Semiparametric regression analysis via Infer.NET

Luts, J; Wang, SSJ; Ormerod, JT; Wand, MP

Semiparametric regression analysis via Infer.NET

Luts, J Wang, SSJ Ormerod, JT Wand, MP

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Publication Type:: Journal Article
Citation:: Journal of Statistical Software, 2018, 87 (2)
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Luts, J	en_US
dc.contributor.author	Wang, SSJ	en_US
dc.contributor.author	Ormerod, JT	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	Journal of Statistical Software, 2018, 87 (2)	en_US
dc.identifier.issn	1548-7660	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128704
dc.description.abstract	© 2018, American Statistical Association. All rights reserved. We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models. The examples are chosen to encompass a wide range of semiparametric regression situations. Infer.NET is shown to produce accurate inference in comparison with Markov chain Monte Carlo via the BUGS package, but to be considerably faster. Potentially, this contribution represents the start of a new era for semiparametric regression, where large and complex analyses are performed via fast Bayesian inference methodology and software, mainly being developed within Machine Learning.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110100061
dc.relation.ispartof	Journal of Statistical Software	en_US
dc.relation.isbasedon	10.18637/jss.v087.i02	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Semiparametric regression analysis via Infer.NET	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	87	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
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	87	en_US

Abstract:

© 2018, American Statistical Association. All rights reserved. We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models. The examples are chosen to encompass a wide range of semiparametric regression situations. Infer.NET is shown to produce accurate inference in comparison with Markov chain Monte Carlo via the BUGS package, but to be considerably faster. Potentially, this contribution represents the start of a new era for semiparametric regression, where large and complex analyses are performed via fast Bayesian inference methodology and software, mainly being developed within Machine Learning.

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http://hdl.handle.net/10453/128704