The two key ideas that come to our rescue derive from the two approaches that we are trying to combine.  From relational logic, we have the notion of universal patterns, which hold for all objects in a class.  From Bayesian networks, we have the notion of locality of interaction, which in the relational case has a particular twist:  Links give us precisely a notion of “interaction”, and thereby provide a roadmap for which objects can interact with each other.

In this example, we have a template, like a universal quantifier for a probabilistic statement.  It tells us:  “For any registration record in my database, the grade of the student in the course depends on the intelligence of that student and the difficulty of that course.”  This dependency will be instantiated for every object (of the right type) in our domain.  It is also associated with a conditional probability distribution that specifies the nature of that dependence.  We can also have dependencies over several links, e.g., the satisfaction of a student on the teaching ability of the professor who teaches the course.