Gaussian concentration graph models and covariance graph models are two classes of
graphical models that are useful for uncovering latent dependence structures among …

Graphical models provide a framework for exploration of multivariate dependence patterns.
The connection between graph and statistical model is made by identifying the vertices of …

We consider estimation of the covariance matrix of a multivariate random vector under the
constraint that certain covariances are zero. We first present an algorithm, which we call …

We study parameter estimation in linear Gaussian covariance models, which are p-
dimensional Gaussian models with linear constraints on the covariance matrix. Maximum …

Gaussian covariance graph models encode marginal independence among the
components of a multivariate random vector by means of a graph G. These models are …

Ordered sequences of univariate or multivariate regressions provide statistical models for
analysing data from randomized, possibly sequential interventions, from cohort or multi …

We study the role of partial autocorrelations in the reparameterization and parsimonious
modeling of a covariance matrix. The work is motivated by and tries to mimic the …

Finite Gaussian mixture models are widely used for model-based clustering of continuous
data. Nevertheless, since the number of model parameters scales quadratically with the …

A set of independence statements may define the independence structure of interest in a
family of joint probability distributions. This structure is often captured by a graph that …

In graphical modelling, a bi-directed graph encodes marginal independences among
random variables that are identified with the vertices of the graph. We show how to transform …