Density Estimation via Bayesian Inference Engines
Machine Learning
2021-09-28 v4 Machine Learning
Abstract
We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed density estimates have excellent comparative performance and scale well to very large sample sizes due to a binning strategy. Moreover, the approach is fully Bayesian and all estimates are accompanied by pointwise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates.
Cite
@article{arxiv.2009.06182,
title = {Density Estimation via Bayesian Inference Engines},
author = {M. P. Wand and J. C. F. Yu},
journal= {arXiv preprint arXiv:2009.06182},
year = {2021}
}