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Empirical Inference Conference Paper 2007

Bayesian Inference and Optimal Design in the Sparse Linear Model

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Empirical Inference
Koji Tsuda
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Empirical Inference
Florian Steinke
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Empirical Inference
Matthias Seeger

The sparse linear model has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data. Prior work has either approximated Bayesian inference by expensive Markov chain Monte Carlo, or replaced it by point estimation. We show how to obtain a good approximation to Bayesian analysis efficiently, using the Expectation Propagation method. We also address the problems of optimal design and hyperparameter estimation. We demonstrate our framework on a gene network identification task.

Author(s): Seeger, M. and Steinke, F. and Tsuda, K.
Links:
Book Title: JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007
Journal: Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Pages: 444-451
Year: 2007
Month: March
Day: 0
Editors: Meila, M. , X. Shen
Publisher: JMLR
Bibtex Type: Conference Paper (inproceedings)
Address: Cambridge, MA, USA
Event Name: 11th International Conference on Artificial Intelligence and Statistics
Event Place: San Juan, Puerto Rico
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex 

@inproceedings{4261,
  title = {Bayesian Inference and Optimal Design in the Sparse Linear Model},
  journal = {Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007},
  abstract = {The sparse linear model has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data. Prior work has either approximated Bayesian inference by expensive Markov chain Monte Carlo, or replaced it by point estimation. We show how to obtain a good approximation to Bayesian analysis efficiently, using the Expectation Propagation method. We also address the problems of optimal design and hyperparameter estimation. We demonstrate our framework on a gene network identification task. },
  pages = {444-451},
  editors = {Meila, M. , X. Shen},
  publisher = {JMLR},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = mar,
  year = {2007},
  slug = {4261},
  author = {Seeger, M. and Steinke, F. and Tsuda, K.},
  month_numeric = {3}
}