Browse by author
Lookup NU author(s): Professor Chris Oates
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 International Society for Bayesian Analysis. Zero-variance control variates (ZV-CV) are a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort lies in solving a linear regression problem. Significant variance reductions have been achieved with this method in low dimensional examples, but the number of covariates in the regression rapidly increases with the dimension of the target. In this paper, we present compelling empirical evidence that the use of penalized regression techniques in the selection of high-dimensional control variates provides performance gains over the classical least squares method. Another type of regularization based on using subsets of derivatives, or a priori regularization as we refer to it in this paper, is also proposed to reduce computational and storage requirements. Several examples showing the utility and limitations of regularized ZV-CV for Bayesian inference are given. The methods proposed in this paper are accessible through the R package ZVCV.
Author(s): South LF, Oates CJ, Mira A, Drovandi C
Publication type: Article
Publication status: Published
Journal: Bayesian Analysis
Year: 2023
Volume: 18
Issue: 3
Pages: 865-888
Online publication date: 05/09/2023
Acceptance date: 02/04/2018
Date deposited: 18/09/2023
ISSN (print): 1936-0975
ISSN (electronic): 1931-6690
Publisher: International Society for Bayesian Analysis
URL: https://doi.org/10.1214/22-BA1328
DOI: 10.1214/22-BA1328
Data Access Statement: Supplementary Material: Supplementary Material for Regularized Zero-Variance Control Variates (DOI: 10.1214/22-BA1328SUPP; .pdf). This document provides three additional applications, further simulation results for the examples in the paper and a more detailed description of how to implement ZV-CV methods in SMC.
Altmetrics provided by Altmetric
