Fully simplified multivariate normal updates in non-conjugate variational message passing

Wand, MP

Fully simplified multivariate normal updates in non-conjugate variational message passing

Wand, MP

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Publication Type:: Journal Article
Citation:: Journal of Machine Learning Research, 2014, 15 pp. 1351 - 1369
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Journal of Machine Learning Research, 2014, 15 pp. 1351 - 1369	en_US
dc.identifier.issn	1532-4435	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41190
dc.description.abstract	Fully simplified expressions for Multivariate Normal updates in non-conjugate variational message passing approximate inference schemes are obtained. The simplicity of these expressions means that the updates can be achieved very eficiently. Since the Multivariate Normal family is the most common for approximating the joint posterior density function of a continuous parameter vector, these fully simplified updates are of great practical benefit. © 2014 Matt P. Wand.	en_US
dc.relation.ispartof	Journal of Machine Learning Research	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Fully simplified multivariate normal updates in non-conjugate variational message passing	en_US
dc.type	Journal Article
utslib.citation.volume	15	en_US
utslib.for	0104 Statistics	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	17 Psychology and Cognitive Sciences	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.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

Fully simplified expressions for Multivariate Normal updates in non-conjugate variational message passing approximate inference schemes are obtained. The simplicity of these expressions means that the updates can be achieved very eficiently. Since the Multivariate Normal family is the most common for approximating the joint posterior density function of a continuous parameter vector, these fully simplified updates are of great practical benefit. © 2014 Matt P. Wand.

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http://hdl.handle.net/10453/41190