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June 2013 Simple Marginally Noninformative Prior Distributions for Covariance Matrices
Alan Huang, M. P. Wand
Bayesian Anal. 8(2): 439-452 (June 2013). DOI: 10.1214/13-BA815

Abstract

A family of prior distributions for covariance matrices is studied. Members of the family possess the attractive property of all standard deviation and correlation parameters being marginally noninformative for particular hyperparameter choices. Moreover, the family is quite simple and, for approximate Bayesian inference techniques such as Markov chain Monte Carlo and mean field variational Bayes, has tractability on par with the Inverse-Wishart conjugate family of prior distributions. A simulation study shows that the new prior distributions can lead to more accurate sparse covariance matrix estimation.

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Alan Huang. M. P. Wand. "Simple Marginally Noninformative Prior Distributions for Covariance Matrices." Bayesian Anal. 8 (2) 439 - 452, June 2013. https://doi.org/10.1214/13-BA815

Information

Published: June 2013
First available in Project Euclid: 24 May 2013

zbMATH: 1329.62135
MathSciNet: MR3066948
Digital Object Identifier: 10.1214/13-BA815

Rights: Copyright © 2013 International Society for Bayesian Analysis

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Vol.8 • No. 2 • June 2013
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