Informative Bayesian Type A uncertainty evaluation

Prof. Maurice G. Cox

(Senior Fellow, National Physical Laboratory, United Kingdom)

Cartel

Abstract:

A criticism levelled at the Guide to the expression of uncertainty in measurement (GUM) is that it is based on a mixture of frequentist and Bayesian thinking. In particular, the GUM’s Type A (statistical) uncertainty evaluations are frequentist, whereas the Type B evaluations, using state-of-knowledge distributions, are Bayesian. In contrast, making the GUM fully Bayesian implies, among other things, that a conventional objective Bayesian approach to Type A uncertainty evaluation for a number of observations leads to the impractical consequence that must be at least equal to 4, thus presenting a difficulty for many metrologists. This talk presents a Bayesian analysis of Type A uncertainty evaluation that applies for all , as in the frequentist analysis in the current GUM. The analysis is based on assuming that the observations are drawn from a normal distribution (as in the conventional objective Bayesian analysis), but uses an informative prior based on lower and upper bounds for the standard deviation of the sampling distribution for the quantity under consideration. The main outcome of the analysis is a closed-form mathematical expression for the factor by which the standard deviation of the mean observation should be multiplied to calculate the required standard uncertainty. Metrological examples are used to illustrate the approach, which is straightforward to apply using a formula or look-up table.

Fecha: Viernes 18 de mayo de 2018.
Lugar: Auditorio del IIMAS.
Hora: 12:00 horas.