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dc.contributor.advisor West, MIke en_US
dc.contributor.author de Oliveira Sales, Ana Paula en_US
dc.date.accessioned 2012-05-29T16:36:52Z
dc.date.available 2012-11-25T05:30:16Z
dc.date.issued 2011 en_US
dc.identifier.uri http://hdl.handle.net/10161/5617
dc.description Thesis en_US
dc.description.abstract <p>I consider the problem of clustering multiple related groups of data. My approach entails mixture models in the context of hierarchical Dirichlet processes, focusing on their ability to perform inference on the unknown number of components in the mixture, as well as to facilitate the sharing of information and borrowing of strength across the various data groups. Here, I build upon the hierarchical Dirichlet process model proposed by Muller <italics>et al.</italics> (2004), revising some relevant aspects of the model, as well as improving the MCMC sampler's convergence by combining local Gibbs sampler moves with global Metropolis-Hastings split-merge moves. I demonstrate the strengths of my model by employing it to cluster both synthetic and real datasets.</p> en_US
dc.subject Statistics en_US
dc.subject Bayesian statistics en_US
dc.subject Clustering en_US
dc.subject Dirichlet process en_US
dc.subject Hierarchical Dirichlet process en_US
dc.subject Nonparametric Bayesian models en_US
dc.title Clustering Multiple Related Datasets with a Hierarchical Dirichlet Process en_US
dc.type Thesis en_US
dc.department Statistical Science en_US
duke.embargo.months 6 en_US

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