Tractable Inference for Probabilistic Data Models

Csato, L; Opper, M; Winther, O

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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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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DCAD-2 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DCAE-F

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Urheber:
Csato, L^{1, 2}, Autor
Opper, M, Autor
Winther, O, Autor

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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Zusammenfassung: 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.