Abstract：Uncertainty evaluation method using GUM S1 and Bayesian analysis is proposed respectively. It is shown analytically that the GUM S1 solution is a special Bayesian analysis with certain parametrization and prior knowledge. When the model relation is linear and improper prior, GUM S1 and the Bayesian analysis yield the same results different results will emerge in general for non-linear model. The difference between the approaches is because of different parametrizations and different priors, which are illustrated by two examples. It is concluded that for a linear model both analyses can be applied, but for non-linear model, the GUM S1 approach may be preferred. When some prior knowledge about the measurement is available in practice work, it is proper to use the Bayesian analysis.
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2017, Vol. 38 
