LAUSR

SYNOPTIC ABSTRACT Binary measurement systems that classify parts as either pass or fail are widely used. In industrial settings, many previously passed and failed parts are often available. We develop a Bayesian model to incorporate baseline information to determine whether a part originated from the stream of previously passed or failed parts as well as the overall pass rate of the inspection system. Simulation studies demonstrate the viability of our proposed method, and we compare our model to simpler models that do not incorporate all baseline information. We show that in some cases incorporation of baseline data can result in the reduction of posterior standard deviations by a factor of two. Additionally, our Bayesian approach has the advantages of allowing the incorporation of expert opinion and not relying on the assumption of normality.