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