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Abstract: In a Bayesian mixture model, there is no need a priori to restrict the number of components to be finite. Infinite mixture models sidestep the problem of finding the "correct" number of components, and may be handled using a finite amount of computation. In this paper it is demonstrated how inference may be done in infinite mixture models using a Markov Chain whose implementation relies entirely on Gibbs sampling. An example is given of application to multivariate density estimation. 1... (Update)
Context of citations to this paper: More
.... For Gaussian mixture models Markov chain Monte Carlo (MCMC) methods have been developed to approximate these integrals by sampling [6, 5]. The main criticism of MCMC methods is that they are slow and it is usually difficult to assess convergence. Furthermore, the...
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BibTeX entry: (Update)
C.E. Rasmussen. The countably infinite Bayesian Gaussian mixture density model. Technical report, Dept. of Math. Modelling, Tech. Univ. Denmark, 1999. http://citeseer.ist.psu.edu/rasmussen99countably.html More
@misc{ rasmussen99countably,
author = "C. Rasmussen",
title = "The countably infinite Bayesian Gaussian mixture density model",
text = "C.E. Rasmussen. The countably infinite Bayesian Gaussian mixture density
model. Technical report, Dept. of Math. Modelling, Tech. Univ. Denmark,
1999.",
year = "1999",
url = "citeseer.ist.psu.edu/rasmussen99countably.html" }
Citations (may not include all citations):310 Statistical analysis of finite mixture distributions (context) - Titterington, Smith et al. - 1985
169 Mixtures of Dirichlet processes with applications to Bayesia.. (context) - Antoniak - 1974
168 A Bayesian analysis of some nonparametric problems (context) - Ferguson - 1973
140 Bayesian Density Estimation and Inference Using Mixtures - Escobar, West - 1995
91 Adaptive rejection sampling for Gibbs sampling (context) - Gilks, Wild - 1992
31 Estimating mixture of Dirichlet process models - MacEachern, Muller - 1998
16 Hierarchical priors and mixture models with applications in .. - West, Muller et al. - 1994
5 An Introduction to Multivariate Statistics (context) - Anderson - 1984
1 Wishart Variate Generator (context) - Smith, Hocking - 1972
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