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 12 articles , 1 standard article )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  2. Mickaƫl Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  3. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  4. Sun, Furong; Gramacy, Robert B.; Haaland, Benjamin; Lawrence, Earl; Walker, Andrew: Emulating satellite drag from large simulation experiments (2019)
  5. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  6. 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)
  7. Sung, Chih-Li; Gramacy, Robert B.; Haaland, Benjamin: Exploiting variance reduction potential in local Gaussian process search (2018)
  8. Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016) not zbMATH
  9. Christopher Paciorek; Benjamin Lipshitz; Wei Zhuo; Prabhat; Cari G. Kaufman; Rollin Thomas: Parallelizing Gaussian Process Calculations in R (2015) not zbMATH
  10. 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)
  11. Picheny, Victor: Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction (2015)
  12. Gramacy, Robert B.; Niemi, Jarad; Weiss, Robin M.: Massively parallel approximate Gaussian process regression (2014)