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 mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration.
|Author||Robert B. Gramacy <firstname.lastname@example.org>|
|Date of publication||2017-01-09 21:46:21|
|Maintainer||Robert B. Gramacy <email@example.com>|
aGP: Localized Approximate GP Regression For Many Predictive...
alcGP: Improvement statistics for sequential or local design
darg: Generate Priors for GP correlation
deleteGP: Delete C-side Gaussian Process Objects
discrep.est: Estimate Discrepency in Calibration Model
distance: Calculate Euclidean distance between pairs of points
fcalib: Objective function for performing large scale computer model...
laGP: Localized Approximate GP Prediction At a Single Input...
llikGP: Calculate a GP log likelihood
mleGP: Inference for GP correlation parameters
newGP: Create A New GP Object
optim.auglag: Optimize an objective function under multiple blackbox...
predGP: GP Prediction/Kriging