Bayesian Gaussian Graphical models using sparse selection priors and their mixtures
We propose Bayesian methods for estimating the precision matrix in Gaussian graphical models. The methods lead to sparse and adaptively shrunk estimators of the precision matrix, and thus conduct model selection and estimation simultaneously. Our methods are based on selection and shrinkage priors l...
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| Format: | Others |
| Language: | en_US |
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2012
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| Online Access: | http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828 |