  We describe a procedure for constructing a model of a smooth data spectrum
using Gaussian processes rather than the historical parametric description.
This approach considers a fuller space of possible functions, is robust at
increasing luminosity, and allows us to incorporate our understanding of the
underlying physics. We demonstrate the application of this approach to modeling
the background to searches for dijet resonances at the Large Hadron Collider
and describe how the approach can be used in the search for generic localized
signals.
