NSBMtest

datasets

Greedy Bayesian algorithm to fit the noisy stochastic block model to an observed sparse graph. Moreover, a graph inference procedure to recover Gaussian Graphical Model (GGM) from real data. This procedure comes with a control of the false discovery rate. The method is described in the article "Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure" by Kilian, Rebafka, and Villers (2024) <arXiv:2402.19021>.

Valentin Kilian

noisysbmGGM

Noisy Stochastic Block Model for GGM Inference

Fanny Villers

NSBMtest function

<dl>
<dt><code>dataMatrix</code></dt>
<dd>A square matrix containing the observation of the graph</dd><dt><code>theta</code></dt>
<dd>True NSBM parameters</dd><dt><code>latentZ</code></dt>
<dd>True latent clustering</dd><dt><code>latentAdj</code></dt>
<dd>True latent adjacency matrix</dd>
</dl>

Format

NoisySBM for test — NSBMtest

<dl>
<dt><code>dataMatrix</code></dt>
<dd>A square matrix containing the observation of the graph</dd>

<dt><code>theta</code></dt>
<dd>True NSBM parameters</dd>

<dt><code>latentZ</code></dt>
<dd>True latent clustering</dd>

<dt><code>latentAdj</code></dt>
<dd>True latent adjacency matrix</dd>


</dl>

NSBMtest: NoisySBM for test

Description

Usage

Arguments

Format

Examples