CGGPcreate: Create sparse grid GP

In CGGP: Composite Grid Gaussian Processes

Description

Create sparse grid GP

Usage

CGGPcreate(
  d,
  batchsize,
  corr = "PowerExponential",
  grid_sizes = c(1, 2, 4, 4, 8, 12, 20, 28, 32),
  Xs = NULL,
  Ys = NULL,
  HandlingSuppData = "Correct",
  supp_args = list()
)

Arguments

d	Input dimension
batchsize	Number added to design each batch for now only on predictions
corr	Name of correlation function to use. Must be one of "CauchySQT", "CauchySQ", "Cauchy", "Gaussian", "PowerExp", "Matern32", "Matern52".
grid_sizes	Size of grid refinements.
Xs	Supplemental X data
Ys	Supplemental Y data
HandlingSuppData	How should supplementary data be handled? * Correct: full likelihood with grid and supplemental data * Only: only use supplemental data * Ignore: ignore supplemental data
supp_args	Arguments used to fit if Xs and Ys are given

Value

CGGP

See Also

Other CGGP core functions: CGGPappend(), CGGPfit(), predict.CGGP()

Examples

CGGPcreate(d=8,200)

CGGP documentation built on May 8, 2021, 5:06 p.m.