A BAYESIAN ANALYSIS FOR THE UNCERTAINTY EVALUATION OF A MULTIVARIATE NON LINEAR MEASUREMENT MODEL

Following the principles of the Bayesian inference a multivariate non linear measurement model is studied. In particular the analysis concerns a measurand modeled as the ratio between two quantities: the first one being the sum of two variables, which represent respectively constant systematic effects and the sample of indications, while the second quantity, is referred to a measurement standard. By assuming that the information about the input quantities are in form of prior joint probability density function and a series of direct measurement data are available, the Bayes' theorem is applied to evaluate the posterior expectation (estimate), the posterior standard uncertainty and the posterior coverage probability. Numerical results are reported to asses the validity of the proposed analysis.