This digest introduces ways of responding to the call for criterion-referenced information using Bayes' Theorem, a method that was coupled with criterion-referenced testing in the early 1970s (see R. Hambleton and M. Novick, 1973). To illustrate Bayes' Theorem, an example is given in which the goal is to classify an examinee as being a master or nonmaster. Responses to previously piloted items are used to determine the probabilities of mastery and nonmastery, and then the examinee is classified based on those probabilities. The Bayesian network defined in this example is a simple diverging graph in which the master/nonmaster state is causally connected to the set of item responses. When applied to decision support systems and other expert systems, Bayesian networks are typically much more complex, but the computations for basic applications, as illustrated, are quite simple. The framework outlined is applicable to a range of settings and to multidimensional items and tests. Bayesian networks have been used as the basis for computer adaptive tests. The framework can be embedded in an intelligent tutoring system to determine mastery after each instructional unit. One can experiment with simple Bayesian networks using a variety of free, readily available software packages. (SLD)