Message Passing for Collective Graphical Models

• Main Authors: SUN, Tao, SHELDON, Daniel, KUMAR, Akshat
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Artificial intelligence; Free energy; Functions; Graphic methods; Inference engines; Learning systems; Population statistics; Artificial Intelligence and Robotics; Computer Sciences; Numerical Analysis and Scientific Computing; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/2914; https://ink.library.smu.edu.sg/context/sis_research/article/3914/viewcontent/MessagePassingCollectiveGraphicalModels_2015_ICML.pdf
• Institution: Singapore Management University

Collective graphical models (CGMs) are a formalism for inference and learning about a population of independent and identically distributed individuals when only noisy aggregate data are available. We highlight a close connection between approximate MAP inference in CGMs and marginal inference in st...