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Bayesian, Expectation Maximization, Change point

Keshavarz, Marziyeh

Supervisor and department: Biao Huang Chemical Engineering

Examining committee member and department: Li, ZukuiChemical 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 invarious areas including chemical process, climate monitoring, examination of gene expres-sions and quality control in the manufacturing industry, etc. In this thesis, an ExpectationMaximization 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 dataproblem or when directly maximizing the likelihood is di cult. The change point detectionproblem is solved under various scenarios including univariate and multivariate data, knownand unknown covariance. The problem is also extended to changing covariance in the caseof multivariate data analysis. Moreover, using Bayesian inference method these problemsare 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 butthe 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.





Autor: Keshavarz, Marziyeh

Fuente: https://era.library.ualberta.ca/


Introducción



University of Alberta Change Point Detection Using Expectation Maximization Approach by Marziyeh Keshavarz A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Process Control Department of Chemical and Materials Engineering ©Marziyeh Keshavarz Fall 2013 Edmonton, Alberta 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 authors prior written permission. To my parents and my husband for their support and unconditional love 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 expressions 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 properties are subject to change.
This method performs efficiently especially in missing data problem or when directly maximizing the likelihood is difficult.
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 Bay...





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