Approximate Parameter Inference in a Stochastic Reaction-Diffusion Model
Abstract
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.
Cite
Text
Ruttor and Opper. "Approximate Parameter Inference in a Stochastic Reaction-Diffusion Model." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Ruttor and Opper. "Approximate Parameter Inference in a Stochastic Reaction-Diffusion Model." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/ruttor2010aistats-approximate/)BibTeX
@inproceedings{ruttor2010aistats-approximate,
title = {{Approximate Parameter Inference in a Stochastic Reaction-Diffusion Model}},
author = {Ruttor, Andreas and Opper, Manfred},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {669-676},
volume = {9},
url = {https://mlanthology.org/aistats/2010/ruttor2010aistats-approximate/}
}