  We discuss advanced statistical methods to improve parameter estimation of
nuclear models. In particular, using the Liquid Drop Model for nuclear binding
energies, we show that the area around the global $\chi^2$ minimum can be
efficiently identified using Gaussian Process Emulation. We also demonstrate
how Markov-chain Monte-Carlo sampling is a valuable tool for visualising and
analysing the associated multidimensional likelihood surface.
