pred.GP {plgp} | R Documentation |
Prediction for GPs
Description
Prediction on a per-particle basis for Gaussian process (GP) regression, classification, or combined unknown constraint models
Usage
pred.GP(XX, Zt, prior, Y = NULL, quants = FALSE, Sigma = FALSE,
sub = 1:Zt$t)
pred.CGP(XX, Zt, prior, mcreps = 100, cs = NULL)
pred.ConstGP(XX, Zt, prior, quants = TRUE)
Arguments
XX |
|
Zt |
the particle describing model parameters and sufficient statistics that determines the predictive distribution |
prior |
prior parameters passed from |
Y |
not for external use; used internally by CGP and ConstGP internal routines |
quants |
a scalar |
Sigma |
a scalar |
sub |
not for external used; used internally by CGP and ConstGP internal routines |
mcreps |
number of Monte Carlo iterations used in CGP prediction, integrating
over the latent real-valued |
cs |
indicates a class label at which the predictive probability is desired; the entire probability distribution over all class labels will be provided if not specified |
Details
For pred.GP
the predictive mean (and quantiles if quants
= TRUE
is provided. For pred.CGP
the predictive
distribution over the class labels is provided, unless only one
class (cs
) is desired. pred.ConstGP
is a combination
of the pred.GP
and pred.CGP
methods
It is suggested that this function is used in as an argument to
papply
to obtain many predictions - one for each
particle in a cloud - which are combined into a
data.frame
Some of the function arguments aren't meant to
be specified by the user, but are rather there to facilitate usage as a
subroutine inside other PL
functions, such as
lpredprob.GP
and others
Value
A single-row data.frame
is returned with the desired
predictive; these rows are automatically combined when used with
papply
Author(s)
Robert B. Gramacy, rbg@vt.edu
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
Examples
## See the demos via demo(package="plgp") and the examples
## section of ?plgp