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S. Kim and D. B. Dahl and M. Vannucci, “Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models,” Bayesian Analysis, Vol. 4, No. 4, 2009, pp. 707-732.

has been cited by the following article:

• 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.