Discriminative Probabilistic Models for Relational Data


	One of the key benefits of the propositional representation was our ability to represent our knowledge without explicit enumeration of the worlds. In turns out that we can do the same in the probabilistic framework. The key idea, introduced by Pearl in the Bayesian network framework, is to use locality of interaction. This is an assumption which seems to be a fairly good approximation of the world in many cases.

	However, this representation suffers from the same problem as other propositional representations. We have to create separate representational units (propositions) for the different entities in our domain. And the problem is that these instances are not independent. For example, the difficulty of CS101 in one network is the same difficulty as in another, so evidence in one network should influence our beliefs in another.