Approximate inference using DC programming for collective graphical models

• Main Authors: NGUYEN, Duc Thien, Akshat KUMAR, LAU, Hoong Chuin, SHELDON, Daniel
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Approximate inference; Concave-convex procedure; Difference of convex functions; Entropy approximations; Generic optimization; Inference problem; Message passing algorithm; Optimization problems; Artificial Intelligence and Robotics; Computer Sciences; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3400; https://ink.library.smu.edu.sg/context/sis_research/article/4401/viewcontent/ApproximateInterference.pdf
• Institution: Singapore Management University

Collective graphical models (CGMs) provide a framework for reasoning about a population of independent and identically distributed individuals when only noisy and aggregate observations are given. Previous approaches for inference in CGMs work on a junction-tree representation, thereby highly limiti...