Published Jul 14, 2022


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Juan Daniel Molina, MSc

Luis Fernando Giraldo-Jaramillo, MSc

Edilson Delgado-Trejos, PhD



Objective: To propose a methodological procedure that serves as a guide for applying techniques in the measurement uncertainty evaluation, such as GUM, MMC, and Bayes; in addition, to develop an application in a non-trivial case study. Materials and methods: In this paper, a set of steps are proposed that allow validating the measurement uncertainty evaluation from techniques such as GUM, MMC, and Bayes; these were applied as a strategy to evaluate the uncertainty of an indirect measurement process that sought to determine the level of a fluid by measuring the hydrostatic pressure generated by it at rest on the bottom of a container. The results obtained with each technique were compared. Results and discussion: the use of the GUM was found to be valid for the case under study, and the results obtained by applying the Bayesian approach and the MC technique provided highly useful complementary information, such as the Probability Density Function (PDF) of the measurand, which enables a better description of the phenomenon. Likewise, the posterior PDF obtained with Bayes allowed us to approximate closer values around the true values of the measurand, and the ranges of the possible values were broader than those offered by the MMC and the GUM. Conclusions: In the context of the case under study, the Bayesian approach presents more realistic results than GUM and MMC; in addition to the conceptual advantage presented by Bayes, the possibility of updating the results of the uncertainty evaluation in the presence of new evidence.


Uncertainty estimation, GUM, Monte Carlo method, Bayesian inference, indirect measurement Evaluación de incertidumbre, GUM, Método de Monte Carlo, Inferencia Bayesiana, Medición indirecta

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How to Cite
Molina-Muñoz, J. D., Giraldo-Jaramillo, L. F., & Delgado-Trejos, E. (2022). Bayesian Evaluation for Uncertainty of Indirect Measurements in Comparison with GUM and Monte Carlo. Ingenieria Y Universidad, 26.
Electrical and computer engineering