On generating random Gaussian graphical models

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

  • Irene Córdoba
  • Gherardo Varando
  • Concha Bielza
  • Pedro Larrañaga

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.

OriginalsprogEngelsk
TidsskriftInternational Journal of Approximate Reasoning
Vol/bind125
Sider (fra-til)240-250
Antal sider11
ISSN0888-613X
DOI
StatusUdgivet - 2020

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