Sequential Monte Carlo (SMC) inference for fully Bayesian
Gaussian process (GP) regression and classification models by
particle learning (PL) following Gramacy & Polson (2011) .
The sequential nature of inference
and the active learning (AL) hooks provided facilitate thrifty
sequential design (by entropy) and optimization
(by improvement) for classification and
regression models, respectively.
This package essentially provides a generic
PL interface, and functions (arguments to the interface) which
implement the GP models and AL heuristics. Functions for
a special, linked, regression/classification GP model and
an integrated expected conditional improvement (IECI) statistic
provide for optimization in the presence of unknown constraints.
Separable and isotropic Gaussian, and single-index correlation
functions are supported.
See the examples section of ?plgp and demo(package="plgp")
for an index of demos.