Non-informative priors in GUM supplement 1. http://dx.doi.org/10.1016/j.measurement.2011.05.020

Abstract

Supplement 1 to the ‘Guide to the Expression of Uncertainty in Measurement’ (GUM S1)
proposes a Monte Carlo method for the propagation of the probability density functions
(PDFs) assigned to the input quantities that are related to an output quantity through a
measurement model. Guidance is provided in GUM S1 for assigning PDFs to the input
quantities for which data but no prior knowledge are available. The procedure relies on
Bayes’ theorem and on the use of appropriate non-informative priors. An inconsistency
in the choice of such priors is pointed out.