Some of the most challenging problems that programs can face include classification, prediction, and making decisions in the face of messy or incomplete observations, data, or understanding. Bayesian networks provide a basis for implementing solutions to many of these sorts of problems, and Clojure offers excellent raw materials for building them. This talk will briefly introduce the concepts of Bayesian probability and networks, demonstrate the usage of an open source generalized Bayesian network modeling library, and discuss its implementation - highlighting the Clojure facilities that made it possible.