English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

Bayesian Inference and Optimal Design in the Sparse Linear Model

MPS-Authors
/persons/resource/persons84205

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

/persons/resource/persons84235

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

/persons/resource/persons84265

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

External Resource

http://proceedings.mlr.press/v2/seeger07a/seeger07a.pdf
(Publisher version)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://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.