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Tractable Inference for Probabilistic Data Models

2003

Article

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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.

Author(s): Csato, L. and Opper, M. and Winther, O.
Journal: Complexity
Volume: 8
Number (issue): 4
Pages: 64-68
Year: 2003
Month: April
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Institution: Neural Computing Research Group, Aston University, Birmingham , UK
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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@article{2437,
  title = {Tractable Inference for Probabilistic Data Models},
  author = {Csato, L. and Opper, M. and Winther, O.},
  journal = {Complexity},
  volume = {8},
  number = {4},
  pages = {64-68},
  organization = {Max-Planck-Gesellschaft},
  institution = {Neural Computing Research Group, Aston University, Birmingham , UK},
  school = {Biologische Kybernetik},
  month = apr,
  year = {2003},
  month_numeric = {4}
}