Variational inference for heteroscedastic and longitudinal regression models

Menictas, M

Variational inference for heteroscedastic and longitudinal regression models

Menictas, M

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Publication Type:: Thesis
Issue Date:: 2015

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Field	Value	Language
dc.contributor.author	Menictas, M
dc.date.accessioned	2015-11-29T22:43:08Z
dc.date.available	2015-11-29T22:43:08Z
dc.date.issued	2015
dc.identifier.uri	http://hdl.handle.net/10453/38942
dc.description	University of Technology Sydney. Faculty of Science.	en_AU
dc.description.abstract	The focus of this thesis is on the development and assessment of mean field variational Bayes (MFVB), which is a fast, deterministic tool for inference in a Bayesian hierarchical model setting. We assess the performance of MFVB via the use of comprehensive comparisons against a Markov chain Monte Carlo (MCMC) benchmark. Each of the models considered are special cases of semiparametric regression. In particular, we focus on the development and assessment of the performance of MFVB for heteroscedastic and longitudinal semiparametric regression models. Generally, the new MFVB methodology performs well in its assessment of accuracy against MCMC for the semiparametric and nonparametric regression models considered in this thesis. It is also much faster and is shown to be applicable to real-time analyses. Several real data illustrations are provided. Altogether, MFVB proves to be a credible inference tool and a good alternative to MCMC, especially when analysis is hindered by time constraints.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/38942/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.subject	Mean field variational Bayes (MFVB).	en
dc.subject	Bayesian hierarchical model setting.	en
dc.subject	Markov chain Monte Carlo (MCMC) benchmark.	en
dc.subject	Heteroscedastic and longitudinal semiparametric regression models.	en
dc.title	Variational inference for heteroscedastic and longitudinal regression models	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

The focus of this thesis is on the development and assessment of mean field variational Bayes (MFVB), which is a fast, deterministic tool for inference in a Bayesian hierarchical model setting. We assess the performance of MFVB via the use of comprehensive comparisons against a Markov chain Monte Carlo (MCMC) benchmark. Each of the models considered are special cases of semiparametric regression. In particular, we focus on the development and assessment of the performance of MFVB for heteroscedastic and longitudinal semiparametric regression models. Generally, the new MFVB methodology performs well in its assessment of accuracy against MCMC for the semiparametric and nonparametric regression models considered in this thesis. It is also much faster and is shown to be applicable to real-time analyses. Several real data illustrations are provided. Altogether, MFVB proves to be a credible inference tool and a good alternative to MCMC, especially when analysis is hindered by time constraints.

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http://hdl.handle.net/10453/38942