Weighted Bayesian Bootstrap for Scalable Bayes
Methodology
2021-04-06 v1
Abstract
We develop a weighted Bayesian Bootstrap (WBB) for machine learning and statistics. WBB provides uncertainty quantification by sampling from a high dimensional posterior distribution. WBB is computationally fast and scalable using only off-theshelf optimization software such as TensorFlow. We provide regularity conditions which apply to a wide range of machine learning and statistical models. We illustrate our methodology in regularized regression, trend filtering and deep learning. Finally, we conclude with directions for future research.
Cite
@article{arxiv.1803.04559,
title = {Weighted Bayesian Bootstrap for Scalable Bayes},
author = {Michael Newton and Nicholas G. Polson and Jianeng Xu},
journal= {arXiv preprint arXiv:1803.04559},
year = {2021}
}