Maximum likelihood estimation and uncertainty quantification for
Gaussian process approximation of deterministic functions

Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions

29 January 2020

Simo Särkkä

Papers citing "Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions"

8 / 8 papers shown

Title
From Target Tracking to Targeting Track -- Part III: Stochastic Process Modeling and Online Learning Tiancheng Li Jingyuan Wang Guchong Li Dengwei Gao 55 2 0 03 Mar 2025
Smoothness Estimation for Whittle-Matérn Processes on Closed Riemannian Manifolds Moritz Korte-Stapff Toni Karvonen Eric Moulines 29 0 0 31 Dec 2023
Interpolation with the polynomial kernels G. Elefante W. Erb Francesco Marchetti E. Perracchione D. Poggiali G. Santin 24 1 0 15 Dec 2022
An asymptotic study of the joint maximum likelihood estimation of the regularity and the amplitude parameters of a Mat{é}rn model on the circle S. Petit 29 1 0 16 Sep 2022
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation Toni Karvonen 27 1 0 10 Mar 2022
Bayesian Numerical Methods for Nonlinear Partial Differential Equations Junyang Wang Jon Cockayne O. Chkrebtii T. Sullivan Chris J. Oates 59 19 0 22 Apr 2021
Small Sample Spaces for Gaussian Processes Toni Karvonen 22 13 0 04 Mar 2021
Bayesian ODE Solvers: The Maximum A Posteriori Estimate Filip Tronarp Simo Sarkka Philipp Hennig 43 42 0 01 Apr 2020