Open AccessProbabilistic measurement modelling may overcome the opposition between the Bayesian and the frequentistic views
Author(s) -
Giovanni Battista Rossi,
Francesco Crenna
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1379/1/012002
Subject(s) - probabilistic logic , opposition (politics) , bayesian probability , computer science , statistical model , machine learning , probabilistic relevance model , artificial intelligence , risk analysis (engineering) , probabilistic analysis of algorithms , political science , business , politics , law
The probabilistic modelling of measurement systems is discussed, in regards to uncertainty evaluation and different models representing the same real system from different standpoints are compared. We suggest that proper probabilistic modelling directly yields evaluation of measurement uncertainty, without making any commitment to specific philosophical schools, such as the frequentistic and the Bayesian ones. We suggest that this model-based approach may have advantages in education and in the development of recommended practices in measurement.