Using infer.NET for statistical analyses

Wang, SSJ; Wand, MP

Using infer.NET for statistical analyses

Wang, SSJ Wand, MP

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Publication Type:: Journal Article
Citation:: American Statistician, 2011, 65 (2), pp. 115 - 126
Issue Date:: 2011-05-01

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Field	Value	Language
dc.contributor.author	Wang, SSJ	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2011-05-01	en_US
dc.identifier.citation	American Statistician, 2011, 65 (2), pp. 115 - 126	en_US
dc.identifier.issn	0003-1305	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17929
dc.description.abstract	We demonstrate and critique the new Bayesian inference package Infer.NET in terms of its capacity for statistical analyses. Infer.NET differs from the well-known BUGS Bayesian inference packages in that its main engine is the variational Bayes family of deterministic approximation algorithms rather than Markov chain Monte Carlo. The underlying rationale is that such deterministic algorithms can handle bigger problems due to their increased speed, despite some loss of accuracy. We find that Infer.NET is a well-designed computational framework and offers significant speed advantages over BUGS. Nevertheless, the current release is limited in terms of the breadth of models it can handle, and its inference is sometimes inaccurate. Supplemental materials accompany the online version of this article. © 2011 American Statistical Association.	en_US
dc.relation.ispartof	American Statistician	en_US
dc.relation.isbasedon	10.1198/tast.2011.10169	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Using infer.NET for statistical analyses	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	65	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	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	65	en_US

Abstract:

We demonstrate and critique the new Bayesian inference package Infer.NET in terms of its capacity for statistical analyses. Infer.NET differs from the well-known BUGS Bayesian inference packages in that its main engine is the variational Bayes family of deterministic approximation algorithms rather than Markov chain Monte Carlo. The underlying rationale is that such deterministic algorithms can handle bigger problems due to their increased speed, despite some loss of accuracy. We find that Infer.NET is a well-designed computational framework and offers significant speed advantages over BUGS. Nevertheless, the current release is limited in terms of the breadth of models it can handle, and its inference is sometimes inaccurate. Supplemental materials accompany the online version of this article. © 2011 American Statistical Association.

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http://hdl.handle.net/10453/17929