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November 2020 Nested covariance determinants and restricted trek separation in Gaussian graphical models
Mathias Drton, Elina Robeva, Luca Weihs
Bernoulli 26(4): 2503-2540 (November 2020). DOI: 10.3150/19-BEJ1179


Directed graphical models specify noisy functional relationships among a collection of random variables. In the Gaussian case, each such model corresponds to a semi-algebraic set of positive definite covariance matrices. The set is given via a parametrization, and much work has gone into obtaining an implicit description in terms of polynomial (in)equalities. Implicit descriptions shed light on problems such as parameter identification, model equivalence and constraint-based statistical inference. For models given by directed acyclic graphs, which represent settings where all relevant variables are observed, there is a complete theory: All conditional independence relations can be found via graphical $d$-separation and are sufficient for an implicit description. The situation is far more complicated, however, when some of the variables are hidden (or in other words, unobserved or latent). We consider models associated to mixed graphs that capture the effects of hidden variables through correlated error terms. The notion of trek separation explains when the covariance matrix in such a model has submatrices of low rank and generalizes $d$-separation. However, in many cases, such as the infamous Verma graph, the polynomials defining the graphical model are not determinantal, and hence cannot be explained by $d$-separation or trek-separation. In this paper, we show that these constraints often correspond to the vanishing of nested determinants and can be graphically explained by the (more general) notion of restricted trek separation.


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Mathias Drton. Elina Robeva. Luca Weihs. "Nested covariance determinants and restricted trek separation in Gaussian graphical models." Bernoulli 26 (4) 2503 - 2540, November 2020.


Received: 1 July 2018; Revised: 1 November 2019; Published: November 2020
First available in Project Euclid: 27 August 2020

zbMATH: 07256151
MathSciNet: MR4140520
Digital Object Identifier: 10.3150/19-BEJ1179

Rights: Copyright © 2020 Bernoulli Society for Mathematical Statistics and Probability


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Vol.26 • No. 4 • November 2020
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