Rate exact Bayesian adaptation with modified block priors
Statistics Theory
2016-01-22 v3 Statistics Theory
Abstract
A novel block prior is proposed for adaptive Bayesian estimation. The prior does not depend on the smoothness of the function or the sample size. It puts sufficient prior mass near the true signal and automatically concentrates on its effective dimension. A rate-optimal posterior contraction is obtained in a general framework, which includes density estimation, white noise model, Gaussian sequence model, Gaussian regression and spectral density estimation.
Cite
@article{arxiv.1312.3937,
title = {Rate exact Bayesian adaptation with modified block priors},
author = {Chao Gao and Harrison H. Zhou},
journal= {arXiv preprint arXiv:1312.3937},
year = {2016}
}
Comments
Published at http://dx.doi.org/10.1214/15-AOS1368 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)