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Non-Gaussian Mixture Model Averaging for Clustering

The Gaussian mixture model has been used for model-based clustering analysis for
decades. Most model-based clustering analyses are based on the Gaussian mixture
model. Model averaging approaches for Gaussian mixture models are proposed by
Wei and McNicholas, based on a family of 14 Gaussian parsimonious clustering
models. In this thesis, we use non-Gaussian mixture
models, namely the tEigen family, for our averaging approaches. This paper studies
fitting in an averaged model from a set of multivariate t-mixture models instead of
fitting a best model. / Thesis / Master of Science (MSc)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20792
Date January 2017
CreatorsZhang, Xu Xuan
ContributorsMcNicholas, Paul D., Mathematics and Statistics
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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