Electronic Journal of Statistics

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

Nicholas Mitsakakis, Hélène Massam, and Michael D. Escobar

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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.

Article information

Source
Electron. J. Statist. Volume 5 (2011), 18-30.

Dates
First available in Project Euclid: 19 January 2011

Permanent link to this document
http://projecteuclid.org/euclid.ejs/1295457468

Digital Object Identifier
doi:10.1214/11-EJS594

Mathematical Reviews number (MathSciNet)
MR2763796

Zentralblatt MATH identifier
1274.65009

Keywords
Gaussian graphical models G-Wishart distribution Metropolis-Hastings algorithm non-decomposable graphs Deviance Information Criterion

Citation

Mitsakakis, Nicholas; Massam, Hélène; D. Escobar, Michael. A Metropolis-Hastings based method for sampling from the G -Wishart distribution in Gaussian graphical models. Electron. J. Statist. 5 (2011), 18--30. doi:10.1214/11-EJS594. http://projecteuclid.org/euclid.ejs/1295457468.


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