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Web Based Particle Filters

  • Author / Creator
    Wang, Xingpu
  • In this thesis, we first introduce two basic problems of filter, the nonlinear filtering and model selection problem. We show that both of them can be solved by the unnormalized filter approach. Then several web based particle filter algorithms will be discussed. We extend the resampled and branching system on single computer platform to a web based platform. The performance and execution time of these algorithms will be compared upon two simulation models. We define a parameter, called ”Bootstrap Factor”, which is a reasonable way to compare different particle filters. By Bootstrap Factor, we show that the web based branching system performs much better than the double resampled system.

  • Subjects / Keywords
  • Graduation date
    2016-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R30V89R41
  • 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 Mathematical and Statistical Sciences
  • Specialization
    • Statistics
  • Supervisor / co-supervisor and their department(s)
    • Michael Kouritzin (Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Michael Kouritzin (Mathematical and Statistical Sciences)
    • Giseon, Heo (Mathematical and Statistical Sciences)
    • Jochen, Kuttler (Mathematical and Statistical Sciences)
    • Doug, Wiens (Mathematical and Statistical Sciences)