A Metropolis-Hastings based method for sampling from the G-Wishart distribution in Gaussian graphical models

Abstract: In Gaussian graphical models, the conjugate prior for the precision matrix K is called G-Wishart distribution, WG(δ,D). In this paper we propose a new sampling method for the WG(δ,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.

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