The variational Gaussian process (VGP), a Bayesian nonparametric model which adapts its shape to match com- plex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity.