  In this work the LHC inverse problem is quantified in the Bayesian context by
clarifying the relation between the mapping from the theory parameter space to
experimental signature space and the inverse map. We demonstrate that, after
complementing the LHC data by existing astrophysical, collider, and low energy
measurements, a simple likelihood analysis is able to significantly reduce the
inverse problem. The presented approach offers a robust, economic, and
extendable way to extract theoretical parameters from the LHC, and other
experimental, data.
