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A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models. (English) Zbl 1274.65009

Summary: In Gaussian graphical models, the conjugate prior for the precision matrix \(K\) is called \(G\)-Wishart distribution, \(W_{G}(\delta ,D)\). In this paper we propose a new sampling method for the \(W_{G}(\delta,D)\) based on the Metropolis-Hastings algorithm and we 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.

MSC:

65C60 Computational problems in statistics (MSC2010)

Software:

BayesDA
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References:

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