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Permanent link (DOI): https://doi.org/10.7939/R3197X

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Change Point Detection Using Expectation Maximization Approach Open Access

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Other title
Subject/Keyword
Bayesian
Expectation Maximization
Change point
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Keshavarz, Marziyeh
Supervisor and department
Biao Huang (Chemical Engineering)
Examining committee member and department
Li, Zukui(Chemical Engineering)
Shah, Sirish (Chemical Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Process Control
Date accepted
2013-08-29T13:36:48Z
Graduation date
2013-11
Degree
Master of Science
Degree level
Master's
Abstract
Data analysis plays an important role in system modeling, monitoring and optimization. Among those data analysis techniques, change point detection has been widely applied in various areas including chemical process, climate monitoring, examination of gene expres- sions and quality control in the manufacturing industry, etc. In this thesis, an Expectation Maximization (EM) algorithm is proposed to detect the time instants at which data prop- erties are subject to change. This method performs e_ciently especially in missing data problem or when directly maximizing the likelihood is di_cult. The change point detection problem is solved under various scenarios including univariate and multivariate data, known and unknown covariance. The problem is also extended to changing covariance in the case of multivariate data analysis. Moreover, using Bayesian inference method these problems are solved and the results are compared with EM. The results show that in terms of com- putation, due to some iterations involved in EM algorithm, it has higher computation but the convergence is fast.
Language
English
DOI
doi:10.7939/R3197X
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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