addpall.GP | R Documentation |
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
addpall.GP(Z) addpall.CGP(Z) addpall.ConstGP(Z)
Z |
new observation(s) (usually the next one in “time”) to add to
the |
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
nothing is returned, but global variables are modified
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
## See the demos via demo(package="plgp") and the examples ## section of ?plgp
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