Monte Carlo simulation (MCS) is an approach based on the propagation of the full probability distributions. It was introduced by the Joint Committee for Guides in Metrology (JCGM) in the supplement I-JCGM 101:2008. It is used to resolve the problem of calculating measurement uncertainties of complex measurands through simulation of random variables. Further, supplement II on “Extension to any number of output quantities” was published in 2011 and supplement III on “Modelling” is under publication. These supplements cover broader range of measurement issues which are not handled using law of propagation of uncertainty (LPU) alone and provide an alternative method to the conventional LPU approach. The MCS method plays a vital role in cases, where the linearization of the model does not provide enough depiction, or the probability density function of the output quantity deviates considerably from the Gaussian distribution. The SWOT analysis describes the strengths, weaknesses, opportunities and threats associated with the use of MCS in the measurement uncertainty evaluation and is addressed in this paper. The paper also summarizes the implications and prospects associated with the use of MCS in uncertainty evaluation for wide usage in solving problems in physical, biological and engineering sciences.

中文翻译：

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不确定性评估中的蒙特卡洛模拟：战略，意义和未来前景

蒙特卡罗模拟（MCS）是一种基于完整概率分布的传播的方法。它由计量指南联合委员会（JCGM）在补充I-JCGM 101：2008中引入。它通过模拟随机变量来解决计算复杂被测量物测量不确定度的问题。此外，2011年出版了关于“扩展到任何数量的产出数量”的补编II，而关于“建模”的补编III也在出版中。这些补充内容涵盖了更广泛的测量问题，这些问题不能仅使用不确定性传播定律（LPU）来处理，并且提供了传统LPU方法的替代方法。在模型的线性化无法提供足够描述的情况下，MCS方法起着至关重要的作用，或输出量的概率密度函数明显偏离高斯分布。SWOT分析描述了在测量不确定性评估中使用MCS的优势，劣势，机会和威胁，本文将对此进行分析。本文还总结了在不确定性评估中广泛使用MCS解决物理，生物和工程科学问题时的意义和前景。