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• W1794205709 abstract "We study posterior rates of contraction in Gaussian process regression with unbounded covariate domain. Our argument relies on developing a Gaussian approximation to the posterior of the leading coefficients of a Karhunen--Lo'{e}ve expansion of the Gaussian process. The salient feature of our result is deriving such an approximation in the $L^2$ Wasserstein distance and relating the speed of the approximation to the posterior contraction rate using a coupling argument. Specific illustrations are provided for the Gaussian or squared-exponential covariance kernel." @default.
• W1794205709 created "2016-06-24" @default.
• W1794205709 creator A5033365412 @default.
• W1794205709 creator A5034514833 @default.
• W1794205709 date "2015-02-09" @default.
• W1794205709 modified "2023-09-27" @default.
• W1794205709 title "Posterior contraction in Gaussian process regression using Wasserstein approximations" @default.
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• W1794205709 hasPublicationYear "2015" @default.
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