Nonparametric Bayesian inference for multivariate density functions using Feller priors |
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Authors: | Xiang Zhang |
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Affiliation: | Department of Statistics, University of Kentucky, 315 Multi-disciplinary Science Building, 725 Rose Street, Lexington, KY 40536, USA |
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Abstract: | Multivariate density estimation plays an important role in investigating the mechanism of high-dimensional data. This article describes a nonparametric Bayesian approach to the estimation of multivariate densities. A general procedure is proposed for constructing Feller priors for multivariate densities and their theoretical properties as nonparametric priors are established. A blocked Gibbs sampling algorithm is devised to sample from the posterior of the multivariate density. A simulation study is conducted to evaluate the performance of the procedure. |
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Keywords: | blocked Gibbs sampling Dirichlet process multivariate density multivariate Feller prior Pitman–Yor process |
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