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Abstract: We consider the analysis of data under mixture models where the number of components in the mixture is unknown. We concentrate on mixture Dirichlet process models, and in particular we consider such models under conjugate priors. This conjugacy enables us to integrate out many of the parameters in the model, and to discretize the posterior distribution. Particle lters are particularly well suited to such discrete problems, and we propose the use of the particle lter of Fearnhead and Cli ord for ... (Update)
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BibTeX entry: (Update)
@article{ fearnhead04,
author = {P. Fearnhead},
title = {Particle filters for mixture models with an unknown number of components},
journal = {Journal of Statistics and Computing},
year = {2004},
volume = {14},
pages = {11-21},
url = {citeseer.ist.psu.edu/754210.html} }
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