mgpCovMat

List of two elements: 'input' and 'response'. The element 'input'
is a list of N vectors, where each vector represents the input covariate
values for a particular output. The element 'response' is the corresponding
list of N matrices (if there are multiple realisations) or vectors (for a
single realisation) representing the response variables.

Data

Calculate a multivariate Gaussian processes covariance matrix given a
vector of hyperparameters

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

mgpCovMat function

<dl><dt>Data</dt>
<dd>List of two elements: 'input' and 'response'. The element 'input'
is a list of N vectors, where each vector represents the input covariate
values for a particular output. The element 'response' is the corresponding
list of N matrices (if there are multiple realisations) or vectors (for a
single realisation) representing the response variables.</dd>
<dt>hp</dt>
<dd>Vector of hyperparameters</dd></dl>

Arguments

Calculate a multivariate Gaussian processes covariance matrix given a
vector of hyperparameters — mgpCovMat

<dl>

<dt>Data</dt>
<dd>List of two elements: 'input' and 'response'. The element 'input'
is a list of N vectors, where each vector represents the input covariate
values for a particular output. The element 'response' is the corresponding
list of N matrices (if there are multiple realisations) or vectors (for a
single realisation) representing the response variables.</dd>


<dt>hp</dt>
<dd>Vector of hyperparameters</dd>

</dl>

mgpCovMat: Calculate a multivariate Gaussian processes covariance matrix given a vector of hyperparameters

Description

Usage

Value

Arguments

References

Examples