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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. (Update)
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BibTeX entry: (Update)
Rasmussen, C. E. (2000) The Infinite Gaussian Mixture Model, in S. A. Solla, T. K. Leen and K.-R. Muller (editors.), Adv. Neur. Inf. Proc. Sys. 12, MIT Press, pp. 554--560. http://citeseer.ist.psu.edu/rasmussen00infinite.html More
@misc{ rasmussen00infinite,
author = "C. Rasmussen",
title = "The Infinite Gaussian Mixture Model",
text = "Rasmussen, C. E. (2000) The Infinite Gaussian Mixture Model, in S. A. Solla,
T. K. Leen and K.-R. Muller (editors.), Adv. Neur. Inf. Proc. Sys. 12, MIT
Press, pp. 554--560.",
year = "2000",
url = "citeseer.ist.psu.edu/rasmussen00infinite.html" }
Citations (may not include all citations):269 Bayesian Learning for Neural Networks (context) - Neal - 1996
169 Mixtures of Dirichlet processes with applications to Bayesia.. (context) - Antoniak - 1974
168 A Bayesian analysis of some nonparametric problems (context) - Ferguson - 1973
150 On Bayesian analysis of mixtures with an unknown number of c.. - Richardson, Green - 1997
91 Adaptive rejection sampling for Gibbs sampling (context) - Gilks, Wild - 1992
78 Gaussian Processes for Regression - Williams - 1996
60 Markov chain sampling methods for Dirichlet process mixture .. (context) - Neal - 1998
41 SMEM Algorithm for Mixture Models - Ueda, Nakano et al. - 1998
16 Hierarchical priors and mixture models with applications in .. - West, Muller et al. - 1994
The graph only includes citing articles where the year of publication is known.
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