Structured Sparse Modelling with Hierarchical GP

Danil Kuzin, Olga Isupova, Lyudmila Mihaylova

Research output: Working paper / PreprintPreprint

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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.
Original languageEnglish
Publication statusPublished - 27 Apr 2017

Bibliographical note

SPARS 2017


  • stat.ML


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