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Conditional probability distributions and Bayes’ theorem are important and powerful tools in measurement, whenever an a priori knowledge about the measurand is available. It is well known that, thanks to Bayes’ theorem, a new information about the measurand coming from a measurement result can be used to revise the a priori knowledge refining its uncertainty. Of course, this tool can be used only...
A still open issue, in uncertainty evaluation, is that of asymmetrical distributions of the values that can be attributed to the measurand. This problem becomes generally not negligible when the measurement function is highly non-linear. In this case the law of uncertainty propagation suggested by the GUM is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions...
Conditional probability distributions and Bayes' theorem are an important and powerful tool in measurement, whenever a priori information about the measurand is available. It is well-known that the measurement result and associated uncertainty (a posteriori information) can be used to revise the a priori information and, hopefully, decrease its uncertainty. This tool can be used only if both a priori...
According to the Guide to the Expression of Uncertainty in Measurement, all significant systematic effects must be identified and compensated. Though this is the best practice in high-accuracy and high-precision measurements, it might become utterly expensive in industrial applications, where cost is an issue and uncompensated systematic effects can be tolerated, provided that their contribution to...
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