NeurIPS 2017poster9 citations
Efficient and Flexible Inference for Stochastic Systems
Stefan Bauer, Nico S Gorbach, Djordje Miladinovic, Joachim M Buhmann
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.
BibTeX
@inproceedings{NIPS2017_e0126439,
author = {Bauer, Stefan and Gorbach, Nico S and Miladinovic, Djordje and Buhmann, Joachim M},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Efficient and Flexible Inference for Stochastic Systems},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/e0126439e08ddfbdf4faa952dc910590-Paper.pdf},
volume = {30},
year = {2017}
}