Particle Learning of Gaussian Processes
Description
Sequential Monte Carlo inference for fully Bayesian
Gaussian process (GP) regression and classification models by
particle learning (PL). 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 is
provides for optimization in the presence of unknown
constraints. See the examples section of ?plgp and
demo(package="plgp") for an index of examples