References in zbMATH (referenced in 72 articles , 1 standard article )

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  1. Balata, Alessandro; Ludkovski, Michael; Maheshwari, Aditya; Palczewski, Jan: Statistical learning for probability-constrained stochastic optimal control (2021)
  2. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  3. Xiao, Qian; Xu, Hongquan: A mapping-based universal kriging model for order-of-addition experiments in drug combination studies (2021)
  4. 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)
  5. Bachoc, François; Helbert, Céline; Picheny, Victor: Gaussian process optimization with failures: classification and convergence proof (2020)
  6. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  7. El Amri, Mohamed Reda; Helbert, Céline; Lepreux, Olivier; Zuniga, Miguel Munoz; Prieur, Clémentine; Sinoquet, Delphine: Data-driven stochastic inversion via functional quantization (2020)
  8. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  9. Guan, Qian; Reich, Brian J.; Laber, Eric B.; Bandyopadhyay, Dipankar: Bayesian nonparametric policy search with application to periodontal recall intervals (2020)
  10. Hu, Ruimeng: Deep learning for ranking response surfaces with applications to optimal stopping problems (2020)
  11. 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)
  12. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  13. Mike Ludkovski: mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms (2020) arXiv
  14. Valadão, Mônica A. C.; Batista, Lucas S.: A comparative study on surrogate models for SAEAs (2020)
  15. Xie, Fangzheng; Xu, Yanxun: Adaptive Bayesian nonparametric regression using a kernel mixture of polynomials with application to partial linear models (2020)
  16. Yang, F.; Lin, C. Devon; Ranjan, P.: Global fitting of the response surface via estimating multiple contours of a simulator (2020)
  17. Bachoc, François; Bevilacqua, Moreno; Velandia, Daira: Composite likelihood estimation for a Gaussian process under fixed domain asymptotics (2019)
  18. Bachoc, François; Lagnoux, Agnès; López-Lopera, Andrés F.: Maximum likelihood estimation for Gaussian processes under inequality constraints (2019)
  19. Gu, Mengyang: Jointly robust prior for Gaussian stochastic process in emulation, calibration and variable selection (2019)
  20. 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)

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