On generating random Gaussian graphical models
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On generating random Gaussian graphical models. / Córdoba, Irene; Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro.
I: International Journal of Approximate Reasoning, Bind 125, 2020, s. 240-250.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - On generating random Gaussian graphical models
AU - Córdoba, Irene
AU - Varando, Gherardo
AU - Bielza, Concha
AU - Larrañaga, Pedro
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Algorithm validation
KW - Concentration graph
KW - Covariance graph
KW - Positive definite matrix simulation
KW - Undirected graphical model
UR - http://www.scopus.com/inward/record.url?scp=85089486344&partnerID=8YFLogxK
U2 - 10.1016/j.ijar.2020.07.007
DO - 10.1016/j.ijar.2020.07.007
M3 - Journal article
AN - SCOPUS:85089486344
VL - 125
SP - 240
EP - 250
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
SN - 0888-613X
ER -
ID: 248192670