Model choice using reversible jump Markov chain Monte Carlo

Hastie, DI; Green, PJ

Model choice using reversible jump Markov chain Monte Carlo

Hastie, DI Green, PJ

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Publication Type:: Journal Article
Citation:: Statistica Neerlandica, 2012, 66 (3), pp. 309 - 338
Issue Date:: 2012-08-01

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Field	Value	Language
dc.contributor.author	Hastie, DI	en_US
dc.contributor.author	Green, PJ https://orcid.org/0000-0002-4367-4756	en_US
dc.date.issued	2012-08-01	en_US
dc.identifier.citation	Statistica Neerlandica, 2012, 66 (3), pp. 309 - 338	en_US
dc.identifier.issn	0039-0402	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32396
dc.description.abstract	We review the across-model simulation approach to computation for Bayesian model determination, based on the reversible jump Markov chain Monte Carlo method. Advantages, difficulties and variations of the methods are discussed. We also discuss some limitations of the ideal Bayesian view of the model determination problem, for which no computational methods can provide a cure. © 2012 The Authors. Statistica Neerlandica © 2012 VVS.	en_US
dc.relation.ispartof	Statistica Neerlandica	en_US
dc.relation.isbasedon	10.1111/j.1467-9574.2012.00516.x	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Model choice using reversible jump Markov chain Monte Carlo	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	66	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	66	en_US

Abstract:

We review the across-model simulation approach to computation for Bayesian model determination, based on the reversible jump Markov chain Monte Carlo method. Advantages, difficulties and variations of the methods are discussed. We also discuss some limitations of the ideal Bayesian view of the model determination problem, for which no computational methods can provide a cure. © 2012 The Authors. Statistica Neerlandica © 2012 VVS.

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

http://hdl.handle.net/10453/32396