Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866916471386406912 |
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| author | Shyamsundar, Prasanth Scott, Jacob L. Mrenna, Stephen Matchev, Konstantin T. Kong, Kyoungchul |
| author_facet | Shyamsundar, Prasanth Scott, Jacob L. Mrenna, Stephen Matchev, Konstantin T. Kong, Kyoungchul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_12369 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas Shyamsundar, Prasanth Scott, Jacob L. Mrenna, Stephen Matchev, Konstantin T. Kong, Kyoungchul High Energy Physics - Phenomenology Data Analysis, Statistics and Probability 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. |
| title | Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas |
| topic | High Energy Physics - Phenomenology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2309.12369 |