Variational bridge constructs for approximate Gaussian process regression


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.

All of Bayesian nonparametrics (especially the useful bits) workshop at NeurIPS 2018