init.GP: Initialize particles for GPs

In plgp: Particle Learning of Gaussian Processes

Initialize particles for GPs

Description

Functions for initializing particles for Gaussian process (GP) regression, classification, or combined unknown constraint models

Usage

init.GP(prior, d = NULL, g = NULL, Y = NULL)
init.CGP(prior, d = NULL, g = NULL)
init.ConstGP(prior)

Arguments

prior	prior parameters passed from PL generated by one of the prior functions, e.g., prior.GP
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 init.GP; if NULL then pall$Y is used

Value

Returns a particle for internal use in the PL method

Author(s)

Robert B. Gramacy, [e-mail]

References

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/

See Also

PL, draw.GP

Examples

## See the demos via demo(package="plgp") and the examples
## section of ?plgp

plgp documentation built on Oct. 19, 2022, 5:20 p.m.