init.GP | R Documentation |
Functions for initializing particles for Gaussian process (GP) regression, classification, or combined unknown constraint models
init.GP(prior, d = NULL, g = NULL, Y = NULL) init.CGP(prior, d = NULL, g = NULL) init.ConstGP(prior)
prior |
prior parameters passed from |
d |
initial range (or length-scale) parameter(s) for the GP correlation function(s) |
g |
initial nugget parameter for the GP correlation |
Y |
data used to update GP sufficient information in the case of
|
Returns a particle for internal use in the PL
method
Robert B. Gramacy, rbg@vt.edu
Gramacy, R. and Polson, N. (2011). “Particle learning of Gaussian process models for sequential design and optimization.” Journal of Computational and Graphical Statistics, 20(1), pp. 102-118; arXiv:0909.5262
Gramacy, R. and Lee, H. (2010). “Optimization under unknown constraints”. Bayesian Statistics 9, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (Eds.); Oxford University Press
Gramacy, R. (2020). “Surrogates: Gaussian Process Modeling, Design and Optimization for the Applied Sciences”. Chapman Hall/CRC; https://bobby.gramacy.com/surrogates/
https://bobby.gramacy.com/r_packages/plgp/
PL
, draw.GP
## See the demos via demo(package="plgp") and the examples ## section of ?plgp
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