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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.
Items in this Collection
- 1Causality analysis
- 1Information theory
- 1Root cause analysis
- 1Transfer entropy
- 1cyber-physical systems
- 1distributed filtering
Results for "Probability Distributions on a Circle"
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Fall 2018
conditional probability distribution of the state and energy is obtained based on the event-triggered information received at the remote estimator under the energy-dependent measurement transmission policy. The robust state estimation problems are investigated for linear Gaussian systems with event
resources of cyber-physical systems, two event-based state estimation problems are formulated and solved for systems described by hidden Markov models utilizing a new reference measure approach with the change of probability measure. For a linear Gaussian system with an energy harvesting sensor, the joint
-triggered scheduling and systems with unknown exogenous inputs utilizing the risk-sensitive approach, where closed-form risk-sensitive state estimates are derived. A fully distributed robust consensus-based filtering algorithm for systems measured by a sensor network is proposed with stability analysis on
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Spring 2014
industrial process data. A new information theory-based distribution-free measure, transfer 0-entropy, is proposed for causality analysis based on the definitions of 0-entropy and 0-information without assuming a probability space. For the cases of more than two variables, a direct transfer 0-entropy concept
method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. A key assumption for the transfer entropy method is that the sampled data should follow a well-defined probability distribution; yet this assumption may not hold for all types of
detection plays a significant and central role. This thesis focuses mainly on information theory-based approaches for causality analysis that are suitable for both linear and nonlinear process relationships. Previous studies have shown that the transfer entropy approach is a very useful tool in quantifying