  We present a novel and efficient method for fitting dynamical models of
stellar kinematic data in dwarf spheroidal galaxies (dSph). Our approach is
based on Gaussian-process emulation (GPE), which is a sophisticated form of
curve fitting that requires fewer training data than alternative methods. We
use a set of validation tests and diagnostic criteria to assess the performance
of the emulation procedure. We have implemented an algorithm in which both the
GPE procedure and its validation are fully automated. Applying this method to
synthetic data, with fewer than 100 model evaluations we are able to recover a
robust confidence region for the three-dimensional parameter vector of a toy
model of the phase-space distribution function of a dSph. Although the
dynamical model presented in this paper is low-dimensional and static, we
emphasize that the algorithm is applicable to any scheme that involves the
evaluation of computationally expensive models. It therefore has the potential
to render tractable previously intractable problems, for example, the modelling
of individual dSphs using high-dimensional, time-dependent N-body simulations.
