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Change Point Detection Using Expectation Maximization Approach

  • Author / Creator
    Keshavarz, Marziyeh
  • 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.

  • Subjects / Keywords
  • Graduation date
    2013-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3197X
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Chemical and Materials Engineering
  • Specialization
    • Process Control
  • Supervisor / co-supervisor and their department(s)
    • Biao Huang (Chemical Engineering)
  • Examining committee members and their departments
    • Shah, Sirish (Chemical Engineering)
    • Li, Zukui(Chemical Engineering)