Approximate Inference in Collective Graphical Models

• Main Authors: SHELDON, Daniel, SUN, Tao, KUMAR, Akshat, DIETTERICH, Thomas G.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2199; https://ink.library.smu.edu.sg/context/sis_research/article/3199/viewcontent/sheldon13.pdf
• Institution: Singapore Management University

We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical model...