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Conference Paper

Bayesian Inference and Optimal Design in the Sparse Linear Model

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84205

Seeger,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84235

Steinke,  F
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84265

Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Seeger, M., Steinke, F., & Tsuda, K. (2007). Bayesian Inference and Optimal Design in the Sparse Linear Model. Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007), 444-451.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CE75-B
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.