npar

Generic function returning the number of free parameters in a
 covariance 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

npar function

<dl> <dt>object</dt>
<dd>
 
A covariance kernel object.
</dd> <dt>...</dt>
<dd></dd>Other arguments for methods.
</dl>

Arguments

Generic function: Number of Free Parameters in a Covariance Kernel — npar

<dl>

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

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

Other arguments for methods.
</dd>

</dl>

npar: Generic function: Number of Free Parameters in a Covariance Kernel

Description

Usage

Value

Arguments