Graphic modelling is a form of multivariate analysis that uses graphs to represent models.
These graphs display the structure of dependencies, both associational and causal …

This paper introduces a class of graphical independence models that is closed under
marginalization and conditioning but that contains all DAG independence models. This class …

Large observational studies involving research questions that require the measurement of
several features on each individual arise in many fields including the social and medical …

We consider acyclic directed mixed graphs, in which directed edges (x→ y) and bi‐directed
edges (x↔ y) may occur. A simple extension of Pearl's d‐separation criterion, called m …

A multivariate Gaussian graphical Markov model for an undirected graph G, also called a
covariance selection model or concentration graph model, is defined in terms of the Markov …

Graphical Markov models use graphs to represent possible dependences among statistical
variables. Lauritzen, Wermuth, and Frydenberg (LWF) introduced a Markov property for …

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 …

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

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 …

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