This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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347
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #307476 |
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Title:
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A Metropolis-Hastings--Based Method for Sampling from the G-Wishart Conjugate Prior in Gaussian Graphical Models
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Author(s):
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Nicholas Mitsakakis*+ and Helene Massam and Michael Escobar
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Companies:
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Dalla Lana School of Public Health and York University and Dalla Lana School of Public Health
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Address:
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808 Carlaw Ave, Toronto, ON, M4K 3L2, Canada
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Keywords:
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Gaussian Graphical Models ;
G-Wishart distribution ;
Metropolis-Hastings ;
Deviance Information Criterion ;
Choleski decomposition ;
covariance selection
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Abstract:
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Gaussian Graphical Models are tools for the investigation and representation of interdependencies between components of continuous random vectors. This thesis aims to investigate some issues related to the application of Bayesian methods for Gaussian Graphical Models. We adopt the popular $G$-Wishart conjugate prior $W_G(\delta,D)$ for the precision matrix. We propose an efficient sampling method for the $G$-Wishart distribution based on the Metropolis Hastings algorithm and show its validity through a number of numerical experiments. We show that this method can be easily used to estimate the Deviance Information Criterion, providing with a computationally inexpensive approach for model selection.
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