TITLE:
Spike-and-Slab Dirichlet Process Mixture Models
AUTHORS:
Kai Cui, Wenshan Cui
KEYWORDS:
Spike and Slab; Dirichlet Process; Bayesian Expectation-Maximization (BEM); Mixture
JOURNAL NAME:
Open Journal of Statistics,
Vol.2 No.5,
December
19,
2012
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.