symmetrize

Apply symmetrization on estimated graph

Inference of Multiscale graphical models with neighborhood
selection approach. The method is based on solving a convex
optimization problem combining a Lasso and fused-group Lasso
penalties. This allows to infer simultaneously a conditional
independence graph and a clustering partition. The optimization is
based on the Continuation with Nesterov smoothing in a
Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018)
<doi:10.1109/TMI.2018.2829802> implemented in python.

Edmond Sanou

mglasso

Multiscale Graphical Lasso

Tung Le

Christophe Ambroise

Geneviève Robin

symmetrize function

<dl><dt>mat</dt>
<dd>graph or precision matrix</dd>
<dt>rule</dt>
<dd>"and" or "or" rule</dd></dl>

Arguments

Apply symmetrization on estimated graph — symmetrize

<dl>

<dt>mat</dt>
<dd>graph or precision matrix</dd>


<dt>rule</dt>
<dd>"and" or "or" rule</dd>

</dl>

symmetrize: Apply symmetrization on estimated graph

Description

Usage

Value

Arguments