  We present a new framework for modelling discrete kinematic data. Current
techniques typically involve binning. Our approach works directly with the
discrete data and uses maximum-likelihood methods to assess the probability of
the dataset given model predictions. We avoid making hard cuts on the datasets
by allowing for a contaminating population in our models. We apply our models
to discrete proper-motion and line-of-sight-velocity data of Galactic globular
cluster omega Centauri and find a mildly radial velocity anisotropy beta = 0.10
+/- 0.02, an inclination angle i = 50 +/- 1 deg, a V-band mass-to-light ratio
Upsilon = 2.71 +/- 0.05 Msun/Lsun and a distance d = 4.59 +/- 0.08 kpc. All
parameters are in agreement with previous studies, demonstrating the
feasibility of our methods. We find that the models return lower distances and
higher mass-to-light ratios than expected when we include proper motion stars
with high errors or for which there is some blending. We believe this not a
fault of our models but is instead due to underestimates or missing systematic
uncertainties in the provided errors.
