Lattices of Graphical Gaussian Models with Symmetries

Lattices of Graphical Gaussian Models with Symmetries

In order to make graphical Gaussian models a viable modelling tool when the number of variables outgrows the number of observations, [1] introduced model classes which place equality restrictions on concentrations or partial correlations. The models can be represented by vertex and edge coloured graphs. The need for model selection methods makes it imperative to understand the structure of mode...

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...

Learning Gaussian Graphical Models with Overlapping Blocks

We propose the GRAB framework (GRaphical models with overlApping Blocks), a novel general framework to explicitly model densely connected sub-networks in a graphical model. The GRAB learning algorithm takes as input a data matrix of p variables and n samples, and jointly learns both a network among p variables and densely connected groups of variables (called ‘blocks’). GRAB has three major nov...

Learning Gaussian Graphical Models with Overlapping Blocks

Unsupervised Learning with Truncated Gaussian Graphical Models

Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling abilities, due to the Gaussian assumption. In this paper, we introduce a novel variant of GGMs, which relaxes the Gaussian restriction and yet admits efficien...