Learning Sparse Gaussian Graphical Models with Overlapping Blocks

Learning Gaussian Graphical Models with Overlapping Blocks

On Sparse Gaussian Chain Graph Models

In this paper, we address the problem of learning the structure of Gaussian chain graph models in a high-dimensional space. Chain graph models are generalizations of undirected and directed graphical models that contain a mixed set of directed and undirected edges. While the problem of sparse structure learning has been studied extensively for Gaussian graphical models and more recently for con...

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 Latent Variable Gaussian Graphical Models

Gaussian graphical models (GGM) have been widely used in many highdimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. In this paper, we focus on a family of latent variable Gaussian graphical model...

Joint Structural Estimation of Multiple Graphical Models

Gaussian graphical models capture dependence relationships between random variables through the pattern of nonzero elements in the corresponding inverse covariance matrices. To date, there has been a large body of literature on both computational methods and analytical results on the estimation of a single graphical model. However, in many application domains, one has to estimate several relate...