kMatern

kExp

Character corresponding to the smoothness parameter \(\nu\) of
 Mat&lt;U+00E9&gt;rn kernels. At this stage, the possible values are "1/2"
 (exponential kernel), "3/2" or "5/2".

Matérn kernels, obtained by plugging the Euclidian norm into a
 1-dimensional Matérn function.

Gaussian process regression with an emphasis on kernels.
Quantitative and qualitative inputs are accepted. Some pre-defined
kernels are available, such as radial or tensor-sum for
quantitative inputs, and compound symmetry, low rank, group kernel
for qualitative inputs. The user can define new kernels and
composite kernels through a formula mechanism. Useful methods
include parameter estimation by maximum likelihood, simulation,
prediction and leave-one-out validation.

Olivier Roustant

kergp

Gaussian Process Laboratory

kMatern function

<dl> <dt>d</dt>
<dd></dd>Dimension.
 <dt>nu</dt>
<dd></dd>Character corresponding to the smoothness parameter \(\nu\) of
 Matérn kernels. At this stage, the possible values are "1/2"
 (exponential kernel), "3/2" or "5/2".
 </dl>

Arguments

Matérn Kernels — kMatern

<dl>

 <dt>d</dt>
<dd>

Dimension.
</dd>

 <dt>nu</dt>
<dd>

Character corresponding to the smoothness parameter \(\nu\) of
 Matérn kernels. At this stage, the possible values are "1/2"
 (exponential kernel), "3/2" or "5/2".</dd>
 </dl>

kMatern: Matérn Kernels

Description

Usage

Value

Arguments

References

Examples