Nonparametric Bayesian inference for multivariate density functions using Feller priors

Xiang Zhang and Yanbing Zheng

Journal of Nonparametric Statistics, 2014, vol. 26, issue 2, 321-340

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.

Date: 2014
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DOI: 10.1080/10485252.2014.894512

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