When considering a graphical Gaussian model NG Markov with respect to a decomposable graph G, the parameter space of interest for the precision parameter is the cone PG of positive definite matrices… (More)

In this paper, we propose a class of Bayes estimators for the covariance matrix of graphical Gaussian models Markov with respect to a decomposable graph G. Working with the WPG family we derive… (More)

We introduce and exemplify an efficient method for direct sampling from hyperinverse Wishart distributions. The method relies very naturally on the use of standard junction-tree representation of… (More)

In Bayesian analysis of multi-way contingency tables, the selection of a prior distribution for either the log-linear parameters or the cell probabilities parameters is a major challenge. In this… (More)

We describe a novel stochastic search algorithm for rapidly identifying regions of high posterior probability in the space of decomposable, graphical and hierarchical log-linear models. Our approach… (More)

In this paper, we compute moments of a Wishart matrix variate U of the form E(Q(U)) where Q(u) is a polynomial with respect to the entries of the symmetric matrix u, invariant in the sense that it… (More)

Stanford University For a random matrix following a Wishart distribution, we derive formulas for the expectation and the covariance matrix of compound matrices. The compound matrix of order m is… (More)

For a given discrete decomposable graphical model, we identify several alternative parametrizations, and construct the corresponding reference priors for suitable groupings of the parameters.… (More)

We present a Bayesian variable selection procedure that is applicable to genomewide studies involving a combination of clinical, gene expression and genotype information. We use the Mode Oriented… (More)