, Other GPR variants Universal kriging: account for trend parameters in the GPR equations. Cf
Multiple outputs: cokriging. Cf, 2013. ,
Discrete x variables: Cf, 2019. ,
, , vol.68, p.74, 2019.
, Other names, (almost) same equations m(x) = k(x, X )k(X , X ) ?1 F is ubiquitous Bayesian linear regression: the posterior distribution is identical to the GPR equations under conditions on the kernel, cf, vol.35
, Kalman filter, see slide 21 of
, LS-SVR: same functional form of predictor (sum of kernels centered), but explicit regularization control (C , whereas GPR is implicit in likelihood)
,
, , vol.69, p.74, 2019.
, , 1988.
, Radial basis functions, multi-variable functional interpolation and adaptive networks, Royal Signals and Radar Establishment Malvern
Introduction to Gaussian Process Surrogate Models. Lecture at 4th MDIS form@ter workshop, 2017. ,
URL : https://hal.archives-ouvertes.fr/cel-01618068
, , 2013.
Multivariate gaussian process emulators with nonseparable covariance structures, Technometrics, vol.55, issue.1, pp.47-56 ,
, , 2019.
, Aerospace System Analysis and Optimization in uncertainty, chapter Cokriging for multifidelity analysis and optimization
, , 2009.
, Multiples métamodèles pour l'approximation et l'optimisation de fonctions numériques multivariables
, Gaussian processes for big data, 2013.
, , 1951.
, A statistical approach to some basic mine valuation problems on the witwatersrand, Journal of the Southern African Institute of Mining and Metallurgy, vol.52, issue.6, pp.119-139
An overview of gradient-enhanced metamodels with applications, Archives of Computational Methods in Engineering, vol.26, issue.1, pp.61-106, 2019. ,
URL : https://hal.archives-ouvertes.fr/emse-01525674
Introduction to Kriging. Lecture at mnmuq2014 summer school, 2014. ,
URL : https://hal.archives-ouvertes.fr/cel-01081304
A Comparison of Regularization Methods for Gaussian Processes. slides of talk at siam conference on optimization op17 and accompanying technical report hal-01264192, 2017. ,
, , 2018.
, Finite-dimensional gaussian approximation with linear inequality constraints, SIAM/ASA Journal on Uncertainty Quantification, vol.6, issue.3, pp.1224-1255
Principles of geostatistics, Economic geology, vol.58, issue.8, pp.1246-1266, 1963. ,
Efficient global optimization of constrained mixed variable problems, Journal of Global Optimization, vol.73, issue.3, pp.583-613, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01942439
, , 2006.
, Gaussian Processes for Machine Learning
Inverse problems. slides of the MNMUQ2019 course. presented at the French-German summer school Modeling and Numerical Methods for Uncertainty Quantification, 2019. ,
, , 2012.
DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization, Journal of Statistical Software, issue.1, p.51 ,
, , 2019.
, Group kernels for gaussian process metamodels with categorical inputs, SIAM/ASA Journal on Uncertainty Quantification
, , 2018.
, Nested kriging predictions for datasets with a large number of observations, Statistics and Computing, vol.28, issue.4, pp.849-867
A generalized representer theorem, 2001. ,
, International conference on computational learning theory, pp.416-426