Approximate inference using DC programming for collective graphical models

Approximate inference using DC programming for collective graphical models

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...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/3400
https://ink.library.smu.edu.sg/context/sis_research/article/4401/viewcontent/ApproximateInterference.pdf

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