Altering Gaussian process to Student-t process for maximum distribution construction-Reference-Cited by-同舟云学术

Altering Gaussian process to Student-t process for maximum distribution construction

Published:2020-11-10 Issue:4 Volume:52 Page:727-755
ISSN:0020-7721
Container-title:International Journal of Systems Science
language:en
Short-container-title:International Journal of Systems Science

Author:

Wang Weidong¹,Yu Qin²,Fasli Maria³

Affiliation:

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China

2. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China

3. Institute for Data Analytics and Science, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207721.2020.1838663

Reference51 articles.

1. A Gaussian Process Convolution Particle Filter for Multiple Extended Objects Tracking with Non-Regular Shapes

2. Efficient Gaussian process regression for large datasets

3. Barsce, J. C., Palombarini, J. A. & Martínez, E. C. (2018). Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization. https://arxiv.org/abs/1805.04748

4. Computationally efficient algorithm for Gaussian Process regression in case of structured samples

5. Bijl, H., Schön, T. B., Wingerden, J.W. v. & Verhaegen, M. (2017). A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization. https://arxiv.org/abs/1604.00169v3

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