Approximate parameter inference in a stochastic reaction-diffusion model

Andreas Ruttor, Manfred Opper
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:669-676, 2010.

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 this Paper

BibTeX
@InProceedings{pmlr-v9-ruttor10a, 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}, pages = {669--676}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/ruttor10a/ruttor10a.pdf}, url = {https://proceedings.mlr.press/v9/ruttor10a.html}, 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.} }
Endnote
%0 Conference Paper %T Approximate parameter inference in a stochastic reaction-diffusion model %A Andreas Ruttor %A Manfred Opper %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-ruttor10a %I PMLR %P 669--676 %U https://proceedings.mlr.press/v9/ruttor10a.html %V 9 %X 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.
RIS
TY - CPAPER TI - Approximate parameter inference in a stochastic reaction-diffusion model AU - Andreas Ruttor AU - Manfred Opper BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-ruttor10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 669 EP - 676 L1 - http://proceedings.mlr.press/v9/ruttor10a/ruttor10a.pdf UR - https://proceedings.mlr.press/v9/ruttor10a.html AB - 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. ER -
APA
Ruttor, A. & Opper, M.. (2010). Approximate parameter inference in a stochastic reaction-diffusion model. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:669-676 Available from https://proceedings.mlr.press/v9/ruttor10a.html.

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