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.
Cite this paper
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.
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