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

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Date
2012-08-27
Authors
Andrews, Jeffrey Lambert
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Publisher
University of Guelph
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.

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Keywords
Computational Statistics, Cluster Analysis, Multivariate Statistics, Classification, Statistical Learning, Mixture Models
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