Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas
arXiv (Cornell University)(2023)
摘要
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.
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关键词
simultaneous importance sampling,variates techniques
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