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
Subject / Keyword
Author / Creator / Contributor
Year
to
Collections
Languages
Item type
Departments
Supervisors

Results for "Probability Distributions on a Circle"

Subject: Machine learning

  • Application of Machine Learning in the Big Data for Broiler Breeders Recorded by a Precision Feeding System

    Download

    Spring 2021

    You, Jihao

    study. In the first study, the model could only be used to identify daily oviposition events on the subsequent day and the prediction outputs were binary labels. An ANN model was used to predict and output the probability of daily oviposition events occurring using a specific time point in one day. The

    oviposition events on the current day, and the output was a probability that could be informative to indicate how likely oviposition of an individual breeder occurred in the day. In situations where total egg production was known for a group, the ANN model could predict the probability of daily oviposition

    system and make predictions based on the information. The first study investigated predicting daily oviposition events of individual broiler breeders by a random forest (RF) classification model. The raw dataset from the PF system was processed for 34 features in relation to the feeding activity and body

  • Advances in Probabilistic Generative Models: Normalizing Flows, Multi-View Learning, and Linear Dynamical Systems

    Download

    Fall 2020

    Karami, Mahdi

    applicable method to construct complex probability density. Herein, I investigate a set of invertible convolutional flows based on the circular and symmetric convolutions with efficient Jacobian determinant computation and inverse mapping (deconvolution) in 𝒪(𝑁 log𝑁) time. Further, an analytic approach to

    representation and a set of view-specific factors. To approximate the posterior distribution of the latent probabilistic multi-view layer, a variational inference approach is developed that results in a scalable algorithm for training deep generative multi-view neural networks. Empirical studies confirm that the

    the second part, a deep generative framework is expanded to multi-view learning. This model is composed of a linear probabilistic multi-view layer in the latent space in conjunction with deep generative networks as observation models where the variations of each view is captured by a shared latent

  • Machine learning-based monitoring of complex reactive systems

    Download

    Spring 2022

    Puliyanda, Anjana Thimmaiah

    from the reactant to the product configurations. A self-supervised 3D convolutional neural network autoencoder is trained to extract features from the reactant and product simulation trajectories, the probability distributions across the difference between which is used to assess if the solvent

    Processing of complex feedstocks for the production of value-added chemicals and fuels is industrially important. The lack of a priori knowledge of the innumerable species and the reaction pathways governing their conversion, has posed challenges to monitoring these processes. Although, data-driven

    models have been used, their lack of interpretability and an end-to-end modeling framework has limited the efficiency of diagnostic decisions in process monitoring. On the other hand, systems where the mechanistic knowledge of the species and their reactions are arrived at from first-principles

1 - 3 of 3