deriv_crit_IMSPE

matrix for the new design (size 1 x d)

<code>homGP</code> or <code>hetGP</code> model

model

optional previously computed matrix of Wijs, see <code><a rd-options="hetGP" href="/link/Wij?package=hetGP&version=1.1.2&to=hetGP" data-mini-rdoc="hetGP::Wij">Wij</a></code>

Wijs

Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.

Mickael Binois

hetGP

Heteroskedastic Gaussian Process Modeling and Design under
Replication

deriv_crit_IMSPE function

optional previously computed matrix of Wijs, see <code><a rd-options='hetGP' href='Wij'>Wij</a></code>

deriv_crit_IMSPE: Derivative of crit_IMSPE

Description

Usage

Arguments

Value

See Also