Focused Belief Propagation for Query-Specific Inference

With the increasing popularity of largescale probabilistic graphical models, even "lightweight" approximate inference methods are becoming infeasible. Fortunately, often large parts of the model are of no immediate interest to the end user. Given the variable that the user actually cares about, we show how to quantify edge importance in graphical models and to significantly speed up inference by focusing computation on important parts of the model. Our algorithm empirically demonstrates convergence speedup by multiple times over state of the art.

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Anton Chechetka, Carlos Guestrin
• Keywords: Approximate Inference Methods, Graphical Models, JMLR 2010, Probabilistic Graphical Models