  We describe an approximate statistical model for the sample variance
distribution of the non-linear matter power spectrum that can be calibrated
from limited numbers of simulations. Our model retains the common assumption of
a multivariate Normal distribution for the power spectrum band powers, but
takes full account of the (parameter dependent) power spectrum covariance. The
model is calibrated using an extension of the framework in Habib et al. (2007)
to train Gaussian processes for the power spectrum mean and covariance given a
set of simulation runs over a hypercube in parameter space. We demonstrate the
performance of this machinery by estimating the parameters of a power-law model
for the power spectrum. Within this framework, our calibrated sample variance
distribution is robust to errors in the estimated covariance and shows rapid
convergence of the posterior parameter constraints with the number of training
simulations.
