Generalized extreme value additive model analysis via mean field variational bayes

Neville, SE; Palmer, MJ; Wand, MP

Generalized extreme value additive model analysis via mean field variational bayes

Neville, SE Palmer, MJ Wand, MP

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Publication Type:: Journal Article
Citation:: Australian and New Zealand Journal of Statistics, 2011, 53 (3), pp. 305 - 330
Issue Date:: 2011-09-01

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Field	Value	Language
dc.contributor.author	Neville, SE	en_US
dc.contributor.author	Palmer, MJ	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2011-09-01	en_US
dc.identifier.citation	Australian and New Zealand Journal of Statistics, 2011, 53 (3), pp. 305 - 330	en_US
dc.identifier.issn	1369-1473	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17958
dc.description.abstract	We develop Mean Field Variational Bayes methodology for fast approximate inference in Bayesian Generalized Extreme Value additive model analysis. Such models are useful for flexibly assessing the impact of continuous predictor variables on sample extremes. The new methodology allows large Bayesian models to be fitted and assessed without the significant computing costs of Markov Chain Monte Carlo methods. We illustrate our new methodology with maximum rainfall data from the Sydney, Australia, hinterland. Comparisons are made between the Mean Field Variational Bayes and Markov Chain Monte Carlo approaches. © 2012 Australian Statistical Publishing Association Inc..	en_US
dc.relation.ispartof	Australian and New Zealand Journal of Statistics	en_US
dc.relation.isbasedon	10.1111/j.1467-842X.2011.00637.x	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Generalized extreme value additive model analysis via mean field variational bayes	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	53	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	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
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	53	en_US

Abstract:

We develop Mean Field Variational Bayes methodology for fast approximate inference in Bayesian Generalized Extreme Value additive model analysis. Such models are useful for flexibly assessing the impact of continuous predictor variables on sample extremes. The new methodology allows large Bayesian models to be fitted and assessed without the significant computing costs of Markov Chain Monte Carlo methods. We illustrate our new methodology with maximum rainfall data from the Sydney, Australia, hinterland. Comparisons are made between the Mean Field Variational Bayes and Markov Chain Monte Carlo approaches. © 2012 Australian Statistical Publishing Association Inc..

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http://hdl.handle.net/10453/17958