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Spike-and-Slab Dirichlet Process Mixture Models
Department of Statistical Science, Duke University, Durham, USA
School of Science and Information, Qingdao Agricultural University, Qingdao, China
- 1 Department of Statistical Science, Duke University, Durham, USA
- 2 School of Science and Information, Qingdao Agricultural University, Qingdao, China
Open Journal of Statistics·Volume 02 (2012)·Pages 512–518·Published 13 December 2012·DOI10.4236/ojs.2012.25066
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Abstract
In this paper, Spike-and-Slab Dirichlet Process (SS-DP) priors are introduced and discussed for non-parametric Bayesian modeling and inference, especially in the mixture models context. Specifying a spike-and-slab base measure for DP priors combines the merits of Dirichlet process and spike-and-slab priors and serves as a flexible approach in Bayesian model selection and averaging. Computationally, Bayesian Expectation-Maximization (BEM) is utilized to obtain MAP estimates. Two simulated examples in mixture modeling and time series analysis contexts demonstrate the models and computational methodology.
KeywordsSpike and SlabDirichlet ProcessBayesian Expectation-Maximization (BEM)Mixture
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