Variance reduction via simultaneous importance sampling and control variates techniques using vegas

Author:

Shyamsundar Prasanth1,Scott Jacob L.2,Mrenna Stephen1,Matchev Konstantin T.3,Kong Kyoungchul2

Affiliation:

1. Fermi National Accelerator Laboratory

2. University of Kansas

3. University of Florida

Abstract

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

Funder

Fermilab

National Science Foundation

United States Department of Energy

Publisher

Stichting SciPost

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1. Codebase release r1.4 for CoVVVR;SciPost Physics Codebases;2024-03-04

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