Stein's Method Meets Computational Statistics: A Review of Some Recent Developments
Abstract
Stein's method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein's method has led to significant advances in computational statistics in recent years. The goal of this survey is to bring together some of these recent developments and, in doing so, to stimulate further research into the successful field of Stein's method and statistics. The topics we discuss include tools to benchmark and compare sampling methods such as approximate Markov chain Monte Carlo, deterministic alternatives to sampling methods, control variate techniques, parameter estimation and goodness-of-fit testing.
Cite
@article{arxiv.2105.03481,
title = {Stein's Method Meets Computational Statistics: A Review of Some Recent Developments},
author = {Andreas Anastasiou and Alessandro Barp and François-Xavier Briol and Bruno Ebner and Robert E. Gaunt and Fatemeh Ghaderinezhad and Jackson Gorham and Arthur Gretton and Christophe Ley and Qiang Liu and Lester Mackey and Chris. J. Oates and Gesine Reinert and Yvik Swan},
journal= {arXiv preprint arXiv:2105.03481},
year = {2022}
}
Comments
Accepted for publication by "Statistical Science"