Adaptive Convergence Rates of a Dirichlet Process Mixture of Multivariate Normals
Statistics Theory
2011-11-18 v1 Methodology
Statistics Theory
Abstract
It is shown that a simple Dirichlet process mixture of multivariate normals offers Bayesian density estimation with adaptive posterior convergence rates. Toward this, a novel sieve for non-parametric mixture densities is explored, and its rate adaptability to various smoothness classes of densities in arbitrary dimension is demonstrated. This sieve construction is expected to offer a substantial technical advancement in studying Bayesian non-parametric mixture models based on stick-breaking priors.
Keywords
Cite
@article{arxiv.1111.4148,
title = {Adaptive Convergence Rates of a Dirichlet Process Mixture of Multivariate Normals},
author = {Surya T. Tokdar},
journal= {arXiv preprint arXiv:1111.4148},
year = {2011}
}
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12 pages