Full metadata
Title
Bayesian networks and gaussian mixture models in multi-dimensional data analysis with application to religion-conflict data
Description
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 specify prospective models of conditional dependence. Bayesian networks are determined between social psychological factors and conflict variables, and modeled by directed acyclic graphs, while the significant interactions are modeled as conditional probabilities. Since the data are sparse and multi-dimensional, we regress Gaussian mixture models (GMMs) against the data to estimate the conditional probabilities of interest. The parameters of GMMs are estimated using the expectation-maximization (EM) algorithm. However, the EM algorithm may suffer from over-fitting problem due to the high dimensionality and limited observations entailed in this data set. Therefore, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are used for GMM order estimation. To assist intuitive understanding of the interactions of social variables and the intergroup conflicts, we introduce a color-based visualization scheme. In this scheme, the intensities of colors are proportional to the conditional probabilities observed.
Date Created
2012
Contributors
- Liu, Hui (Author)
- Taylor, Thomas (Thesis advisor)
- Cochran, Douglas (Thesis advisor)
- Zhang, Junshan (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
vi, 47 p. : col. ill
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.14952
Statement of Responsibility
Hui Liu
Description Source
Viewed on March 22, 2013
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2012
Note type
thesis
Includes bibliographical references (p. 43-47)
Note type
bibliography
Field of study: Electrical engineering
System Created
- 2012-08-24 06:27:07
System Modified
- 2021-08-30 01:46:25
- 2 years 8 months ago
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