Calculate the second derivative of the likelihood function with respect to
one of the hyperparameters, given the first and second derivative of the
kernel with respect to that hyperparameter.

Functionalities for modelling functional data with
multidimensional inputs, multivariate functional data, and non-separable
and/or non-stationary covariance structure of function-valued processes.
In addition, there are functionalities for functional regression models where
the mean function depends on scalar and/or functional covariates and
the covariance structure depends on functional covariates.
The development version of the package can be found on
<https://github.com/gpfda/GPFDA-dev>.

Evandro Konzen

GPFDA

Gaussian Process for Functional Data Analysis

D2 function

<dl><dt>d1</dt>
<dd>First derivative of the kernel function with respect to the required
hyperparameter.</dd>
<dt>d2</dt>
<dd>Second derivative of the kernel function with respect to the
required hyperparameter.</dd>
<dt>inv.Q</dt>
<dd>Inverse of covariance matrix Q.</dd>
<dt>Alpha.Q</dt>
<dd>This is alpha * alpha'- invQ, where invQ is the inverse of the
covariance matrix Q, and alpha = invQ * Y, where Y is the response.</dd></dl>

Arguments

Second derivative of the likelihood — D2

<dl>

<dt>d1</dt>
<dd>First derivative of the kernel function with respect to the required
hyperparameter.</dd>


<dt>d2</dt>
<dd>Second derivative of the kernel function with respect to the
required hyperparameter.</dd>


<dt>inv.Q</dt>
<dd>Inverse of covariance matrix Q.</dd>


<dt>Alpha.Q</dt>
<dd>This is alpha * alpha'- invQ, where invQ is the inverse of the
covariance matrix Q, and alpha = invQ * Y, where Y is the response.</dd>

</dl>

D2: Second derivative of the likelihood

Description

Usage

Value

Arguments

Details

References

Examples