Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data

Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data

The expressive power of Gaussian process (GP) models comes at a cost of poor scalability in the size of the data. To improve their scalability, this paper presents an overview of our recent progress in scaling up GP models for large spatiotemporally correlated data through parallelization on cluster...

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Bibliographic Details
Main Authors: Low, Kian Hsiang, Chen, Jie, Hoang, Trong Nghia, Xu, Nuo, Jaillet, Patrick
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:en_US
Published: Springer-Verlag 2018
Online Access:http://hdl.handle.net/1721.1/115059
https://orcid.org/0000-0002-8585-6566

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http://hdl.handle.net/1721.1/115059
https://orcid.org/0000-0002-8585-6566

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