This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
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
Results for "Probability Distributions on a Circle"
-
Fall 2019
thesis forms a transition function for the constrained latent features. As a hierarchical extension of the hidden Markov model, it describes a dynamic model for the probabilities of discrete variables. By using the Beta distribution to replace the Gaussian distribution, the novel transition function
. Besides several probability models, novel inferencing algorithms are elaborated for different application scenarios. In most chemical processes, features with large inertia and small varying velocity are believed to be more informative. By imposing this modelling preferences as prior distributions of
model parameters, the first contribution of this thesis builds the dynamic latent features under a fully Bayesian framework. The preference for large inertia is implemented through a constraint and a prior distribution for the dynamic model of latent features, namely the transition function. The