Graphical models are powerful tools that are regularly used to investigate complex
dependence structures in high-throughput biomedical datasets. They allow for holistic …

Gaussian graphical models provide a powerful framework to reveal the conditional
dependency structure between multivariate variables. The process of uncovering the …

In this article, we propose a Bayesian approach to inference on multiple Gaussian graphical
models. Specifically, we address the problem of inferring multiple undirected networks in …

Decoding complex relationships among large numbers of variables with relatively few
observations is one of the crucial issues in science. One approach to this problem is …

Gaussian graphical models (GGM;“networks”) allow for estimating conditional dependence
structures that are encoded by partial correlations. This is accomplished by identifying non …

Graphical models provide powerful tools to uncover complicated patterns in multivariate
data and are commonly used in Bayesian statistics and machine learning. In this paper, we …

Disease Map**: From Foundations to Multidimensional Modeling guides the reader from
the basics of disease map** to the most advanced topics in this field. A multidimensional …

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

This paper proposes a new algorithm for Bayesian model determination in Gaussian
graphical models under G-Wishart prior distributions. We first review recent development in …

A fundamental problem associated with the task of network reconstruction from dynamical or
behavioral data consists in determining the most appropriate model complexity in a manner …