Posterior contraction in Gaussian process regression using Wasserstein approximations

DOI10.1093/imaiai/iax003zbMath1383.62108arXiv1502.02336OpenAlexW2963616939MaRDI QIDQ4603714

Publication date: 19 February 2018

Published in: Information and Inference: A Journal of the IMA (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1502.02336

zbMATH Keywords

Gaussian process kernel regression Hermite polynomials random design Wasserstein distance posterior contraction

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Gaussian processes (60G15) Asymptotic properties of nonparametric inference (62G20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

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