MAD-Bayes matching and alignment for labelled and unlabelled configurations

Green, PJ

MAD-Bayes matching and alignment for labelled and unlabelled configurations

Green, PJ

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Publication Type:: Chapter
Citation:: Geometry Driven Statistics, 2015, pp. 377 - 390
Issue Date:: 2015-11-27

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Field	Value	Language
dc.contributor.author	Green, PJ https://orcid.org/0000-0002-4367-4756	en_US
dc.date.issued	2015-11-27	en_US
dc.identifier.citation	Geometry Driven Statistics, 2015, pp. 377 - 390	en_US
dc.identifier.isbn	9781118866573	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120388
dc.description.abstract	© 2015 John Wiley & Sons, Ltd. All rights reserved. Professor Kanti Mardia has made numerous original contributions to an area of unlabelled shape analysis inspired by matching and alignment problems arising in protein bioinformatics. In this chapter, a Bayesian model proposed by Mardia and Green in 2006, and others related to it, are revisited to investigate the potential for using modern optimisation algorithms to expedite calculations of properties of the posterior distribution, in place of the Monte Carlo computational methods originally proposed.	en_US
dc.relation.ispartof	Geometry Driven Statistics	en_US
dc.relation.isbasedon	10.1002/9781118866641.ch19	en_US
dc.title	MAD-Bayes matching and alignment for labelled and unlabelled configurations	en_US
dc.type	Chapter
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.publication-status	Published	en_US

Abstract:

© 2015 John Wiley & Sons, Ltd. All rights reserved. Professor Kanti Mardia has made numerous original contributions to an area of unlabelled shape analysis inspired by matching and alignment problems arising in protein bioinformatics. In this chapter, a Bayesian model proposed by Mardia and Green in 2006, and others related to it, are revisited to investigate the potential for using modern optimisation algorithms to expedite calculations of properties of the posterior distribution, in place of the Monte Carlo computational methods originally proposed.

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http://hdl.handle.net/10453/120388