Gaussian Process Sampling
Gaussian Process Sampling is a stochastic simulation method that generates realizations from a Gaussian process prior, distinguished by its specification of a mean function and covariance kernel to produce sample paths consistent with the probabilistic structure of Gaussian random functions.
98
Publications
4.9K
Citations
323
Authors
169
Institutions
Publications per year
2017–2026
58
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169

Shanghai Jiao Tong University
Shanghai, China
4 h-index
13 publications
274 citations
National Institute for Materials Science
Tsukuba, Japan
3 h-index
8 publications
344 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
8 publications
404 citations
National University of Singapore
Singapore, Singapore
3 h-index
3 publications
98 citations

Imperial College London
London, United Kingdom
3 h-index
5 publications
136 citations
University of Science and Technology of China
Hefei, China
3 h-index
7 publications
146 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
3 publications
75 citations
The University of Tokyo
Tokyo, Japan
2 h-index
4 publications
75 citations
1–8 of 169
Repository · 6 publications · 138 citations
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Conference · 2 publications · 109 citations
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2 publications · 37 citations
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2 publications · 243 citations
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CSDA · 2 publications · 68 citations
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