  Due to the intractable nature of exact lifted inference, research has
recently focused on the discovery of accurate and efficient approximate
inference algorithms in Statistical Relational Models (SRMs), such as Lifted
First-Order Belief Propagation. FOBP simulates propositional factor graph
belief propagation without constructing the ground factor graph by identifying
and lifting over redundant message computations. In this work, we propose a
generalization of FOBP called Lifted Generalized Belief Propagation, in which
both the region structure and the message structure can be lifted. This
approach allows more of the inference to be performed intra-region (in the
exact inference step of BP), thereby allowing simulation of propagation on a
graph structure with larger region scopes and fewer edges, while still
maintaining tractability. We demonstrate that the resulting algorithm converges
in fewer iterations to more accurate results on a variety of SRMs.
