
Cite
@article{arxiv.1008.2277,
title = {Faithfulness in Chain Graphs: The Gaussian Case},
author = {Jose M. Peña},
journal= {arXiv preprint arXiv:1008.2277},
year = {2012}
}