We discuss the implementation, development and performance of methods of stochastic
computation in Gaussian graphical models. We view these methods from the perspective of …

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

Copula models have become one of the most widely used tools in the applied modelling of
multivariate data. Similarly, Bayesian methods are increasingly used to obtain efficient …

We propose a Bayesian approach for inference in the multivariate probit model, taking into
account the association structure between binary observations. We model the association …

Supplement to “Bayesian inference for multiple Gaussian graphical models with application
to metabolic association networks”. We provide additional material to support the results in …

1.1 Machine learning: what and why? 1 1.1. 1 Types of machine learning 2 1.2 Supervised
learning 3 1.2. 1 Classification 3 1.2. 2 Regression 8 1.3 Unsupervised learning 9 1.3. 1 …

Standard Gaussian graphical models implicitly assume that the conditional independence
among variables is common to all observations in the sample. However, in practice …

Bayesian graphical modeling provides an appealing way to obtain uncertainty estimates
when inferring network structures, and much recent progress has been made for Gaussian …

While much work has explored probabilistic graphical models for independent data, less
attention has been paid to time series. The goal in this setting is to determine conditional …

An objective Bayes approach based on graphical modeling is proposed to learn the
contemporaneous dependencies among multiple time series within the framework of Vector …