Bayesian network model for quality control with categorical attribute data - Cobb, Barry R.; Li, Linda (Applied soft computing, 2019)
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. •Data classified into three or more categories and/or related to multiple defective conditions or system states.•Consideration of the proportion of output in each category as a continuous random variable.•Sample sizes and intervals that vary over time.•Combination of quantitative sample observations and the qualitative knowledge possessed by managers.•Inclusion of additional factor variables that affect the state of the process.