parMap

Map the parameter of a composite covariance kernel on the inputs and
 the parameters of the 1d kernel.

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

parMap function

<dl> <dt>object</dt>
<dd>
 
A composite covariance kernel.
</dd> <dt>...</dt>
<dd></dd>Arguments for methods.
</dl>

Arguments

Map the parameter of a composite covariance kernel on the inputs and
 the parameters of the 1d kernel.

Generic Function: Map the Parameters of a Composite Covariance
  Kernel — parMap

<dl>

 <dt>object</dt>
<dd>
 
A composite covariance kernel.
</dd>

 <dt>...</dt>
<dd>

Arguments for methods.
</dd>

</dl>

parMap: Generic Function: Map the Parameters of a Composite Covariance Kernel

Description

Usage

Value

Arguments