matern32_cor

Applies the Matern 5/2 correlation function to a list of distance matrices using corresponding length-scale parameters.

internal

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

matern32_cor function

<dl><dt>Ms</dt>
<dd>a list with as many elements as inputs. Each element of the list must be a n times n matrix of differences
between the observation coordinates, with n the number of input coordinates.</dd>
<dt>thetas</dt>
<dd>an array with the length-scale parameters corresponding to the inputs for which the distance matrices were provided.</dd></dl>

Arguments

José Betancourt, François Bachoc, Thierry Klein and Jérémy Rohmer

Author

Matern 3/2 correlation function — matern32_cor

<dl>

<dt>Ms</dt>
<dd>a list with as many elements as inputs. Each element of the list must be a n times n matrix of differences
between the observation coordinates, with n the number of input coordinates.</dd>


<dt>thetas</dt>
<dd>an array with the length-scale parameters corresponding to the inputs for which the distance matrices were provided.</dd>

</dl>

matern32_cor: Matern 3/2 correlation function

Description

Usage

Value

Arguments

Author