A new algorithm for importance analysis of the inputs with distribution parameter uncertainty

作者：Li, Luyi^*; Lu, Zhenzhou

来源：International Journal of Systems Science, 2016, 47(13): 3065-3077.

DOI：10.1080/00207721.2015.1088099

• importance analysis
• input variable
• parameter uncertainty
• surrogate sampling function
• single-loop Monte Carlo method

Importance analysis is aimed at finding the contributions by the inputs to the uncertainty in a model output. For structural systems involving inputs with distribution parameter uncertainty, the contributions by the inputs to the output uncertainty are governed by both the variability and parameter uncertainty in their probability distributions. A natural and consistent way to arrive at importance analysis results in such cases would be a three-loop nested Monte Carlo (MC) sampling strategy, in which the parameters are sampled in the outer loop and the inputs are sampled in the inner nested double-loop. However, the computational effort of this procedure is often prohibitive for engineering problem. This paper, therefore, proposes a newly efficient algorithm for importance analysis of the inputs in the presence of parameter uncertainty. By introducing a 'surrogate sampling probability density SS-PDF)' and incorporating the single-loop MC theory into the computation, the proposed algorithm can reduce the original three-loop nested MC computation into a single-loop one in terms of model evaluation, which requires substantially less computational effort. Methods for choosing proper SS-PDF are also discussed in the paper. The efficiency and robustness of the proposed algorithm have been demonstrated by results of several examples.

• 出版日期2016
• 单位西北工业大学