Work

Structured Sparse Modelling with Hierarchical GP

Year: 2017

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1704.08727

Authors: Kuzin, Danil, Isupova, Olga, Mihaylova, Lyudmila

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2611895898

Cites: 14

Cited by:

Related to: 10