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Theses and Dissertations

This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.

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Results for "Probability Distributions on a Circle"

Departments: Department of Mathematical and Statistical Sciences Supervisors: Hillen, Thomas (Department of Mathematical and Statistical Sciences)

  • Tumor Control Probability Models

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    Fall 2011

    Gong, Jiafen

    model from a birth-death process. The calculation of this NTCP model provides an alternative proof to a formula derived by Hanin (Hanin, 2004) to compute the probability distribution of the tumor size from its generating function. My formula is computationally more efficient, compared to Hanin’s

    , used for quantifying normal tissue complication. In this thesis, I begin with a simple Poisson TCP based on mean cell population dynamics. Optimal treatment schedules are obtained by maximizing this TCP while constraining the CRE under a given threshold. Some of the optimal results suggest the usage

    Cancer is one of the major causes of death in the world. In the field of Oncology, clinical trials form the crux of medical effort to find better treatment schedules. These trials are expensive, time consuming, and carry great risks for the patients involved. Mathematical models provide a

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