17.02.2022
Supplement 1 to the GUM (GUM-S1) extends the GUM uncertainty framework to nonlinear functions and non-Gaussian distributions. For this purpose, it employs a Monte Carlo method that yields a probability density function for the measurand. This Monte Carlo method has been successfully applied in numerous applications throughout metrology. However, considerable criticism has been raised against the type A uncertainty evaluation of GUM-S1. Most of the criticism could be addressed by including prior information about the measurand which, however, is beyond the scope of GUM-S1. We propose an alternative Monte Carlo method that will allow prior information about the measurand to be included. The proposed method is based on a Bayesian uncertainty evaluation and applies a simple rejection sampling approach using the Monte Carlo techniques of GUM-S1. Software support in Python, Matlab® and even as a spreadsheet example is provided.
Related Publication: Manuel Marschall Gerd Wübbeler and Clemens Elster (2021). Rejection sampling for Bayesian uncertainty evaluation using the Monte Carlo techniques of GUM-S1. Metrologia, (2021) [DOI: 
10.1088/1681-7575/ac3920].
Expert: Manuel Marschall (8.42)