Generalised Extreme Value geoadditive model analysis via variational Bayes

Neville, SE; Wand, MP

Generalised Extreme Value geoadditive model analysis via variational Bayes

Neville, SE Wand, MP

Permalink

Publication Type:: Conference Proceeding
Citation:: Procedia Environmental Sciences, 2011, 3 pp. 8 - 13
Issue Date:: 2011-01-01

Closed Access

	Filename	Description	Size
	2011004583OK.pdf		212.8 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Neville, SE	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	Procedia Environmental Sciences, 2011, 3 pp. 8 - 13	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19063
dc.description.abstract	We devise a variational Bayes algorithm 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 Monte Carlo methods. © 2010 Published by Elsevier Ltd. Ltd.	en_US
dc.relation.ispartof	Procedia Environmental Sciences	en_US
dc.relation.isbasedon	10.1016/j.proenv.2011.02.003	en_US
dc.title	Generalised Extreme Value geoadditive model analysis via variational Bayes	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3	en_US
utslib.for	0104 Statistics	en_US
dc.location.activity	Enschede	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.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

We devise a variational Bayes algorithm 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 Monte Carlo methods. © 2010 Published by Elsevier Ltd. Ltd.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/19063