Efficient variance reduction approach based on the variation of the input importance

To reduce the output variance, the variance-based importance analysis can provide an efficient way by reducing the variance of the important' inputs. But with the reduction of the variance of those important' inputs, the input importance will change and it is no longer the most efficient way to reduce the variance of those important' inputs alone. Thus, analyst needs to consider reducing the variance of other inputs to obtain a more efficient way. This work provides a graphical solution for analyst to decide how to reduce the input variance to achieve the targeted reduction of the output variance efficiently. Furthermore, by the importance sampling-based approach, the graphical solution can be obtained with only a single group of samples, which can decrease the computational cost greatly.