Bayesian Inference and Optimal Design in the Sparse Linear Model

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.


Published in:
Artificial Intelligence and Statistics 11
Presented at:
Artificial Intelligence and Statistics 11
Year:
2007
Keywords:
Laboratories:




 Record created 2010-12-01, last modified 2018-01-28

External link:
Download fulltext
n/a
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)