  Gravitational wave astrophysics relies heavily on the use of matched
filtering both to detect signals in noisy data from detectors, and to perform
parameter estimation on those signals. Matched filtering relies upon prior
knowledge of the signals expected to be produced by a range of astrophysical
systems, such as binary black holes. These waveform signals can be computed
using numerical relativity techniques, where the Einstein field equations are
solved numerically, and the signal is extracted from the simulation. Numerical
relativity simulations are, however, computationally expensive, leading to the
need for a surrogate model which can predict waveform signals in regions of the
physical parameter space which have not been probed directly by simulation. We
present a method for producing such a surrogate using Gaussian process
regression which is trained directly on waveforms generated by numerical
relativity. This model returns not just a single interpolated value for the
waveform at a new point, but a full posterior probability distribution on the
predicted value. This model is therefore an ideal component in a Bayesian
analysis framework, through which the uncertainty in the interpolation can be
taken into account when performing parameter estimation of signals.
