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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.
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