Approximate Gaussian Process Inference for the Drift Function in Stochastic Differential Equations
Abstract
We introduce a nonparametric approach for estimating drift functions in systems of stochastic differential equations from incomplete observations of the state vector. Using a Gaussian process prior over the drift as a function of the state vector, we develop an approximate EM algorithm to deal with the unobserved, latent dynamics between observations. The posterior over states is approximated by a piecewise linearized process and the MAP estimation of the drift is facilitated by a sparse Gaussian process regression.
Cite
Text
Ruttor et al. "Approximate Gaussian Process Inference for the Drift Function in Stochastic Differential Equations." Neural Information Processing Systems, 2013.Markdown
[Ruttor et al. "Approximate Gaussian Process Inference for the Drift Function in Stochastic Differential Equations." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/ruttor2013neurips-approximate/)BibTeX
@inproceedings{ruttor2013neurips-approximate,
title = {{Approximate Gaussian Process Inference for the Drift Function in Stochastic Differential Equations}},
author = {Ruttor, Andreas and Batz, Philipp and Opper, Manfred},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {2040-2048},
url = {https://mlanthology.org/neurips/2013/ruttor2013neurips-approximate/}
}