Generator-Recognizer Networks: A unified approach to probabilistic databases

Ruiwen Chen; Yongyi Mao; Iluju Kiringa

doi:10.1109/ICDE.2010.5447925

Generator-Recognizer Networks: A unified approach to probabilistic databases

Ruiwen Chen, Yongyi Mao, Iluju Kiringa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

Under the tuple-level uncertainty paradigm, we introduce a novel graphical model, Generator-Recognizer Network (GRN), as a model for probabilistic databases. The GRN modeling framework extends existing graphical models of probabilistic databases and is capable of representing a much wider range of dependence structures.

Original language	English
Title of host publication	Proceedings of the 26th International Conference on Data Engineering, ICDE 2010, March 1-6, 2010, Long Beach, California, USA
Pages	169-172
Number of pages	4
ISBN (Electronic)	978-1-4244-5444-0
DOIs	https://doi.org/10.1109/ICDE.2010.5447925
Publication status	Published - Mar 2010

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10.1109/ICDE.2010.5447925

http://dx.doi.org/10.1109/ICDE.2010.5447925

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title = "Generator-Recognizer Networks: A unified approach to probabilistic databases",

abstract = "Under the tuple-level uncertainty paradigm, we introduce a novel graphical model, Generator-Recognizer Network (GRN), as a model for probabilistic databases. The GRN modeling framework extends existing graphical models of probabilistic databases and is capable of representing a much wider range of dependence structures.",

author = "Ruiwen Chen and Yongyi Mao and Iluju Kiringa",

year = "2010",

month = mar,

doi = "10.1109/ICDE.2010.5447925",

language = "English",

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T1 - Generator-Recognizer Networks: A unified approach to probabilistic databases

AU - Chen, Ruiwen

AU - Mao, Yongyi

AU - Kiringa, Iluju

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AB - Under the tuple-level uncertainty paradigm, we introduce a novel graphical model, Generator-Recognizer Network (GRN), as a model for probabilistic databases. The GRN modeling framework extends existing graphical models of probabilistic databases and is capable of representing a much wider range of dependence structures.

U2 - 10.1109/ICDE.2010.5447925

DO - 10.1109/ICDE.2010.5447925

M3 - Conference contribution

SN - 978-1-4244-5445-7

SP - 169

EP - 172

BT - Proceedings of the 26th International Conference on Data Engineering, ICDE 2010, March 1-6, 2010, Long Beach, California, USA

ER -

Generator-Recognizer Networks: A unified approach to probabilistic databases

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