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Clustering Microarray Data Via a Bayesian Infinite Mixture Model

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dc.contributor.advisor McNicholas, Paul
dc.contributor.author Givari, Dena
dc.date.accessioned 2013-01-04T20:40:10Z
dc.date.available 2013-01-04T20:40:10Z
dc.date.copyright 2012-12
dc.date.created 2012-11-29
dc.date.issued 2013-01-04
dc.identifier.uri http://hdl.handle.net/10214/5210
dc.description.abstract Clustering microarray data is a helpful way of identifying genes which are biologically related. Unfortunately, when attempting to cluster microarray data, certain issues must be considered including: the uncertainty in the number of true clusters; the expression of a given gene is often a ected by the expression of other genes; and microarray data is usually high dimensional. This thesis outlines a Bayesian in nite Gaussian mixture model which addresses the issues outlined above by: not requiring the researcher to specify the number of clusters expected, applying a non-diagonal covariance structure, and using mixtures of factor analyzers and extensions thereof to structure the covariance matrix such that it is based on a few latent variables. This approach will be illustrated on real and simulated data. en_US
dc.language.iso en en_US
dc.subject Bayesian en_US
dc.subject Clustering en_US
dc.subject Microarray en_US
dc.subject Hierarchical en_US
dc.title Clustering Microarray Data Via a Bayesian Infinite Mixture Model en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US
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