M. Banerjee and T. Richardson, On a Dualization of Graphical Gaussian Models: A Correction Note, Scandinavian Journal of Statistics, vol.27, issue.4, pp.817-820, 2003.
DOI : 10.1111/1467-9469.00323

S. L. Buhl, On the existence of maximum likelihood estimators for graphical gaussian models. Scan, Journal of Statistic, vol.20, pp.263-270, 1993.

R. Castelo and A. Roverato, A robust procedure for gaussian graphical models search for microarray data with p larger than n, Journal of Machine Learning Research, vol.57, pp.2621-2650, 2006.

D. Edwards, Introduction to graphical modelling, 2000.

N. Friedman, M. Linial, I. Nachman, and D. Pe-'er, Using bayesian networks to analyse expression data, J. Comput. Biol, vol.7, pp.3-4, 2000.

K. Khare and B. Rajaratnam, Conjugate Wishart distributions for covariance graph models, 2008.

S. L. Lauritzen, Graphical Models, 1996.

P. Magwene and J. Kim, Estimating genomic coexpression networks using firstorder conditional independence, Genom Biol, vol.5, issue.12, 2004.

D. Malouche and S. Sevestre-ghalila, Estimating high dimensional faithful gaussian graphical models by low-order conditioning, Proceeding, of 26th IASTED International Multi-Conference on Applied Informatics, pp.595-620, 2008.

H. Toh and K. Horimoto, Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling, Bioinformatics, vol.18, issue.2, pp.287-297, 2002.
DOI : 10.1093/bioinformatics/18.2.287

A. Wille and P. Bühlman, Low-Order Conditional Independence Graphs for Inferring Genetic Networks, Statistical Applications in Genetics and Molecular Biology, vol.5, issue.1, pp.1-32, 2006.
DOI : 10.2202/1544-6115.1170