Computational Aspects Related to Inference in Gaussian Graphical Models With the G-Wishart Prior

作者：Lenkoski Alex^*; Dobra Adrian

来源：Journal of Computational and Graphical Statistics, 2011, 20(1): 140-157.

DOI：10.1198/jcgs.2010.08181

• Bayesian model averaging
• Covariance estimation
• Covariance selection
• Multivariate regression
• Stochastic search

We describe a comprehensive framework for performing Bayesian inference for Gaussian graphical models based on the G-Wishart prior with a special focus on efficiently including nondecomposable graphs in the model space. We develop a new approximation method to the normalizing constant of a G-Wishart distribution based on the Laplace approximation. We review recent developments in stochastic search algorithms and propose a new method, the mode oriented stochastic search (MOSS), that extends these techniques and proves superior at quickly finding graphical models with high posterior probability. We then develop a novel stochastic search technique for multivariate regression models and conclude with a real-world example from the recent covariance estimation literature. Supplemental materials are available online.

• 出版日期2011-3