# Structured Sparse Modelling with Hierarchical GP

## TL;DR

This paper introduces a Bayesian hierarchical Gaussian process model for structured sparse linear regression, utilizing Expectation Propagation for inference, and demonstrates its effectiveness on real spatio-temporal data.

## Contribution

It presents a novel hierarchical GP prior for spike and slab coefficients in sparse regression, combined with an EP inference algorithm, advancing structured sparsity modeling.

## Key findings

- Effective modeling of spatio-temporal sparsity
- Successful application to real data
- Improved inference with hierarchical GP prior

## Abstract

In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data.