Optimal Uncertainty Quantification on moment class using canonical moments
arXiv:1811.12788
Abstract
We gain robustness on the quantification of a risk measurement by accounting for all sources of uncertainties tainting the inputs of a computer code. We evaluate the maximum quantile over a class of distributions defined only by constraints on their moments. The methodology is based on the theory of canonical moments that appears to be a well-suited framework for practical optimization.
21 pages, 9 figures