addpall.GP {plgp}

Add data to pall

Description

Add sufficient data common to all particles to the global pall variable, a mnemonic for “particles-all”, for Gaussian process (GP) regression, classification, or combined unknown constraint models

Usage

addpall.GP(Z)
addpall.CGP(Z)
addpall.ConstGP(Z)

Arguments

Z

new observation(s) (usually the next one in “time”) to add to the pall global variable

Details

All three functions add new Z$x to pall$X; addpall.GP also adds Z$y to pall$Y, addpall.CGP also adds Z$c to pall$Y, and addpall.ConstGP does both

Value

nothing is returned, but global variables are modified

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

Examples

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