  A method for tuning parameters in Monte Carlo generators is described and
applied to a specific case. The method works in the following way: each
observable is generated several times using different values of the parameters
to be tuned. The output is then approximated by some analytic form to describe
the dependence of the observables on the parameters. This approximation is used
to find the values of the parameter that give the best description of the
experimental data. This results in significantly faster fitting compared to an
approach in which the generator is called iteratively. As an application, we
employ this method to fit the parameters of the unintegrated gluon density used
in the CASCADE Monte Carlo generator, using inclusive deep inelastic data
measured by the H1 Collaboration. We discuss the results of the fit, its
limitations, and its strong points.
