params.GP: Extract parameters from GP particles

In plgp: Particle Learning of Gaussian Processes

Extract parameters from GP particles

Description

Extract parameters from particles for Gaussian process (GP) regression, classification, or combined unknown constraint models

Usage

params.GP()
params.CGP()
params.ConstGP()

Details

Collects the parameters from each of the particles (contained in the global variable peach) into a data.frame that can be used for quick summary and visualization, e.g., via hist. These functions are also called to make progress visualizations in PL

Value

returns a data.frame containing summaries for each parameter in its columns

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

Examples

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

plgp documentation built on Oct. 19, 2022, 5:20 p.m.