Non-informative priors in GUM Supplement 1

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 Si 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. (C) 2011 Elsevier Ltd. All rights reserved.