Learning in Graphical Gaussian Models
Paolo Giudici · 1996
Abstract We address the problem of statistical learning in a graphical Gaussian model from a Bayesian viewpoint. Two classes of prior distributions on the variance-covariance matrix which parameterises the model are introduced and compared. The implications on graphical model selection are discussed.