Efficient learning and inference for probabilistic graphical models
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Authors
Nie, Siqi
Issue Date
2016-12
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Electrical engineering
Alternative Title
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
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY