Conference paper
Bayesian Inference for Structured Spike and Slab Priors
Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial Gaussian process on the spike and slab probabilities.
Thus, prior information on the structure of the sparsity pattern can be encoded using generic covariance functions. Furthermore, we provide a Bayesian inference scheme for the proposed model based on the expectation propagation framework. Using numerical experiments on synthetic data, we demonstrate the benefits of the model.
Language: | English |
---|---|
Publisher: | Neural Information Processing Systems Foundation |
Year: | 2014 |
Pages: | 1745-1753 |
Proceedings: | 28th Annual Conference on Neural Information Processing Systems (NIPS 2014)Conference on Neural Information Processing Systems |
Types: | Conference paper |
ORCIDs: | Andersen, Michael Riis , Winther, Ole and Hansen, Lars Kai |