deriv_crit_IMSPE

Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. 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

<dl><dt>x</dt>
<dd>matrix for the new design (size 1 x d)</dd>
<dt>model</dt>
<dd><code>homGP</code> or <code>hetGP</code> model</dd>
<dt>Wijs</dt>
<dd>optional previously computed matrix of Wijs, see <code>Wij</code></dd></dl>

Arguments

Derivative of crit_IMSPE — deriv_crit_IMSPE

<dl>

<dt>x</dt>
<dd>matrix for the new design (size 1 x d)</dd>


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


<dt>Wijs</dt>
<dd>optional previously computed matrix of Wijs, see <code>Wij</code></dd>

</dl>

deriv_crit_IMSPE: Derivative of crit_IMSPE

Description

Usage

Value

Arguments

See Also