Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas

Autor:	Shyamsundar, Prasanth, Scott, Jacob L., Mrenna, Stephen, Matchev, Konstantin T., Kong, Kyoungchul
Rok vydání:	2023
Předmět:	High Energy Physics - Phenomenology Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	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. Comment: 20 pages, 2 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.12369 Zobrazit plný text záznamu View this record from Arxiv