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): | Empirische Inferenz |
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: |
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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}, doi = {}, month_numeric = {3} } |