Bayesian Analysis

Simple Marginally Noninformative Prior Distributions for Covariance Matrices

Alan Huang and M. P. Wand

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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.

Article information

Source
Bayesian Anal. Volume 8, Number 2 (2013), 439-452.

Dates
First available in Project Euclid: 24 May 2013

Permanent link to this document
https://projecteuclid.org/euclid.ba/1369407559

Digital Object Identifier
doi:10.1214/13-BA815

Mathematical Reviews number (MathSciNet)
MR3066948

Zentralblatt MATH identifier
1329.62135

Keywords
Bayesian inference Gibbs sampling Markov chain Monte Carlo Mean field variational Bayes

Citation

Huang, Alan; Wand, M. P. Simple Marginally Noninformative Prior Distributions for Covariance Matrices. Bayesian Anal. 8 (2013), no. 2, 439--452. doi:10.1214/13-BA815. https://projecteuclid.org/euclid.ba/1369407559


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