NeurIPS 2017poster11 citations
Convergence rates of a partition based Bayesian multivariate density estimation method
Linxi Liu, Dangna Li, Wing Hung Wong
Abstract
We study a class of non-parametric density estimators under Bayesian settings. The estimators are obtained by adaptively partitioning the sample space. Under a suitable prior, we analyze the concentration rate of the posterior distribution, and demonstrate that the rate does not directly depend on the dimension of the problem in several special cases. Another advantage of this class of Bayesian density estimators is that it can adapt to the unknown smoothness of the true density function, thus achieving the optimal convergence rate without artificial conditions on the density. We also validate the theoretical results on a variety of simulated data sets.
BibTeX
@inproceedings{NIPS2017_f55cadb9,
author = {Liu, Linxi and Li, Dangna and Wong, Wing Hung},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Convergence rates of a partition based Bayesian multivariate density estimation method},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/f55cadb97eaff2ba1980e001b0bd9842-Paper.pdf},
volume = {30},
year = {2017}
}