Positivity for Gaussian graphical models

DOI10.1016/j.aam.2013.03.001zbMath1279.62135arXiv1210.0390OpenAlexW1989745763WikidataQ57427082 ScholiaQ57427082MaRDI QIDQ394750

Seth Sullivant, Kelli Talaska, Jan Draisma

Publication date: 27 January 2014

Published in: Advances in Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1210.0390

zbMATH Keywords

determinantsconditional independenceLinström-Gessel-Viennot lemma

Mathematics Subject Classification ID

Multivariate analysis (62H99) Applications of graph theory (05C90) Determinants, permanents, traces, other special matrix functions (15A15)

Related Items (4)

Partial correlation hypersurfaces in Gaussian graphical models ⋮ Causal Structural Learning via Local Graphs ⋮ Nested covariance determinants and restricted trek separation in Gaussian graphical models ⋮ Brownian motion tree models are toric

Cites Work

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• Global identifiability of linear structural equation models
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• Trek separation for Gaussian graphical models
• Binomial determinants, paths, and hook length formulae
• A non-commutative version of Jacobi's equality on the cofactors of a matrix
• Loop-erased walks and total positivity
• On a Parametrization of Positive Semidefinite Matrices with Zeros
• On the Vector Representations of Induced Matroids

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