voila: Variational Inference for Langevin Equations

SGP-based estimate of a Langevin Equation/SDE

1	sgp_sde(kernel, xm, priorMeanFun, posteriorMean, posteriorCov)

`kernel`	@param kernel An object representing a gaussian process' kernel, e.g. a sde_kernel object.
`xm`	A matrix representing the pseudo-inputs of the Sparse Gaussian Process (SGP).
`priorMeanFun`	A function returning the prior mean of the gaussian process.
`posteriorMean`	A vector representing the mean of the gaussian distribution that approximates the posterior of the drift/diffusion term.
`posteriorCov`	A matrix representing the covariance matrix of the gaussian distribution that approximates the posterior of the drift/diffusion term.