An explicit link between graphical models and Gaussian Markov random fields on metric graphs

Fuente: arXiv
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Autores principales: Bolin, David, Simas, Alexandre B., Wallin, Jonas
Formato: Preprint
Publicado: 2025
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author Bolin, David
Simas, Alexandre B.
Wallin, Jonas
author_facet Bolin, David
Simas, Alexandre B.
Wallin, Jonas
contents We derive an explicit link between Gaussian Markov random fields on metric graphs and graphical models, and in particular show that a Markov random field restricted to the vertices of the graph is, under mild regularity conditions, a Gaussian graphical model with a distribution which is faithful to its pairwise independence graph, which coincides with the neighbor structure of the metric graph. This is used to show that there are no Gaussian random fields on general metric graphs which are both Markov and isotropic in some suitably regular metric on the graph, such as the geodesic or resistance metrics.
format Preprint
id arxiv_https___arxiv_org_abs_2501_03701
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle An explicit link between graphical models and Gaussian Markov random fields on metric graphs
Bolin, David
Simas, Alexandre B.
Wallin, Jonas
Probability
Statistics Theory
We derive an explicit link between Gaussian Markov random fields on metric graphs and graphical models, and in particular show that a Markov random field restricted to the vertices of the graph is, under mild regularity conditions, a Gaussian graphical model with a distribution which is faithful to its pairwise independence graph, which coincides with the neighbor structure of the metric graph. This is used to show that there are no Gaussian random fields on general metric graphs which are both Markov and isotropic in some suitably regular metric on the graph, such as the geodesic or resistance metrics.
title An explicit link between graphical models and Gaussian Markov random fields on metric graphs
topic Probability
Statistics Theory
url https://arxiv.org/abs/2501.03701