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Computational aspects related to inference in Gaussian graphical models with the G-Wishart prior

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...

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Bibliographic Details
Main Authors: Lenkoski, Alex (Author) , Dobra, Adrian (Author)
Format: Article (Journal)
Language:English
Published: 2011
In: Journal of computational and graphical statistics
Year: 2011, Volume: 20, Issue: 1, Pages: 140-157
ISSN:1537-2715
DOI:10.1198/jcgs.2010.08181
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1198/jcgs.2010.08181
Verlag, lizenzpflichtig, Volltext: https://www.tandfonline.com/doi/abs/10.1198/jcgs.2010.08181
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Author Notes:Alex Lenkoski and Adrian Dobra

MARC

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520 |a 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. 
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