Message Passing for Collective Graphical Models
نویسندگان
چکیده
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 algorithm is a strict generalization of BP, and is much more efficient than previous approaches to inference in CGMs. We demonstrate its performance on both synthetic and real datasets concerning the bird migration problem.
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