An explicit link between graphical models and Gaussian Markov random fields on metric graphs
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915093507211264 |
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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 |