precision_to_regression

Compute precision matrix from regression vectors

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

precision_to_regression function

<dl><dt>K</dt>
<dd>precision matrix</dd></dl>

Arguments

Compute precision matrix from regression vectors — precision_to_regression

<dl>

<dt>K</dt>
<dd>precision matrix</dd>

</dl>

precision_to_regression: Compute precision matrix from regression vectors

Description

Usage

Value

Arguments