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.

与时刻无关的全局灵敏度（MIGS）分析可以帮助决策者有效地减少模型输出的不确定性。本文提出了一种新颖的用于MIGS分析的单环仿真（NSLS）方法。通过区间离散化策略，将现有方法中的概率密度函数（PDF）的估计转换为对输入的边际概率，输出的边际概率以及输入与输出的联合概率的估计，从而避免了NSLS。 MIGS分析中PDF估计导致的计算复杂性和错误。为了大大降低NSLS的计算成本来估算MIGS指数，随后开发了基于NSLS的自适应克里格（NSLS-AK）方法。在NSLS-AK中，首先构建了一个粗略的全局克里金模型，