Approximate parameter inference in a stochastic reaction-diffusion model
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:669-676, 2010.
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.