The moment-independent global sensitivity (MIGS) analysis can help decision makers to efficiently reduce the uncertainty of model output. This article proposes a novel single-loop simulation (NSLS) method for MIGS analysis. Through an interval discretization strategy, estimation of probability density functions (PDFs) in existing methods is transformed to estimation of the marginal probability of the input, the marginal probability of the output and the joint probability of the inputs–output in NSLS, which avoids the computational complexity and error resulting from PDF estimation in MIGS analysis. To drastically reduce the computational cost of NSLS to estimate the MIGS index, an NSLS-based adaptive kriging (NSLS-AK) method is subsequently developed. In NSLS-AK, a rough global kriging model is first constructed, then this kriging model is adaptively updated until the convergent condition is satisfied for accurately and efficiently predicting the responses of the samples in estimating the MIGS index.

矩无关的全局敏感性（MIGS）分析可以帮助决策者有效地降低模型输出的不确定性。本文提出了一种用于 MIGS 分析的新型单回路模拟 (NSLS) 方法。通过区间离散化策略，将现有方法中概率密度函数（PDF）的估计转化为NSLS中输入边际概率、输出边际概率和输入-输出联合概率的估计，避免了MIGS 分析中 PDF 估计导致的计算复杂度和误差。为了显着降低 NSLS 估计 MIGS 指数的计算成本，随后开发了一种基于 NSLS 的自适应克里金法 (NSLS-AK)。在 NSLS-AK 中，首先构建了一个粗略的全局克里金模型，