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On generating random Gaussian graphical models
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DOI：10.1016/j.ijar.2020.07.007.png)
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Abstract
En 中文
Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (ii) a compatible symmetric positive definite (SPD) matrix. In order to ensure positive definiteness in (ii), a dominant diagonal is usually imposed. In this work we investigate different methods to generate random symmetric positive definite matrices with undirected graphical constraints. We show that if the graph is chordal it is possible to sample uniformly from the set of correlation matrices compatible with the graph, while for general undirected graphs we rely on a partial orthogonalization method. (C) 2020 Elsevier Inc. All rights reserved.
Keywords:
Concentration graph
Covariance graph
Positive definite matrix simulation
Undirected graphical model
Algorithm validation
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