Spike-and-Slab Dirichlet Process Mixture Models

Kai Cui, Wenshan Cui

Department of Statistical Science, Duke University, Durham, USA.
School of Science and Information, Qingdao Agricultural University, Qingdao, China.

DOI: 10.4236/ojs.2012.25066                

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.

Keywords

Spike and Slab; Dirichlet Process; Bayesian Expectation-Maximization (BEM); Mixture

Conflicts of Interest

The authors declare no conflicts of interest.

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