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

机译：多维数据分析中的贝叶斯网络和高斯混合模型及其在宗教冲突数据中的应用。

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摘要

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.
机译：本文探讨了统计信号处理方法在旨在测量支撑人类社会动态的心理和社会学现象的调查数据中的应用。在过去的十年中，使用信号处理方法来分析因测量社会，生物和其他非传统现象而产生的信号已成为信号处理研究的重要且不断发展的领域。在这里，我们探索将统计建模和信号处理概念应用到从全球群体关系项目获得的数据中，特别是为了理解和量化与群体间冲突相关的社会心理因素的影响和相互作用。;我们使用贝叶斯网络来指定预期模型有条件的依赖。在社会心理因素和冲突变量之间确定贝叶斯网络，并通过有向无环图对其进行建模，而将重大交互作用建模为条件概率。由于数据稀疏和多维，因此我们针对数据回归高斯混合模型（GMM），以估计感兴趣的条件概率。使用期望最大化（EM）算法估计GMM的参数。但是，由于该数据集的高维数和有限的观测值，EM算法可能会遇到过拟合问题。因此，将Akaike信息准则（AIC）和贝叶斯信息准则（BIC）用于GMM顺序估计。为了帮助直观地理解社会变量和群体间冲突的相互作用，我们引入了一种基于颜色的可视化方案。在此方案中，颜色的强度与观察到的条件概率成正比。

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  • 作者

    Liu, Hui.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Statistics.;Engineering Electronics and Electrical.
  • 学位 M.S.
  • 年度 2012
  • 页码 54 p.
  • 总页数 54
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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