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In this article we study the problem of quantifying the uncertainty in anexperiment with a technical system. We propose new density estimates whichcombine observed data of the technical system and simulated data from an(imperfect) simulation model based on estimated input distributions. We analyzethe rate of convergence of these estimates. The finite sample size performanceof the estimates is illustrated by applying them to simulated data. Thepractical usefulness of the newly proposed estimates is demonstrated by usingthem to predict the uncertainty of a lateral vibration attenuation system withpiezo-elastic supports.

On the density estimation problem for uncertainty propagation with unknown input distributions