%0 Journal Article
%T Spike-and-Slab Dirichlet Process Mixture Models
%A Kai Cui
%A Wenshan Cui
%J Open Journal of Statistics
%P 512-518
%@ 2161-7198
%D 2012
%I Scientific Research Publishing
%R 10.4236/ojs.2012.25066
%X 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.
%K Spike and Slab
%K Dirichlet Process
%K Bayesian Expectation-Maximization (BEM)
%K Mixture
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=25550