matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real …

-phasis on Dempster covariance selection method in part one is followed in the second part by a description of a modi-fication of …

) de-scription of a modification of Dempster covariance selection algorithm based on its combination with that of tree …

evaluate the prior apply generally to any decomposable graphical model. KEY WORDS: Covariance selection; Graphical models … gains in efficiency are possible. Dempster (1972) advocates a covariance selection approach to estimate a covariance matrix … the covariance matrix based on their selected graph. A number of articles take a Bayesian approach to covariance selection …

value than (C1logp/n)1/2 for some constant C1, we can obtain covariance selection consistency by thresholding the sample …

In this paper we consider some methods for the maximum likelihood estimation of sparse Gaussian graphical (covariance … selection) models when the number of variables is very large (tens of thousands or more). We present a procedure for determining …