Since there may be some inconsistencies due to the independent learning method, RDNs use Gibbs sampling to recover joint distribution, like DNs.
Therefore, the learners used by DNs, like decision trees or logistic regression, do not work for RDNs.
In addition, if the joint distribution doesn't sum to one owing to the approximations caused by the independent learning, then it is called a numerical inconsistency.
The main advantages of RDNs are their ability to use relationship information to improve the model's performance.
Diagnosis, forecasting, automated vision, sensor fusion and manufacturing control are some examples of problems where RDNs were applied.