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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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2011
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| 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 |
| Summary: | 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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| Item Description: | Elektronische Reprodutkion der Druck-Ausgabe Published online: 01 Jan 20102 Gesehen am 16.08.2022 |
| Physical Description: | Online Resource |
| ISSN: | 1537-2715 |
| DOI: | 10.1198/jcgs.2010.08181 |