Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions
type: Item
instance of: scholarly article
title: Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions
zbMATH Open document ID: 1461.60020
DOI: 10.1137/20M1315968
author: Toni Karvonen
author: George Wynne
author: Filip Tronarp
author: Chris J. Oates
author: Simo Särkkä
published in: SIAM/ASA Journal on Uncertainty Quantification
publication date: 2020-08-31T00:00:00Z
full work available at URL: https://arxiv.org/abs/2001.10965
Mathematics Subject Classification ID: 60G15
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 68T37
Mathematics Subject Classification ID: 65D05
Mathematics Subject Classification ID: 46E22
zbMATH DE Number: 7242141
zbMATH Keywords: nonparametric regression
zbMATH Keywords: scattered data approximation
zbMATH Keywords: credible sets
zbMATH Keywords: Bayesian cubature
zbMATH Keywords: model misspecification
describes a project that uses: GaussQR
MaRDI profile type: Publication
arXiv ID: 2001.10965
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Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions (cites work)
cites work: Maximum Likelihood Estimation for a Smooth Gaussian Random Field Model

Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions (OpenAlex ID)
OpenAlex ID: W3047099336