Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points-Reference-Cited by-同舟云学术

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Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points

Published:2021-01-24 Issue: Volume: Page:
ISSN:
Container-title:2020 28th European Signal Processing Conference (EUSIPCO)
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Author:

Liu Yuhao,Djuric Petar M.

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9287308/9287310/09287481.pdf?arnumber=9287481

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. Review of the application of modeling and estimation method in system identification for nonlinear state-space models;International Journal of Modeling, Simulation, and Scientific Computing;2024-02-01

3. Topology Inference of Directed Graphs by Gaussian Processes With Sparsity Constraints;IEEE Transactions on Signal Processing;2024

4. Output-Dependent Gaussian Process State-Space Model;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

5. An efficient two‐stage algorithm for parameter identification of non‐linear state‐space models‐based on Gaussian process regression;IET Control Theory & Applications;2023-01-28

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