State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction
Abstract
Latent force models (LFMs) are flexible models that combine mechanistic modelling principles (i.e., physical models) with non-parametric data-driven components. Several key applications of LFMs need non-linearities, which results in analytically intractable inference. In this work we show how non-linear LFMs can be represented as non-linear white noise driven state-space models and present an efficient non-linear Kalman filtering and smoothing based method for approximate state and parameter inference. We illustrate the performance of the proposed methodology via two simulated examples, and apply it to a real-world problem of long-term prediction of GPS satellite orbits.
Cite
Text
Hartikainen et al. "State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction." International Conference on Machine Learning, 2012.Markdown
[Hartikainen et al. "State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/hartikainen2012icml-state/)BibTeX
@inproceedings{hartikainen2012icml-state,
title = {{State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction}},
author = {Hartikainen, Jouni and Seppänen, Mari and Särkkä, Simo},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/hartikainen2012icml-state/}
}