Skip to Main Content
  1. Graduate and Postdoctoral Studies (GPS), Faculty of
  2. Theses and Dissertations

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.

Items in this Collection

Skip to Search Results
Filter
Close
Author / Creator / Contributor
Departments
Subject / Keyword
Year
to
Collections
Languages
Item type
Supervisors

Results for "Probability Distributions on a Circle"

Author: Fan, Lei Departments: Department of Chemical and Materials Engineering

  • Robust Latent Variable Modeling Using Probabilistic Slow Feature Analysis

    Download

    Fall 2020

    Fan, Lei

    features can be extracted and the impact of outliers is alleviated by the latent variance scale. The next contribution of this thesis is to develop a semi-supervised model based on probability slow feature analysis to include the information from quality variables in the extracted latent features while

    information of the process. With a probabilistic formulation, dynamic latent variable models, based on extracting slowly varying features, are developed in this thesis to address the aforementioned data irregularities, thus give reliable prediction results of quality variables that are otherwise difficult to

    -distribution that has heavier tails, more weights can be assigned to the outliers thus they can be properly accounted for during modeling process. In feature extraction phase, a weighted Kalman gain is proposed since it violates the Gaussian assumption of the traditional Kalman filter. Smoother and slower

1 - 1 of 1