The explicit form of expectation propagation for a simple statistical model

Kim, ASI; Wand, MP

The explicit form of expectation propagation for a simple statistical model

Kim, ASI Wand, MP

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Publication Type:: Journal Article
Citation:: Electronic Journal of Statistics, 2016, 10 (1), pp. 550 - 581
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Kim, ASI	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Electronic Journal of Statistics, 2016, 10 (1), pp. 550 - 581	en_US
dc.identifier.issn	1935-7524	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43963
dc.description.abstract	© 2016, Institute of Mathematical Statistics. All rights reserved. We derive the explicit form of expectation propagation for approximate deterministic Bayesian inference in a simple statistical model. The model corresponds to a random sample from the Normal distribution. The explicit forms, and their derivation, allow a deeper understanding of the issues and challenges involved in practical implementation of expectation propagation for statistical analyses. No auxiliary approximations are used: we follow the expectation propagation prescription exactly. A simulation study shows expectation propagation to be more accurate than mean field variational Bayes for larger sample sizes, but at the cost of considerably more algebraic and computational effort.	en_US
dc.relation.ispartof	Electronic Journal of Statistics	en_US
dc.relation.isbasedon	10.1214/16-EJS1114	en_US
dc.title	The explicit form of expectation propagation for a simple statistical model	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	10	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	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

© 2016, Institute of Mathematical Statistics. All rights reserved. We derive the explicit form of expectation propagation for approximate deterministic Bayesian inference in a simple statistical model. The model corresponds to a random sample from the Normal distribution. The explicit forms, and their derivation, allow a deeper understanding of the issues and challenges involved in practical implementation of expectation propagation for statistical analyses. No auxiliary approximations are used: we follow the expectation propagation prescription exactly. A simulation study shows expectation propagation to be more accurate than mean field variational Bayes for larger sample sizes, but at the cost of considerably more algebraic and computational effort.

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http://hdl.handle.net/10453/43963