@inproceedings{1a4e2f34b0d74eb9be16865ac4a24d54,
title = "Gaussian process state-space models with time-varying parameters and inducing points",
abstract = "We propose time-varying Gaussian process state-space models (TVGPSSM) whose hyper-parameters vary with time. The models have the ability to estimate time-varying functions and thereby increase flexibility to extract information from observed data. The proposed inference approach makes use of time-varying inducing points to adapt to changes of the function, and it exploits hierarchical importance sampling. The experimental results show that the approach has better performance than that of the standard Gaussian process.",
keywords = "Gaussian processes, Hierarchical importance sampling, State-space model, System identification",
author = "Yuhao Liu and Djuric, \{Petar M.\}",
note = "Publisher Copyright: {\textcopyright} 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.; 28th European Signal Processing Conference, EUSIPCO 2020 ; Conference date: 24-08-2020 Through 28-08-2020",
year = "2021",
month = jan,
day = "24",
doi = "10.23919/Eusipco47968.2020.9287481",
language = "English",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
pages = "1462--1466",
booktitle = "28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings",
}