Simple marginally noninformative prior distributions for covariance matrices

Huang, A; Wand, MP

Simple marginally noninformative prior distributions for covariance matrices

Huang, A

Wand, MP

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Publication Type:: Journal Article
Citation:: Bayesian Analysis, 2013, 8 (2), pp. 439 - 452
Issue Date:: 2013-06-10

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Field	Value	Language
dc.contributor.author	Huang, A https://orcid.org/0000-0002-7848-6755	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2013-06-10	en_US
dc.identifier.citation	Bayesian Analysis, 2013, 8 (2), pp. 439 - 452	en_US
dc.identifier.issn	1936-0975	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27857
dc.description.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 hyper-parameter choices. Moreover, the family is quite simple and, for approximate Bayesian inference techniques such as Markov chain Monte Carlo and mean eld variational Bayes, has tractability on par with the Inverse-Wishart conjugate fam-ily of prior distributions. A simulation study shows that the new prior distributions can lead to more accurate sparse covariance matrix estimation. © 2013 International Society for Bayesian Analysis.	en_US
dc.relation.ispartof	Bayesian Analysis	en_US
dc.relation.isbasedon	10.1214/13-BA815	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Simple marginally noninformative prior distributions for covariance matrices	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	8	en_US
utslib.for	0104 Statistics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical Sciences
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

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 hyper-parameter choices. Moreover, the family is quite simple and, for approximate Bayesian inference techniques such as Markov chain Monte Carlo and mean eld variational Bayes, has tractability on par with the Inverse-Wishart conjugate fam-ily of prior distributions. A simulation study shows that the new prior distributions can lead to more accurate sparse covariance matrix estimation. © 2013 International Society for Bayesian Analysis.

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http://hdl.handle.net/10453/27857