Generate priors for Gaussian process (GP) regression, classification, or combined unknown constraint models
prior.GP(m, cov = c("isotropic", "separable", "sim"))
prior.CGP(m, cov = c("isotropic", "separable", "sim"))
prior.ConstGP(m, cov.GP = c("isotropic", "separable", "sim"),
cov.CGP = cov.GP)a valid prior object for the appropriate GP model;
By making the output $drate and/or $grate
values negative causes the corresponding lengthscale d
parameter(s) and nugget d parameter to be fixed at the
reciprocal of their absolute values, respectively. This effectively
turns off inference for these values, and allows one to study the GP
predictive distribution as a function of fixed values. When both
are fixed it is sensible to use only one particle (P=1, as an
argument to PL)
positive scalar integer specifying the dimensionality of the input space
whether to use an "isotropic" or "separable" power
exponential correlation function with power 2 -- nugget included;
a single index model ("sim") capability is provided as “beta”
functionality; applies to both regression and classification GPs
specifies the covariance for the real-valued response in the combined unknown constraint GP model
specifies the covariance for the categorical response in the combined unknown constraint GP model
Robert B. Gramacy, rbg@vt.edu
These function generate a default prior object in the correct format
for use with the other PL routines, e.g.,
init.GP and pred.GP. The object returned
may be modified as necessary.
The prior.ConstGP is essentially the combination
of prior.GP and prior.CGP
for regression and classification GP models, respectively
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/
PL, lpredprob.GP,
propagate.GP, init.GP,
pred.GP
## See the demos via demo(package="plgp") and the examples
## section of ?plgp
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