Approximate parameter inference in a stochastic reaction-diffusion model
Andreas Ruttor and Manfred Opper
We present an approximate inference approach to parameter
estimation in a spatio - temporal stochastic process of the reaction
diffusion type. The continuous space limit of an inference
method for Markov jump processes leads to
an approximation which is related to a spatial Gaussian process.
An efficient solution in feature space using a Fourier basis is
applied to inference on simulational data.