On generating random Gaussian graphical models

Copula Gaussian Graphical Models *

We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models and embed graphical model selection inside a semiparametric Gaussian copula. The domain of applicability of our methods is very broad and encompass many stud...

Generating Gaussian Pseudo-Random Deviates

Generating Cosmological Gaussian Random Fields

We present a generic algorithm for generating Gaussian random initial conditions for cosmological simulations on periodic rectangular lattices. We show that imposing periodic boundary conditions on the real-space correlator and choosing initial conditions by convolving a white noise random field results in a significantly smaller error than the traditional procedure of using the power spectrum....

Groups acting on Gaussian graphical models

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Testing Unfaithful Gaussian Graphical Models

The global Markov property for Gaussian graphical models ensures graph separation implies conditional independence. Specifically if a node set S graph separates nodes u and v then Xu is conditionally independent of Xv given XS . The opposite direction need not be true, that is, Xu ⊥ Xv | XS need not imply S is a node separator of u and v. When it does, the relation Xu ⊥ Xv | XS is called faithf...