calc.ecis

calc.eis

calc.ents

calc.ieci

calc.iecis

calc.alcs

calc.vars

calc.ktKik.x

calc2.ktKik.x

covar

covar.sep

covar.sim

cv.folds

dist2covar.symm

distance

ei.adapt

entropy

entropy.adapt

entropy.bvsb

findmin.ConstGP

findmin.GP

hist.particle.params

ieci.adapt

alc.adapt

mindist.adapt

var.adapt

ieci.const.adapt

alc.const.adapt

ieci.ConstGP

alc.ConstGP

ieci.GP

alc.GP

lpost.GP

PL.clear

pred.mean.GP

resample

tquants

unif.propose.pos

mvnorm.propose.rw

updat.GP

util.GP

renorm.weights

renorm.lweights

latents.CGP

phist

getmap.CGP

getmap.GP

<p>Internal <span class="pkg">plgp</span> functions</p>

 internal

plgp-internal

Sequential Monte Carlo (SMC) inference for fully Bayesian
Gaussian process (GP) regression and classification models by
particle learning (PL) following Gramacy & Polson (2011) <arXiv:0909.5262>.
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.

plgp-internal: Internal plgp Functions

Description

Arguments

Details