Bayesian conformity assessment in presence of systematic measurement errors

Conformity assessment of the distribution of the values of a quantity is investigated by using a Bayesian approach. The effect of systematic, non-negligible measurement errors is taken into account. The analysis is general, in the sense that the probability distribution of the quantity can be of any kind, that is even different from the ubiquitous normal distribution, and the measurement model function, linking the measurand with the observable and non-observable influence quantities, can be non-linear. Further, any joint probability density function can be used to model the available knowledge about the systematic errors. It is demonstrated that the result of the Bayesian analysis here developed reduces to the standard result (obtained through a frequentistic approach) when the systematic measurement errors are negligible. A consolidated frequentistic extension of such standard result, aimed at including the effect of a systematic measurement error, is directly compared with the Bayesian result, whose superiority is demonstrated. Application of the results here obtained to the derivation of the operating characteristic curves used for sampling plans for inspection by variables is also introduced