The paper presents a fairly efficient approximation for the computation of variance‐based sensitivity measures associated with a general, n‐dimensional function of random variables. The proposed approach is based on a multiplicative version of the dimensional reduction method (M‐DRM), in which a given complex function is approximated by a product of low dimensional functions. Together with the Gaussian quadrature, the use of M‐DRM significantly reduces the computation effort associated with global sensitivity analysis. An important and practical benefit of the M‐DRM is the algebraic simplicity and closed‐form nature of sensitivity coefficient formulas. Several examples are presented to show that the M‐DRM method is as accurate as results obtained from simulations and other approximations reported in the literature.
