Bayesian Inference for Structured Spike and Slab Priors
Abstract
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.
Cite
Text
Andersen et al. "Bayesian Inference for Structured Spike and Slab Priors." Neural Information Processing Systems, 2014.Markdown
[Andersen et al. "Bayesian Inference for Structured Spike and Slab Priors." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/andersen2014neurips-bayesian/)BibTeX
@inproceedings{andersen2014neurips-bayesian,
title = {{Bayesian Inference for Structured Spike and Slab Priors}},
author = {Andersen, Michael R and Winther, Ole and Hansen, Lars K.},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {1745-1753},
url = {https://mlanthology.org/neurips/2014/andersen2014neurips-bayesian/}
}