Author(s)

Kai Cui, Wenshan Cui

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

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.

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

K. Cui and W. Cui, "Spike-and-Slab Dirichlet Process Mixture Models," Open Journal of Statistics, Vol. 2 No. 5, 2012, pp. 512-518. doi: 10.4236/ojs.2012.25066.