In recent years, probabilistic graphical models have emerged as a powerful framework for
understanding complex dependencies in multivariate data, offering a structured approach to …

Bayesian methods for learning Gaussian graphical models offer a robust framework that
addresses model uncertainty and incorporates prior knowledge. Despite their theoretical …

Gaussian graphical models depict the conditional dependencies between variables within a
multivariate normal distribution in a graphical format. The identification of these graph …

Graphical models describe associations between variables through the notion of conditional
independence. Gaussian graphical models are a widely used class of such models where …

Bayesian inference for graphical models has received much attention in the literature in
recent years. It is well known that, when the graph G is decomposable, Bayesian inference is …

Graphical models [12] provide a probabilistic framework to characterize the multivariate
dependency structure among random variables. These models have received considerable …

A nonparanormal graphical model is a semiparametric generalization of a Gaussian
graphical model for continuous variables in which it is assumed that the variables follow a …

We consider the inference of the structure of an undirected graphical model in a Bayesian
framework. To avoid convergence issues and highly demanding Monte Carlo sampling, we …

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

We consider the problem of estimating a sparse precision matrix of a multivariate Gaussian
distribution, where the dimension p may be large. Gaussian graphical models provide an …