Fearnhead, Paul (2004) Particle filters for mixture models with an unknown number of components. Statistics and Computing, 14 (1). pp. 11-21. ISSN 0960-3174Full text not available from this repository.
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.
|Journal or Publication Title:||Statistics and Computing|
|Uncontrolled Keywords:||Dirichlet process - Gaussian mixture models - Gibbs sampling - MCMC - particle filters|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
|Deposited By:||Prof Paul Fearnhead|
|Deposited On:||15 Apr 2008 09:57|
|Last Modified:||16 Jan 2017 01:53|
Actions (login required)