Particle filters for mixture models with an unknown number of components.

Fearnhead, Paul (2004) Particle filters for mixture models with an unknown number of components. Statistics and Computing, 14 (1). pp. 11-21. ISSN 0960-3174

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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 filters are particularly well suited to such discrete problems, and we propose the use of the particle filter of Fearnhead and Clifford for this problem. The performance of this particle filter, when analyzing both simulated and real data from a Gaussian mixture model, is uniformly better than the particle filter algorithm of Chen and Liu. In many situations it outperforms a Gibbs Sampler. We also show how models without the required amount of conjugacy can be efficiently analyzed by the same particle filter algorithm.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
?? dirichlet process - gaussian mixture models - gibbs sampling - mcmc - particle filterscomputational theory and mathematicstheoretical computer sciencestatistics and probabilitystatistics, probability and uncertaintyqa mathematics ??
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Deposited On:
15 Apr 2008 08:57
Last Modified:
15 Jul 2024 11:30