init.GP

init.CGP

init.ConstGP

prior parameters passed from <code><a href="/link/PL?package=plgp&version=1.1-2" rd-options="" data-mini-rdoc="plgp::PL">PL</a></code> generated by one of
    the prior functions, e.g., <code><a href="/link/prior.GP?package=plgp&version=1.1-2" rd-options="" data-mini-rdoc="plgp::prior.GP">prior.GP</a></code>

prior

initial range (or length-scale) parameter(s) for the GP correlation
    function(s)

initial nugget parameter for the GP correlation

data used to update GP sufficient information in the case of
  <code>init.GP</code>; if <code>NULL</code> then <code>pall$Y</code> is used

Functions for initializing particles for Gaussian process (GP)
  regression, classification, or combined unknown constraint
  models

models

methods

regression

classif

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. 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

init.GP: Initialize particles for GPs

Description

Usage

Arguments

Value

References

See Also

Examples