draw.GP {plgp} | R Documentation |
Metropolis-Hastings draw for GP parameters
Description
Functions for using Metropolis-Hastings (MH) to evolve a particle according to the posterior distribution given by a Gaussian process (GP) for regression, classification, or combined unknown constraint model
Usage
draw.GP(Zt, prior, l = 3, h = 4, thin = 10, Y = NULL)
draw.CGP(Zt, prior, l = 3, h = 4, thin = 10)
draw.ConstGP(Zt, prior, l = 3, h = 4, thin = 10)
Arguments
Zt |
the particle describing model parameters and sufficient statistics that determines the predictive distribution |
prior |
prior parameters passed from |
l |
positive uniform random walk parameter; for old parameter
|
h |
positive uniform random walk parameter; see above |
thin |
thinning level in the MCMC; describes the number of MH rounds executed before the value is saved as a sample from the (marginal) posterior distribution |
Y |
not for external use; used internally by CGP and ConstGP internal routines |
Details
These functions are used in two important places in plgp.
At the user level, they can be used to initialize the particles
at time start
; see PL
and the demos.
Internally, they are used in the PL
propagate
step, e.g., propagate.GP
draw.ConstGP
is a combination
of the draw.GP
and draw.CGP
methods, which are
for regression and classification GPs, respectively
Value
These functions return an updated particle Zt
Author(s)
Robert B. Gramacy, rbg@vt.edu
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
Examples
## See the demos via demo(package="plgp") and the examples
## section of ?plgp