**7. Conclusions**

This paper represents a review paper of the RFV approach, proposed in the literature in the last decades.

It has been shown the potentiality of this approach, which is able to represent and propagate measurement results in closed form, by simulating the way the uncertainty contributions propagate through the measurement procedure.

Other more specific applications are present in the more recent literature, like for instance the generalization of Bayes' theorem in the possibility domain [19,20] or the realization of a possibilistic Kalman filter [21,22], thus showing the versatility of the RFV approach.

**Author Contributions:** Conceptualization, S.S.; methodology, S.S.; software, H.V.J.; validation, H.V.J.; formal analysis, H.V.J. and S.S.; investigation, H.V.J. and S.S.; resources, H.V.J. and S.S.; data curation, H.V.J. and S.S.; writing—original draft preparation, S.S.; writing— review and editing, H.V.J. and S.S.; visualization, H.V.J. and S.S.; supervision, S.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
