Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models
Papaspiliopoulos, Omiros and Roberts, Gareth O.. (2008) Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models. Biometrika, Vol.95 (No.1). pp. 169-186. ISSN 0006-3444Full text not available from this repository.
Official URL: http://dx.doi.org/10.1093/biomet/asm086
Inference for Dirichlet process hierarchical models is typically performed using Markov chain Monte Carlo methods, which can be roughly categorized into marginal and conditional methods. The former integrate out analytically the infinite-dimensional component of the hierarchical model and sample from the marginal distribution of the remaining variables using the Gibbs sampler. Conditional methods impute the Dirichlet process and update it as a component of the Gibbs sampler. Since this requires imputation of an infinite-dimensional process, implementation of the conditional method has relied on finite approximations. In this paper, we show how to avoid such approximations by designing two novel Markov chain Monte Carlo algorithms which sample from the exact posterior distribution of quantities of interest. The approximations are avoided by the new technique of retrospective sampling. We also show how the algorithms can obtain samples from functionals of the Dirichlet process. The marginal and the conditional methods are compared and a careful simulation study is included, which involves a non-conjugate model, different datasets and prior specifications.
|Item Type:||Journal Article|
|Subjects:||Q Science > QH Natural history > QH301 Biology
Q Science > QA Mathematics
|Divisions:||Faculty of Science > Statistics|
|Journal or Publication Title:||Biometrika|
|Official Date:||March 2008|
|Number of Pages:||18|
|Page Range:||pp. 169-186|
|Access rights to Published version:||Restricted or Subscription Access|
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