**Lifted Aggregation in Directed First-order Probabilistic Models
**

*By Jacek Kisynski*

Representations that mix graphical models and first-order
logic—called either first-order or relational probabilistic models—are
becoming increasingly popular. In these models, random variables are
parameterized by logical variables. As exact inference for first-order
probabilistic graphical models at the propositional level can be formidably
expensive, there is an ongoing effort to design efficient lifted inference
algorithms for such models. The idea behind lifted inference is to carry out
as much inference as possible without propositionalizing. This talk focuses
on directed first-order models that require an aggregation operator when a
parent random variable is parameterized by logical variables that are not
present in a child random variable. I will describe our work on extending
Milch et al.'s C-FOVE lifted inference algorithm with efficient lifted
aggregation procedures.