plgp (version 1.0)

prior.GP: Generate priors for GP models

Description

Generate priors for Gaussian process (GP) regression, classification, or combined unknown constraint models

Usage

prior.GP(m, cov = c("isotropic", "separable"))
prior.CGP(m, cov = c("isotropic", "separable"))
prior.ConstGP(m, cov.GP = c("isotropic", "separable"),
              cov.CGP = cov.GP)

Arguments

m

positive scalar integer specifying the dimensionality of the input space

cov

whether to use an "isotropic" or "separable" power exponential correlation function with power 2 -- nugget included; applies to both regression and classification GPs

cov.GP

specifies the covariance for the real-valued response in the combined unknown constraint GP model

cov.CGP

specifies the covariance for the categorical response in the combined unknown constraint GP model

Value

• a valid prior object for the appropriate GP model

Details

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

References

Gramacy, R. and Polson, N. (2010). Particle learning of Gaussian process models for sequential design and optimization. Tech. Rep. arXiv:0909.5262, University of Cambridge.

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

http://www.statslab.cam.ac.uk/~bobby/plgp.html

See Also

PL, lpredprob.GP, propagate.GP, init.GP, pred.GP

Examples

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