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Model-based Learning: t-Families, Variable Selection, and Parameter Estimation

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Title: Model-based Learning: t-Families, Variable Selection, and Parameter Estimation
Author: Andrews, Jeffrey Lambert
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: McNicholas, Paul
Abstract: The phrase model-based learning describes the use of mixture models in machine learning problems. This thesis focuses on a number of issues surrounding the use of mixture models in statistical learning tasks: including clustering, classification, discriminant analysis, variable selection, and parameter estimation. After motivating the importance of statistical learning via mixture models, five papers are presented. For ease of consumption, the papers are organized into three parts: mixtures of multivariate t-families, variable selection, and parameter estimation.
URI: http://hdl.handle.net/10214/3879
Date: 2012-07


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