  This paper introduces a method to approximate Gaussian process regression by
representing the problem as a stochastic differential equation and using
variational inference to approximate solutions. The approximations are compared
with full GP regression and generated paths are demonstrated to be
indistinguishable from GP samples. We show that the approach extends easily to
non-linear dynamics and discuss extensions to which the approach can be easily
applied.
