Dependent mixtures of Dirichlet processes
Computational Statistics & Data Analysis
Bayesian nonparametric mixed random utility models
Computational Statistics & Data Analysis
Nonparametric Bayes classification and hypothesis testing on manifolds
Journal of Multivariate Analysis
Posterior consistency in conditional distribution estimation
Journal of Multivariate Analysis
Univariate Bayesian nonparametric mixture modeling with unimodal kernels
Statistics and Computing
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We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun. Stat., Simul. Comput. 36:45---54, 2007). This new sampler allows for the fitting of infinite mixture models with a wide-range of prior specifications. To illustrate this flexibility we consider priors defined through infinite sequences of independent positive random variables. Two applications are considered: density estimation using mixture models and hazard function estimation. In each case we show how the slice efficient sampler can be applied to make inference in the models. In the mixture case, two submodels are studied in detail. The first one assumes that the positive random variables are Gamma distributed and the second assumes that they are inverse-Gaussian distributed. Both priors have two hyperparameters and we consider their effect on the prior distribution of the number of occupied clusters in a sample. Extensive computational comparisons with alternative "conditional" simulation techniques for mixture models using the standard Dirichlet process prior and our new priors are made. The properties of the new priors are illustrated on a density estimation problem.