Message Passing for Collective Graphical Models
Tao Sun, Dan Sheldon, Akshat Kumar · 2015
Collective graphical models (CGMs) are a formalism for inference and learning with aggregate data that are motivated by a model for bird migration. We highlight a close connection between approximate MAP inference in CGMs and marginal inference in standard graphical models. The connection leads us to derive a novel Belief Propagation (BP)-style algorithm for collective graphical models. The al-gorithm is a strict generalization of BP, and is much more efficient than previous approaches to inference in CGMs. We demonstrate its performance on both syn-thetic and real datasets concerning the bird migration problem. 1