predict.homGP

Gaussian process predictions using a homoskedastic noise GP object (of class <code>homGP</code>)

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

predict.homGP function

<dl><dt>object</dt>
<dd>an object of class <code>homGP</code>; e.g., as returned by <code>mleHomGP</code></dd>
<dt>x</dt>
<dd>matrix of designs locations to predict at (one point per row)</dd>
<dt>xprime</dt>
<dd>optional second matrix of predictive locations to obtain the predictive covariance matrix between <code>x</code> and <code>xprime</code></dd>
<dt>...</dt>
<dd>no other argument for this method</dd></dl>

Arguments

Gaussian process predictions using a homoskedastic noise GP object (of class homGP) — predict.homGP

<dl>

<dt>object</dt>
<dd>an object of class <code>homGP</code>; e.g., as returned by <code>mleHomGP</code></dd>


<dt>x</dt>
<dd>matrix of designs locations to predict at (one point per row)</dd>


<dt>xprime</dt>
<dd>optional second matrix of predictive locations to obtain the predictive covariance matrix between <code>x</code> and <code>xprime</code></dd>


<dt>...</dt>
<dd>no other argument for this method</dd>

</dl>

predict.homGP: Gaussian process predictions using a homoskedastic noise GP object (of class `homGP`)

Description

Usage

Value

Arguments

Details