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  Bayesian Inference and Optimal Design in the Sparse Linear Model

Seeger, M., Steinke, F., & Tsuda, K. (2007). Bayesian Inference and Optimal Design in the Sparse Linear Model. In M. Meila, & X. Shen (Eds.), Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico (pp. 444-451). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CE75-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-E8A9-9
Genre: Conference Paper

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 Creators:
Seeger, M1, 2, Author              
Steinke, F1, 2, Author              
Tsuda, K1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2007-03
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 4261
 Degree: -

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Title: 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Place of Event: San Juan, Puerto Rico
Start-/End Date: 2007-03-21 - 2007-03-24

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Source 1

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Title: Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico
Source Genre: Proceedings
 Creator(s):
Meila, M, Editor
Shen, X, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 444 - 451 Identifier: -

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
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Pages: - Volume / Issue: 2 Sequence Number: - Start / End Page: - Identifier: -