Efficient and Flexible Inference for Stochastic Systems
Abstract
Many real world dynamical systems are described by stochastic differential equations. Thus parameter inference is a challenging and important problem in many disciplines. We provide a grid free and flexible algorithm offering parameter and state inference for stochastic systems and compare our approch based on variational approximations to state of the art methods showing significant advantages both in runtime and accuracy.
Cite
Text
Bauer et al. "Efficient and Flexible Inference for Stochastic Systems." Neural Information Processing Systems, 2017.Markdown
[Bauer et al. "Efficient and Flexible Inference for Stochastic Systems." Neural Information Processing Systems, 2017.](https://mlanthology.org/neurips/2017/bauer2017neurips-efficient/)BibTeX
@inproceedings{bauer2017neurips-efficient,
title = {{Efficient and Flexible Inference for Stochastic Systems}},
author = {Bauer, Stefan and Gorbach, Nico S and Miladinovic, Djordje and Buhmann, Joachim M},
booktitle = {Neural Information Processing Systems},
year = {2017},
pages = {6988-6998},
url = {https://mlanthology.org/neurips/2017/bauer2017neurips-efficient/}
}