  A method is presented to exploit adaptive integration algorithms using
importance sampling, like VEGAS, for the task of scanning theoretical
predictions depending on a multi-dimensional parameter space. Usually, a
parameter scan is performed with emphasis on certain features of a theoretical
prediction. Adaptive integration algorithms are well-suited to perform this
task very efficiently. Predictions which depend on parameter spaces with many
dimensions call for such an adaptive scanning algorithm.
