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Gaussian process (GP) regression is a powerful interpolation technique due to its flexibility in capturing non-linearity.
The intrinsic random functions and their applications
Georges Matheron · 1973
Earlier work this paper cites.
Spline smoothing: the equivalent variable kernel method
Bernard W Silverman · 1984
Earlier work this paper cites.
Asymptotics via empirical processes
David Pollard · 1989
Earlier work this paper cites.
Design and analysis of computer experiments
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Earlier work this paper cites.
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Grace Wahba · 1990
Earlier work this paper cites.
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Carla Currin, Toby Mitchell, Max Morris, and Don Ylvisaker · 1991
Earlier work this paper cites.
An analysis of Bayesian inference for nonparametric regression
Dennis D Cox · 1993
Earlier work this paper cites.
A constrained risk inequality with applications to nonparametric functional estimation
Lawrence D Brown and Mark G Low · 1996
Earlier work this paper cites.
Weak convergence
Aad W Van Der Vaart and Jon A Wellner · 1996
Earlier work this paper cites.
Wald lecture: On the Bernstein-von Mises theorem with infinite-dimensional parameters
David Freedman · 1999
Earlier work this paper cites.
Exact adaptive pointwise estimation on sobolev classes of densities
Cristina Butucea · 2001
Earlier work this paper cites.
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Marc C Kennedy and Anthony O’Hagan · 2001
Earlier work this paper cites.
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Shahar Mendelson · 2002
Earlier work this paper cites.
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Earlier work this paper cites.
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Tong Zhang · 2005
Earlier work this paper cites.
Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher KI Williams · 2006
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Andrea Caponnetto and Ernesto De Vito · 2007
Earlier work this paper cites.
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Sudipto Banerjee, Alan E Gelfand, Andrew O Finley, and Huiyan Sang · 2008
Earlier work this paper cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Noel Cressie · 2015
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