Arc Confidence measures the confidence/robustness/variability of the machine learned arcs.
One of the three resampling methods is used for generating data sets that are thus utilized for learning networks .
Arc Confidence has been first updated in version 7.0.
As of version 8.0, two tables describing Arc and Edge Frequencies are available at the end of the Arc Confidence Analysis Report.
Arcs and Edges result from the conversion of the Bayesian network into its corresponding Essential Graph.

Let's suppose the ground truth network we are looking for is described below: Its Essential Graph is as follow: Let's assume we have a data set that contains samples from this joint distribution. After creating 100 data sets with Bootstrap and learning 100 networks, we got the following tables: Below is the corresponding Synthesis Structure: 