| GPfit-package {GPfit} | R Documentation |
Gaussian Process Modeling
Description
A computationally stable approach of fitting a Gaussian process (GP) model to simulator outputs. It is assumed that the input variables are continuous and the outputs are obtained from scalar valued deterministic computer simulator.
Details
This package implements a slightly modified version of the regularized GP
model proposed in Ranjan et al. (2011). For details see MacDonald et al.
(2015). A new parameterization of the Gaussian correlation is used for the
ease of optimization. This package uses a multi-start gradient based search
algorithm for optimizing the deviance (negative 2*log-likelihood).
For a complete list of functions, use library(help="GPfit").
The
main function for fitting the GP model is GP_fit.
Author(s)
Blake MacDoanld, Hugh Chipman, Pritam Ranjan
Maintainer: Hugh
Chipman <hugh.chipman@acadiau.ca>
References
MacDonald, K.B., Ranjan, P. and Chipman, H. (2015). GPfit: An R
Package for Fitting a Gaussian Process Model to Deterministic Simulator
Outputs. Journal of Statistical Software, 64(12), 1-23.
http://www.jstatsoft.org/v64/i12/
Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable
Approach to Gaussian Process Interpolation of Deterministic Computer
Simulation Data, Technometrics, 53(4), 366 - 378.
Santner, T.J., Williams, B., and Notz, W. (2003), The design and analysis of
computer experiments, Springer Verlag, New York.