Mixture Modeling, Sparse Covariance Estimation and Parallel Computing in Bayesian Analysis
<p>Mixture modeling of continuous data is an extremely effective and popular method for density estimation and clustering. However as the size of the data grows, both in terms of dimension and number of observations, many modeling and computational problems arise. In the Bayesian setting, comp...
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2012
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| Online Access: | http://hdl.handle.net/10161/6151 |