  Data analyses in hadron collider physics depend on background simulations
performed by Monte Carlo (MC) event generators. However, calculational
limitations and non-perturbative effects require approximate models with
adjustable parameters. In fact, we need to simultaneously tune many
phenomenological parameters in a high-dimensional parameter-space in order to
make the MC generator predictions fit the data. It is desirable to achieve this
goal without spending too much time or computing resources iterating parameter
settings and comparing the same set of plots over and over again. We present
extensions and improvements to the MC tuning system, Professor, which addresses
the aforementioned problems by constructing a fast analytic model of a MC
generator which can then be easily fitted to data. Using this procedure it is
for the first time possible to get a robust estimate of the uncertainty of
generator tunings. Furthermore, we can use these uncertainty estimates to study
the effect of new (pseudo-) data on the quality of tunings and therefore decide
if a measurement is worthwhile in the prospect of generator tuning. The
potential of the Professor method outside the MC tuning area is presented as
well.
