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
propagate.GP(z, Zt, prior)
propagate.CGP(z, Zt, prior)
propagate.ConstGP(z, Zt, prior)These functions return a new particle with the new observation incorporated
new observation whose to be incorporate into the
particle Zt
the particle describing model parameters and sufficient statistics that the new data is being incorporated into
prior parameters passed from PL generated by one of
the prior functions, e.g., prior.GP
Robert B. Gramacy, rbg@vt.edu
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
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
## See the demos via demo(package="plgp") and the examples
## section of ?plgp
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