Generic function for the Maximum Likelihood estimation of a Gaussian Process
 model.

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

mle function

<dl> <dt>object</dt>
<dd></dd>An object representing a covariance kernel.
 <dt>...</dt>
<dd></dd>Other arguments for methods, typically including a response, a
 design, ... 
</dl>

Arguments

Generic Function: Maximum Likelihood Estimation of a Gaussian Process
  Model — mle

<dl>

 <dt>object</dt>
<dd>

An object representing a covariance kernel.
</dd>

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

Other arguments for methods, typically including a response, a
 design, ...</dd>

 
</dl>

mle: Generic Function: Maximum Likelihood Estimation of a Gaussian Process Model

Description

Usage

Value

Arguments

See Also