Multivariate Gaussian Processes
Multivariate Gaussian Processes is a stochastic process that generalizes univariate Gaussian Processes by modeling multivariate functions as a joint Gaussian distribution over multiple output variables, characterized by a mean function and a matrix-valued covariance function encoding inter-variable and spatial-temporal dependencies.
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2017–2026
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Estimation of Non-Normalized Statistical Models by Score Matching
Aapo Hyvärinen · 2005 · 725 citations
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Centre National de la Recherche Scientifique
Paris, France
12 h-index
20 publications
4.3K citations
University of Oxford
Oxford, United Kingdom
10 h-index
30 publications
1.7K citations
Stanford University
Stanford, United States
8 h-index
10 publications
633 citations
University College London
London, United Kingdom
8 h-index
10 publications
878 citations

Imperial College London
London, United Kingdom
7 h-index
11 publications
527 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
7 h-index
15 publications
1.6K citations
Duke University
Durham, United States
6 h-index
11 publications
839 citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
11 publications
985 citations
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Repository · 26 publications · 1.2K citations
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CSDA · 7 publications · 1.2K citations
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