Efficient learning and inference for probabilistic graphical models

Authors
Nie, Siqi
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Other Contributors
Ji, Qiang, 1963-
Wozny, M. J. (Michael J.)
Wang, Meng
Mitchell, John E.
Issue Date
2016-12
Keywords
Electrical engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
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Abstract
Probabilistic graphical models (PGMs) are powerful tools to compactly represent the probabilistic dependencies among random variables. With their powerful and intuitive representation ability as well as a body of well-developed algorithms, PGMs have been widely applied to solving many real world problems. Despite significant progress, learning and inference with PGMs remain intractable, in particular for large models and big data. This thesis focuses on developing advanced learning and inference methods for both directed and undirected PGMs to improve their performance on large domains.
Description
December 2016
School of Engineering
Department
Dept. of Electrical, Computer, and Systems Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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