^March April – 2011 ^May

First-Order Probabilistic Models extend the Propositional Graphical Models (e.g. Markov Network) by introducing the concept of domain entities, along with a first-order language which depicts the properties of each entity and the various interactions which they exhibit. One way of performing inference in first-order models is to construct a propositional model of the same joint distribution, and apply one of the known inference methods. However, it is desirable to apply inference directly to the first-order model, thus avoiding an explicit extraction of the propositional model, which can be very large. Moreover, an inference task in a first-order model may require exponentially less amount of time compared with its propositional counterpart.