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  Tractable Inference for Probabilistic Data Models

Csato, L., Opper, M., & Winther, O. (2003). Tractable Inference for Probabilistic Data Models. Complexity, 8(4), 64-68. Retrieved from http://www3.interscience.wiley.com/cgi-bin/abstract/104550679/ABSTRACT.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DCAD-2 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DCAE-F
Genre: Journal Article

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Csato, L1, 2, Author           
Opper, M, Author
Winther, O, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              

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 Abstract: We present an approximation technique for probabilistic data models with a large number of hidden variables, based on ideas from statistical physics. We give examples for two nontrivial applications. © 2003 Wiley Periodicals, Inc.

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 Dates: 2003-04
 Publication Status: Issued
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 Identifiers: URI: http://www3.interscience.wiley.com/cgi-bin/abstract/104550679/ABSTRACT
BibTex Citekey: 2437
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Title: Complexity
Source Genre: Journal
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Pages: - Volume / Issue: 8 (4) Sequence Number: - Start / End Page: 64 - 68 Identifier: -