Bayesian Inference and Optimal Design in the Sparse Linear Model

Matthias Seeger, Florian Steinke, Koji Tsuda
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:444-451, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v2-seeger07a, title = {Bayesian Inference and Optimal Design in the Sparse Linear Model}, author = {Seeger, Matthias and Steinke, Florian and Tsuda, Koji}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {444--451}, year = {2007}, editor = {Meila, Marina and Shen, Xiaotong}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/seeger07a/seeger07a.pdf}, url = {https://proceedings.mlr.press/v2/seeger07a.html}, 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.} }
Endnote
%0 Conference Paper %T Bayesian Inference and Optimal Design in the Sparse Linear Model %A Matthias Seeger %A Florian Steinke %A Koji Tsuda %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-seeger07a %I PMLR %P 444--451 %U https://proceedings.mlr.press/v2/seeger07a.html %V 2 %X 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.
RIS
TY - CPAPER TI - Bayesian Inference and Optimal Design in the Sparse Linear Model AU - Matthias Seeger AU - Florian Steinke AU - Koji Tsuda BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-seeger07a PB - PMLR DP - Proceedings of Machine Learning Research VL - 2 SP - 444 EP - 451 L1 - http://proceedings.mlr.press/v2/seeger07a/seeger07a.pdf UR - https://proceedings.mlr.press/v2/seeger07a.html AB - 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. ER -
APA
Seeger, M., Steinke, F. & Tsuda, K.. (2007). Bayesian Inference and Optimal Design in the Sparse Linear Model. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:444-451 Available from https://proceedings.mlr.press/v2/seeger07a.html.

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