  We propose a suitable analytical framework to perform numerical analysis of
problems arising in compressible fluid models with uncertain data. We discuss
both weak and strong stochastic approach, where the former is based on the
knowledge of the mere distribution (law) of the random data typical for the
Monte-Carlo and related methods, while the latter assumes the data to be known
as a random variabble on a given probability space aiming at obtaining the
associated solution in the same form. As an example of the strong approach, we
discuss the stochastic collocation method based on a piecewise constant
approximation of the random data.
