# Probabilistic Logical Graphical models like Markov Logic networks etc

I can't quite get a grasp of how and where these Probabilistic Logical Graphical Models (or PLGM or Statistical Relational Learning Models) score better than ordinary Probabilistic Graphical Models (PGM).

Background:

Most introductions to PLGM state that these models extend PGM (which are supposed to be probabilistic propositional logic models) to include predicate logic (or first-order logic).

I am not an expert on logic, but my understanding is that you can never do with propositional logic all that you can do with predicate logic. But the examples given for PLGM seem like they can all be implemented even through PGM. Hence my question above.