Modeling the Drift Function in Stochastic Differential Equations using Reduced Rank Gaussian Processes

Loading...
Thumbnail Image
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2018-01-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
778-783
Series
18th IFAC Symposium on System Identification, SYSID 2018, Volume 51, issue 15, IFAC-PapersOnLine
Abstract
In this paper, we propose a Gaussian process-based nonlinear, time-varying drift model for stochastic differential equations. In particular, we combine eigenfunction expansion of the Gaussian process’ covariance kernel in the spatial input variables with spectral decomposition in the time domain to obtain a reduced rank state space representation of the drift model, which avoids the growing complexity (with respect to time) of the full Gaussian process solution. The proposed approach is evaluated on two nonlinear benchmark problems, the Bouc Wen and the cascaded tanks systems.
Description
Keywords
Bayesian methods, estimation, filtering, Gaussian processes, Nonlinear system identification, nonparametric methods, smoothing
Other note
Citation
Hostettler , R , Tronarp , F & Särkkä , S 2018 , Modeling the Drift Function in Stochastic Differential Equations using Reduced Rank Gaussian Processes . in 18th IFAC Symposium on System Identification, SYSID 2018 . 15 edn , vol. 51 , IFAC-PapersOnLine , Elsevier , pp. 778-783 , IFAC Symposium on System Identification , Stockholm , Sweden , 09/07/2018 . https://doi.org/10.1016/j.ifacol.2018.09.137