  We introduce a novel paradigm for learning non-parametric drift and diffusion
functions for stochastic differential equation (SDE). The proposed model learns
to simulate path distributions that match observations with non-uniform time
increments and arbitrary sparseness, which is in contrast with gradient
matching that does not optimize simulated responses. We formulate sensitivity
equations for learning and demonstrate that our general stochastic distribution
optimisation leads to robust and efficient learning of SDE systems.
