The Infinite Gaussian Mixture Model

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Carl Rasmussen


In a Bayesian mixture model it is not necessary a priori to limit the num(cid:173) ber of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of find(cid:173) ing 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.