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Clustering Multiple Related Datasets with a Hierarchical Dirichlet Process

dc.contributor.advisor West, Mike
dc.contributor.author de Oliveira Sales, Ana Paula
dc.date.accessioned 2012-05-29T16:36:52Z
dc.date.available 2012-11-25T05:30:16Z
dc.date.issued 2011
dc.identifier.uri https://hdl.handle.net/10161/5617
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>
dc.subject Statistics
dc.subject Bayesian statistics
dc.subject Clustering
dc.subject Dirichlet process
dc.subject Hierarchical Dirichlet process
dc.subject Nonparametric Bayesian models
dc.title Clustering Multiple Related Datasets with a Hierarchical Dirichlet Process
dc.type Master's thesis
dc.department Statistical Science
duke.embargo.months 6


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