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 |
| Bibtex Type: | Article (article) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Neural Computing Research Group, Aston University, Birmingham , UK |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@article{2437,
title = {Tractable Inference for Probabilistic Data Models},
journal = {Complexity},
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.},
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},
slug = {2437},
author = {Csato, L. and Opper, M. and Winther, O.},
month_numeric = {4}
}