Semiparametric regression via message passing algorithms

Kim, Sang Il

Semiparametric regression via message passing algorithms

Kim, Sang Il

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Publication Type:: Thesis
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Kim, Sang Il
dc.date.accessioned	2019-05-13T01:28:13Z
dc.date.available	2019-05-13T01:28:13Z
dc.date.issued	2019
dc.identifier.uri	http://hdl.handle.net/10453/133327
dc.description	University of Technology Sydney. Faculty of Science.	en_AU
dc.description.abstract	Message passing algorithms are a group of fast, deterministic inference algorithms originated from the field of computer science. The focus of this thesis is on transferral of message passing algorithms into the major statistics field of semiparametric regression. We work on unveiling variational message passing (VMP) and expectation propagation (EP) from statistical perspective and developing explicit computable algorithms via VMP and EP for approximate statistical inference on model parameters in various regression models. We also contribute on demonstrating the notion of existing factor graph fragments which compartmentalise the algebra and coding required for VMP as well as developing some new fragments. We then assess the performance of VMP and EP with a Markov chain Monte Carlo (MCMC) benchmark in the context of inferential accuracy and computation speed. A series of numerical studies carried out throughout this thesis suggest that VMP and EP could be good alternatives to MCMC depending on models under consideration and the type and size of dataset.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/133327/2/02whole.pdf
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.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Semiparametric regression via message passing algorithms	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

Message passing algorithms are a group of fast, deterministic inference algorithms originated from the field of computer science. The focus of this thesis is on transferral of message passing algorithms into the major statistics field of semiparametric regression. We work on unveiling variational message passing (VMP) and expectation propagation (EP) from statistical perspective and developing explicit computable algorithms via VMP and EP for approximate statistical inference on model parameters in various regression models. We also contribute on demonstrating the notion of existing factor graph fragments which compartmentalise the algebra and coding required for VMP as well as developing some new fragments. We then assess the performance of VMP and EP with a Markov chain Monte Carlo (MCMC) benchmark in the context of inferential accuracy and computation speed. A series of numerical studies carried out throughout this thesis suggest that VMP and EP could be good alternatives to MCMC depending on models under consideration and the type and size of dataset.

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http://hdl.handle.net/10453/133327