Functional Graphical Models

Functional Graphical Models *

Graphical models have attracted increasing attention in recent years, especially in settings involving high dimensional data. In particular Gaussian graphical models are used to model the conditional dependence structure among p Gaussian random variables. As a result of its computational efficiency the graphical lasso (glasso) has become one of the most popular approaches for fitting high dimen...

Functional Graphical Models

Functional models are frequently used in computer vision and photogrammetry, as they enable the mathematical formulation of several problems such as pose computation and more generally the parameter estimation problem. However, the structural properties of such models have only seldom been studied. This contribution is dedicated to the analysis of such properties. We propose a new formalism tha...

Graphical models for functional connectivity

A lack of functional integration has been proposed as a signature of many degenerative and developmental disorders, including schizophrenia and autism (Just et al., 2004). These connectivity patterns are often assessed with ad-hoc techniques based on pairwise correlations. The procedures may lead to a misleading characterization of the underlying neural connectivity pattern, because these techn...

Bayesian Graphical Models for Multivariate Functional Data

Graphical models express conditional independence relationships among variables. Although methods for vector-valued data are well established, functional data graphical models remain underdeveloped. By functional data, we refer to data that are realizations of random functions varying over a continuum (e.g., images, signals). We introduce a notion of conditional independence between random func...

Graphical Models of Functional MRI Data for Assessing Brain Connectivity