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Permanent link (DOI): https://doi.org/10.7939/R3NT1D

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A Discrete-time Particle Filter and Central Limit Theorem Open Access

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Other title
Subject/Keyword
Central Limit Theorem
Particle Filter
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Ye, Zi
Supervisor and department
Kouritzin, Mike (Mathematical and Statistical Sciences)
Examining committee member and department
Choulli, Tahir (Mathematical and Statistical Sciences)
Wong, Yau Shu (Mathematical and Statistical Sciences)
Berger, Arno (Mathematical and Statistical Sciences)
Department
Department of Mathematical and Statistical Sciences
Specialization
Applied Mathematics
Date accepted
2013-10-23T14:20:05Z
Graduation date
2014-06
Degree
Master of Science
Degree level
Master's
Abstract
We introduce two kinds of particle filters, one is weighted particle filter and the other is resampling particle filter. We prove the Strong Law of Large Numbers and Central Limit Theorem for both particle filters. Then, we show that the resampling particle filter is better than the weighted one.
Language
English
DOI
doi:10.7939/R3NT1D
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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