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Bayesian Density Estimation and Inference Using Mixtures
Michael D. Escobar and Mike West
Journal of the American Statistical Association
Vol. 90, No. 430 (Jun., 1995), pp. 577-588
Stable URL: http://www.jstor.org/stable/2291069
Page Count: 12
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We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation and are exemplified by special cases where data are modeled as a sample from mixtures of normal distributions. Efficient simulation methods are used to approximate various prior, posterior, and predictive distributions. This allows for direct inference on a variety of practical issues, including problems of local versus global smoothing, uncertainty about density estimates, assessment of modality, and the inference on the numbers of components. Also, convergence results are established for a general class of normal mixture models.
Journal of the American Statistical Association © 1995 American Statistical Association