Bayesian Graphical Models for Predicting Errors in Databases

In recent years, much attention has been directed at various graphical “conditional independence” models and at the application of such graphical models to probabilistic expert systems. However, there exists a broad range of statistical problems to which Bayesian graphical models, in particular, can be applied.Here we demonstrate the simplicity and flexibility of Bayesian graphical models for one important class of statistical problems, namely, predicting the number of errors in a database. We consider three approaches and show how additional approaches can easily be developed using the framework described here.