propagate.GP {plgp}

PL propagate rule for GPs

Description

Incorporation of a new data point for Gaussian process (GP) regression, classification, or combined unknown constraint models; primarily to be used particle learning (PL) propagate step

Usage

propagate.GP(z, Zt, prior)
propagate.CGP(z, Zt, prior)
propagate.ConstGP(z, Zt, prior)

Arguments

z

new observation whose to be incorporate into the particle Zt

Zt

the particle describing model parameters and sufficient statistics that the new data is being incorporated into

prior

prior parameters passed from PL generated by one of the prior functions, e.g., prior.GP

Details

This is the workhorse of the PL propagate step. After re-sampling the particles, PL calls propagate on each of the particles to obtain the set used in the next round/time-step

The propagate.ConstGP is essentially the combination of propagate.GP and propagate.CGP for regression and classification GP models, respectively

Value

These functions return a new particle with the new observation incorporated

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

Examples

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