Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
The sparse linear model, where latent parameters are endowed with a Laplace prior, has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data, or sparse coding of images with overcomplete basis sets. 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 inference efficiently, using the Expectation Propagation method. We also address the problem of optimal design in an application to the identification of gene regulatory networks.
Joint work with Florian Steinke, Koji Tsuda (all MPI for Biological Cybernetics)