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

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.

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 Creators:
Rasmussen, CE1, 2, Author              
Görür, D1, 2, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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.

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 Dates: 2006-06
 Publication Status: Published online
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Title: ICML Workshop on Learning with Nonparametric Bayesian Methods 2006
Place of Event: Pittsburgh, PA, USA
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Invited: Yes

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