Learning Tractable Graphical Models
Probabilistic graphical models have been successfully applied to a wide variety of fields such as computer vision, natural language processing, robotics, and many more. However, for large scale problems represented using unrestricted probabilistic graphical models, exact inference is often intractab...
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| Language: | en_US |
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University of Oregon
2017
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| Online Access: | http://hdl.handle.net/1794/22799 |