  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.
