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.

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References in zbMATH (referenced in 67 articles , 1 standard article )

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  1. Azaïs, Jean-Marc; Bachoc, François; Lagnoux, Agnès; Nguyen, Thi Mong Ngoc: Semi-parametric estimation of the variogram scale parameter of a Gaussian process with stationary increments (2020)
  2. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  3. El Amri, Mohamed Reda; Helbert, Céline; Lepreux, Olivier; Zuniga, Miguel Munoz; Prieur, Clémentine; Sinoquet, Delphine: Data-driven stochastic inversion via functional quantization (2020)
  4. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  5. Guan, Qian; Reich, Brian J.; Laber, Eric B.; Bandyopadhyay, Dipankar: Bayesian nonparametric policy search with application to periodontal recall intervals (2020)
  6. Hu, Ruimeng: Deep learning for ranking response surfaces with applications to optimal stopping problems (2020)
  7. López-Lopera, Andrés F.; Bachoc, François; Durrande, Nicolas; Rohmer, Jérémy; Idier, Déborah; Roustant, Olivier: Approximating Gaussian process emulators with linear inequality constraints and noisy observations via MC and MCMC (2020)
  8. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  9. Mike Ludkovski: mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms (2020) arXiv
  10. Xie, Fangzheng; Xu, Yanxun: Adaptive Bayesian nonparametric regression using a kernel mixture of polynomials with application to partial linear models (2020)
  11. Yang, F.; Lin, C. Devon; Ranjan, P.: Global fitting of the response surface via estimating multiple contours of a simulator (2020)
  12. Bachoc, François; Bevilacqua, Moreno; Velandia, Daira: Composite likelihood estimation for a Gaussian process under fixed domain asymptotics (2019)
  13. Bachoc, François; Lagnoux, Agnès; López-Lopera, Andrés F.: Maximum likelihood estimation for Gaussian processes under inequality constraints (2019)
  14. Gu, Mengyang: Jointly robust prior for Gaussian stochastic process in emulation, calibration and variable selection (2019)
  15. Hammond, Janelle K.; Chakir, R.; Bourquin, F.; Maday, Y.: PBDW: a non-intrusive reduced basis data assimilation method and its application to an urban dispersion modeling framework (2019)
  16. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  17. Mohammadi, Hossein; Challenor, Peter; Goodfellow, Marc: Emulating dynamic non-linear simulators using Gaussian processes (2019)
  18. Picheny, Victor; Binois, Mickael; Habbal, Abderrahmane: A Bayesian optimization approach to find Nash equilibria (2019)
  19. Salem, Malek Ben; Bachoc, François; Roustant, Olivier; Gamboa, Fabrice; Tomaso, Lionel: Gaussian process-based dimension reduction for goal-oriented sequential design (2019)
  20. Sambakhé, Diariétou; Rouan, Lauriane; Bacro, Jean-Noël; Gozé, Eric: Conditional optimization of a noisy function using a kriging metamodel (2019)

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