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MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models

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Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Görür,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Rasmussen, C., & Görür, D. (2006). MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models. Talk presented at ICML Workshop on Learning with Nonparametric Bayesian Methods 2006. Pittsburgh, PA, USA.


Cite as: http://hdl.handle.net/21.11116/0000-0004-CB68-3
Abstract
We compare the predictive accuracy of the Dirichlet Process Gaussian mixture models using conjugate and conditionally conjugate priors and show that better density models result from using the wider class of priors. We explore several MCMC schemes exploiting conditional conjugacy and show their computational merits on several multidimensional density estimation problems.