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Bayesian Networks and Gaussian Mixture Models in Multi-Dimensional Data Analysis with Application to Religion-Conflict Data

Abstract This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We use Bayesian networks to... (more)
Created Date 2012
Contributor Liu, Hui (Author) / Taylor, Thomas (Advisor) / Cochran, Douglas (Advisor) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Statistics / AIC / Bayesian Network / BIC / Expectation-maximization / Gaussian Mixture Model
Type Masters Thesis
Extent 54 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Electrical Engineering 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis