laGP

laGP: Local Approximate Gaussian Process Regression. Performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is also provided, as are associated wrapper routines for blackbox optimization under constraints via an augmented Lagrangian scheme, and large scale computer model calibration.


References in zbMATH (referenced in 21 articles , 1 standard article )

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  1. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  2. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  3. Bachoc, F.; Lagnoux, A.: Fixed-domain asymptotic properties of maximum composite likelihood estimators for Gaussian processes (2020)
  4. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  5. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  6. Monterrubio-Gómez, Karla; Roininen, Lassi; Wade, Sara; Damoulas, Theodoros; Girolami, Mark: Posterior inference for sparse hierarchical non-stationary models (2020)
  7. Sung, Chih-Li; Wang, Wenjia; Plumlee, Matthew; Haaland, Benjamin: Multiresolution functional ANOVA for large-scale, many-input computer experiments (2020)
  8. Heaton, Matthew J.; Datta, Abhirup; Finley, Andrew O.; Furrer, Reinhard; Guinness, Joseph; Guhaniyogi, Rajarshi; Gerber, Florian; Gramacy, Robert B.; Hammerling, Dorit; Katzfuss, Matthias; Lindgren, Finn; Nychka, Douglas W.; Sun, Furong; Zammit-Mangion, Andrew: A case study competition among methods for analyzing large spatial data (2019)
  9. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  10. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  11. Sun, Furong; Gramacy, Robert B.; Haaland, Benjamin; Lawrence, Earl; Walker, Andrew: Emulating satellite drag from large simulation experiments (2019)
  12. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  13. Gladish, Daniel W.; Pagendam, Daniel E.; Peeters, Luk J. M.; Kuhnert, Petra M.; Vaze, Jai: Emulation engines: choice and quantification of uncertainty for complex hydrological models (2018)
  14. Hwang, Youngdeok; Lu, Siyuan; Kim, Jae-Kwang: Bottom-up estimation and top-down prediction: solar energy prediction combining information from multiple sources (2018)
  15. Johnson, Leah R.; Gramacy, Robert B.; Cohen, Jeremy; Mordecai, Erin; Murdock, Courtney; Rohr, Jason; Ryan, Sadie J.; Stewart-Ibarra, Anna M.; Weikel, Daniel: Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study (2018)
  16. Sung, Chih-Li; Gramacy, Robert B.; Haaland, Benjamin: Exploiting variance reduction potential in local Gaussian process search (2018)
  17. Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016) not zbMATH
  18. Christopher Paciorek; Benjamin Lipshitz; Wei Zhuo; Prabhat; Cari G. Kaufman; Rollin Thomas: Parallelizing Gaussian Process Calculations in R (2015) not zbMATH
  19. Gramacy, Robert B.; Bingham, Derek; Holloway, James Paul; Grosskopf, Michael J.; Kuranz, Carolyn C.; Rutter, Erica; Trantham, Matt; Drake, R. Paul: Calibrating a large computer experiment simulating radiative shock hydrodynamics (2015)
  20. Picheny, Victor: Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction (2015)

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