We introduce efficient Markov chain Monte Carlo methods for inference and model
determination in multivariate and matrix-variate Gaussian graphical models. Our framework …

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by
providing not only point estimates but also uncertainty in the clustering structure and …

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 …

Abstract We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-
GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset …

In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no
extra variables affecting the conditional independence. In modern genomic datasets, there is …

The G‐Wishart distribution is the conjugate prior for precision matrices that encode the
conditional independence of a Gaussian graphical model. Although the distribution has …

Finite Gaussian mixture models are widely used for model-based clustering of continuous
data. Nevertheless, since the number of model parameters scales quadratically with the …

We develop a scalable multistep Monte Carlo algorithm for inference under a large class of
nonparametric Bayesian models for clustering and classification. Each step is …

A convenient way of modelling complex interactions is by employing graphs or networks
which correspond to conditional independence structures in an underlying statistical model …

Background Computational network biology is an emerging interdisciplinary research area.
Among many other network approaches, probabilistic graphical models provide a …