First-Order Probabilistic Inference consists of performing inference with probabilistic models specified
with an expressive language using quantified relational predicates, as opposed to, for example, graphical models, which
specify dependencies for each (propositional) random variable.
It consists of two parts:
BLOG/DBLOG (Bayesian LOGic/ Dynamic BLOG), and
lifted inference.
Projects I have previously worked on: