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Conference Paper

The Infinite Gaussian Mixture Model

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

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

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Citation

Rasmussen, C. (2000). The Infinite Gaussian Mixture Model. Advances in Neural Information Processing Systems 12, 554-560.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E4CC-C
Abstract
In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the ``right'' number of mixture components. Inference in the model is done using an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling.