In this paper we propose an extension of the classical Sobol' estimator for the estimation of variance based sensitivity indices. The approach assumes a linear correlation model between the input variables which is used to decompose the contribution of an input variable into a correlated and an uncorrelated part. This method provides sampling matrices following the original joint probability distribution which are used directly to compute the model output without any assumptions or approximations of the model response function.

翻译：本文提出了一种经典Sobol估计器的扩展方法，用于估计基于方差的敏感性指标。该方法假设输入变量之间存在线性相关模型，该模型用于将输入变量的贡献分解为相关部分与不相关部分。此方法生成的抽样矩阵遵循原始联合概率分布，可直接用于计算模型输出，无需对模型响应函数作任何假设或近似。