Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, robust, well-written software. This article, based on our MCQMC 2020 tutorial, describes some of the better quasi-Monte Carlo (QMC) software available. We highlight the key software components required by QMC to approximate multivariate integrals or expectations of functions of vector random variables. We have combined these components in QMCPy, a Python open-source library, which we hope will draw the support of the QMC community. Here we introduce QMCPy.
翻译：希望因使用低差异序列而提高效率的从业者需要正确、稳健、完善和完善的软件。这篇文章以我们的 MCQMC 2020 辅导为基础,描述了一些更好的准蒙特卡洛(QMC ) 软件。我们强调QMC 所需的关键软件组件,以接近矢量随机变量的多变量集成或预期功能。我们将这些组件合并到QMCPy(一个Python开放源码库),我们希望它将获得QMC 社区的支持。这里我们引入QMCPy 。
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