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A Framework for Evaluating Approximation Methods for Gaussian Process
  Regression

A Framework for Evaluating Approximation Methods for Gaussian Process Regression

29 May 2012
Krzysztof Chalupka
Christopher K. I. Williams
Iain Murray
    GP
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Papers citing "A Framework for Evaluating Approximation Methods for Gaussian Process Regression"

4 / 4 papers shown
Title
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions
Gledson Rodrigo Tondo
I. Kavrakov
Guido Morgenthal
39
2
0
13 Mar 2025
Approximating multivariate posterior distribution functions from Monte
  Carlo samples for sequential Bayesian inference
Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference
B. Thijssen
L. Wessels
16
8
0
12 Dec 2017
Cluster-based Kriging Approximation Algorithms for Complexity Reduction
Cluster-based Kriging Approximation Algorithms for Complexity Reduction
Bas van Stein
Hao Wang
W. Kowalczyk
M. Emmerich
Thomas Bäck
25
45
0
04 Feb 2017
System Identification through Online Sparse Gaussian Process Regression
  with Input Noise
System Identification through Online Sparse Gaussian Process Regression with Input Noise
Hildo Bijl
Thomas B. Schon
J. Wingerden
M. Verhaegen
22
41
0
29 Jan 2016
1