LAUSR

In this paper, we propose a Bayesian framework to analyse proficiency tests results that allows to combine prior information on laboratories and prior knowledge on the consensus value when no measurement uncertainties nor replicates are reported. For these proficiency tests, where the reported data is reduced to its minimum, we advocate that each piece of information related to the measurement process is valuable and can lead to a more reliable estimation of the consensus value and its associated uncertainty. The resulting marginal posterior distribution of the consensus value relies on the management of expert knowledge used to build prior distributions on the consensus value and the laboratory effects. The choices of priors are discussed to promote the method when the required auxiliary information is available. This new approach is applied on a simulated data set and on a real-life environmental proficiency test.