# Bayesian inference for spatio-temporal spike-and-slab priors

arXiv: https://arxiv.org/abs/1509.04752 · HTML: https://mlsanity.com/paper/1509.04752/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [Time-adaptive infinite-dimensional Gaussian process regression on manifolds](https://mlsanity.com/paper/2603.21144/) (2026)

## Abstract (arXiv)

In this work, we address the problem of solving a series of underdetermined linear inverse problems subject to a sparsity constraint. We generalize the spike-and-slab prior distribution to encode a priori correlation of the support of the solution in both space and time by imposing a transformed Gaussian process on the spike-and-slab probabilities. An expectation propagation (EP) algorithm for posterior inference under the proposed model is derived. For large scale problems, the standard EP algorithm can be prohibitively slow. We therefore introduce three different approximation schemes to reduce the computational complexity. Finally, we demonstrate the proposed model using numerical experiments based on both synthetic and real data sets.