Graphical models form a cornerstone of modern data analysis by providing a visually intuitive framework to represent and reason about the complex interdependencies among variables. In particular, ...

Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a ...

Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

Journal of Computational and Graphical Statistics, Vol. 22, No. 3, Special Issue: Advances in Markov Chain Monte Carlo (September 2013), pp. 729-748 (20 pages) This article describes methods for ...