summary,fgpm-method

Display the structure of a <code>fgpm</code>
 object and the value of the parameters (variance and length-scales).

Construction and smart selection of Gaussian process models
for analysis of computer experiments
with emphasis on treatment of functional inputs that are regularly sampled. This package
offers: (i) flexible modeling of functional-input regression
problems through the fairly general Gaussian process model; (ii)
built-in dimension reduction for functional inputs; (iii)
heuristic optimization of the structural parameters of the model
(e.g., active inputs, kernel function, type of distance).
An in-depth tutorial in the use of funGp is provided in
Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and
Metamodeling background is provided in
Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.
The algorithm for structural parameter optimization is described
in <https://hal.science/hal-02532713>.

Jose Betancourt

funGp

Gaussian Process Models for Scalar and Functional Inputs

François Bachoc

Thierry Klein

Jeremy Rohmer

Yves Deville

Deborah Idier

summary,fgpm-method function

<dl><dt>object</dt>
<dd>An <code>fgpm</code> object.</dd>
<dt>...</dt>
<dd>Not used yet.</dd></dl>

Arguments

Summary method for fgpm objects — summary,fgpm-method

<dl>

<dt>object</dt>
<dd>An <code>fgpm</code> object.</dd>


<dt>...</dt>
<dd>Not used yet.</dd>

</dl>

summary,fgpm-method: Summary method for `fgpm` objects

Description

Usage

Arguments

Examples