deriv_crit_EI

Derivative of EI criterion for GP models

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_EI 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>cst</dt>
<dd>threshold for contour criteria</dd>
<dt>preds</dt>
<dd>pre-computed preds for contour criteria</dd></dl>

Arguments

Derivative of EI criterion for GP models — deriv_crit_EI

<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>cst</dt>
<dd>threshold for contour criteria</dd>


<dt>preds</dt>
<dd>pre-computed preds for contour criteria</dd>

</dl>

deriv_crit_EI: Derivative of EI criterion for GP models

Description

Usage

Arguments

References

See Also