Bayesian network model for quality control with categorical attribute data

A Bayesian network is developed to monitor a production process where categorical attribute data are available. The number of sample items in each category is entered each time period, allowing the revised probability that the system is in-control or in one of multiple out-of-control states to be calculated. In contrast to other Bayesian methods, qualitative knowledge can be combined with sample data. The network permits the classification of the system into more than two states, so diagnostic analysis can be performed simultaneously with inference. The system state can be updated to reflect evidence on variables that complements the sample data. (C) 2019 Elsevier B.V. All rights reserved.