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Bayesian Inference and Optimal Design in the Sparse Linear Model

2007

Conference Paper

ei


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.
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

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 11th International Conference on Artificial Intelligence and Statistics
Event Place: San Juan, Puerto Rico

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{4261,
  title = {Bayesian Inference and Optimal Design in the Sparse Linear Model},
  author = {Seeger, M. and Steinke, F. and Tsuda, K.},
  journal = {Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007},
  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},
  month_numeric = {3}
}