DiceKriging
R package DiceKriging: Kriging methods for computer experiments. Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.
Keywords for this software
References in zbMATH (referenced in 41 articles , 1 standard article )
Showing results 1 to 20 of 41.
Sorted by year (- Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
- Gu, Mengyang; Wang, Long: Scaled Gaussian stochastic process for computer model calibration and prediction (2018)
- Gu, Mengyang; Wang, Xiaojing; Berger, James O.: Robust Gaussian stochastic process emulation (2018)
- Ludkovski, Mike; Risk, Jimmy; Zail, Howard: Gaussian process models for mortality rates and improvement factors (2018)
- Marmin, Sébastien; Ginsbourger, David; Baccou, Jean; Liandrat, Jacques: Warped Gaussian processes and derivative-based sequential designs for functions with heterogeneous variations (2018)
- Mathieu Carmassi; Pierre Barbillon; Matthieu Chiodetti; Merlin Keller; Eric Parent: CaliCo: a R package for Bayesian calibration (2018) arXiv
- Rullière, Didier; Durrande, Nicolas; Bachoc, François; Chevalier, Clément: Nested kriging predictions for datasets with a large number of observations (2018)
- Ben Salem, Malek; Roustant, Olivier; Gamboa, Fabrice; Tomaso, Lionel: Universal prediction distribution for surrogate models (2017)
- Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, Michel Lang: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions (2017) arXiv
- Hu, Ruimeng; Ludkovsk, Mike: Sequential design for ranking response surfaces (2017)
- Maatouk, Hassan; Bay, Xavier: Gaussian process emulators for computer experiments with inequality constraints (2017)
- Muehlenstaedt, Thomas; Fruth, Jana; Roustant, Olivier: Computer experiments with functional inputs and scalar outputs by a norm-based approach (2017)
- Owen, N. E.; Challenor, P.; Menon, P. P.; Bennani, S.: Comparison of surrogate-based uncertainty quantification methods for computationally expensive simulators (2017)
- Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
- Azzimonti, Dario; Bect, Julien; Chevalier, Clément; Ginsbourger, David: Quantifying uncertainties on excursion sets under a Gaussian random field prior (2016)
- Beck, Joakim; Guillas, Serge: Sequential design with mutual information for computer experiments (MICE): emulation of a tsunami model (2016)
- Bouhlel, Mohamed Amine; Bartoli, Nathalie; Otsmane, Abdelkader; Morlier, Joseph: An improved approach for estimating the hyperparameters of the Kriging model for high-dimensional problems through the partial least squares method (2016)
- Cousin, Areski; Maatouk, Hassan; Rullière, Didier: Kriging of financial term-structures (2016)
- De Lozzo, Matthias; Marrel, Amandine: Estimation of the derivative-based global sensitivity measures using a Gaussian process metamodel (2016)
- Padonou, Esperan; Roustant, Olivier: Polar Gaussian processes and experimental designs in circular domains (2016)