A general Monte-Carlo approach to consider a maximum admissible risk in decision-making procedures based on measurement results





measurement uncertainty, threshold, tolerance limit, acceptance limit, decision making, risk of wrong decision, maximum admissible risk


According to the standards, decision-making procedures generally consider both a threshold that should not be exceeded and the measurement uncertainty that is associated to the measurement result. However, the general indications given in the Standards, in their examples, refer to the particular case when the measurand distributes according to a normal PDF. But a generalization to other cases is not considered and is not straightforward.

In a previous paper, the Authors proposed a decision-making procedure which not only considers the measurement uncertainty and the threshold, but also considers a Maximum Admissible Risk. The proposed procedure leads to decisions taken with a risk of a wrong decision lower than the given Maximum Admissible Risk. In particular, closed-form formulas were derived under specific assumptions for the distributions of the measured values. Hence, the aim of this paper is to generalize the proposed decision rule and method for setting acceptance and rejection limits, by applying the Monte-Carlo method. In this way, it can be generally applied, even when the distribution associated to the measurement result is not a priori known in closed form.

Author Biography

Simona Salicone, Politecnico di Milano

Simona Salicone is an Associate Professor of electrical and electronic measurements at Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano.

Her principal research interests are concerned with the analysis of advanced mathematical methods for uncertainty representation and estimation. Her research activity is reflected in the over 100 papers published in international and national scientific journals, in the proceedings of national and international conferences in the field of instrumentation and measurements, and in two monographs, edited by Springer: “Measurement Uncertainty. An approach via the mathematical theory of evidence” (2007) and “Measuring Uncertainty within the Theory of Evidence” (2018).

In 2004 she has received, by the IEEE Instrumentation and Measurement Society, in recognition of contributions made to measurement uncertainty theory, the 2004 Outstanding Young Engineer Award. In 2007, Simona Salicone is elected, by the Officers and Board of Directors of the IEEE, to the grade of IEEE Senior Member. In years 2013 to 2017, Simona Salicone has been part of the Academic Board of the PhD in Electrical Engineering at Politecnico di Milano. Since 2014 until the end of 2016, she has been part of the Editorial Board of the IEEE Instrumentation and Measurement Magazine, as the responsible of the column “Future Trends in Instrumentation & Measurements”. Since January 2016 until the end of 2017, Simona Salicone has been the Associate Editor in Chief of the IEEE Instrumentation and Measurement Magazine. In 2016 Simona Salicone has received, in appreciation of outstanding service to IEEE Transaction on Instrumentation and Measurement, the recognition as one of the Transactions Outstanding Reviewers of 2015, by the IEEE Instrumentation and Measurement Society. Simona Salicone is the winner of the 2016 IEEE Instrumentation and Measurement Society Faculty Course Development Award.

In 2019, her monograph “Measuring Uncertainty within the Theory of Evidence” was among the top 25% most downloaded eBooks in its respective eBook Collection. In 2020, Simona Salicone has been recognized as a Top 70 Most Published Author of All Time by the IEEE Instrumentation and Measurement Society – Transaction on Instrumentation and Measurement.

Since 2019, Simona is part of the Selection Committee of the IEEE Faculty Course Development Award. Since 2021, she has been part of the Editorial Board of the MDPI Metrology journal.







Research Papers