list_kern_to_inv

Compute the inverse covariance matrices associated with all individuals
in the database, taking into account their specific inputs and
hyper-parameters.

internal

An implementation for the multi-task Gaussian processes with common
mean framework. Two main algorithms, called 'Magma' and 'MagmaClust',
are available to perform predictions for supervised learning problems, in
particular for time series or any functional/continuous data applications.
The corresponding articles has been respectively proposed by Arthur Leroy,
Pierre Latouche, Benjamin Guedj and Servane Gey (2022)
<doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche,
Benjamin Guedj and Servane Gey (2023) <https://jmlr.org/papers/v24/20-1321.html>.
Theses approaches leverage the learning of cluster-specific mean processes,
which are common across similar tasks, to provide enhanced prediction
performances (even far from data) at a linear computational cost (in
the number of tasks). 'MagmaClust' is a generalisation of 'Magma'
where the tasks are simultaneously clustered into groups, each being
associated to a specific mean process. User-oriented functions in the
package are decomposed into training, prediction and plotting
functions. Some basic features (classic kernels, training, prediction) of
standard Gaussian processes are also implemented.

Arthur Leroy

MagmaClustR

Clustering and Prediction using Multi-Task Gaussian Processes
with Common Mean

Pierre Latouche

Pierre Pathé

Alexia Grenouillat

Hugo Lelievre

list_kern_to_inv function

<dl><dt>db</dt>
<dd>A tibble or data frame of input data. Required column: 'ID'.
Suggested column: 'Input' (for indicating the reference input).</dd>
<dt>kern</dt>
<dd>A kernel function.</dd>
<dt>hp</dt>
<dd>A tibble or data frame, containing the hyper-parameters associated
with each individual.</dd>
<dt>pen_diag</dt>
<dd>A number. A jitter term, added on the diagonal to prevent
numerical issues when inverting nearly singular matrices.</dd>
<dt>deriv</dt>
<dd>A character, indicating according to which hyper-parameter the
derivative should be computed. If NULL (default), the function simply returns
the list of covariance matrices.</dd></dl>

Arguments

Compute an inverse covariance matrix for multiple individuals — list_kern_to_inv

<dl>

<dt>db</dt>
<dd>A tibble or data frame of input data. Required column: 'ID'.
Suggested column: 'Input' (for indicating the reference input).</dd>


<dt>kern</dt>
<dd>A kernel function.</dd>


<dt>hp</dt>
<dd>A tibble or data frame, containing the hyper-parameters associated
with each individual.</dd>


<dt>pen_diag</dt>
<dd>A number. A jitter term, added on the diagonal to prevent
numerical issues when inverting nearly singular matrices.</dd>


<dt>deriv</dt>
<dd>A character, indicating according to which hyper-parameter the
derivative should be computed. If NULL (default), the function simply returns
the list of covariance matrices.</dd>

</dl>

list_kern_to_inv: Compute an inverse covariance matrix for multiple individuals

Description

Usage

Value

Arguments

Examples