Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems

08/19/2015
by   Michael Riis Andersen, et al.
0

We are interested in solving the multiple measurement vector (MMV) problem for instances, where the underlying sparsity pattern exhibit spatio-temporal structure motivated by the electroencephalogram (EEG) source localization problem. We propose a probabilistic model that takes this structure into account by generalizing the structured spike and slab prior and the associated Expectation Propagation inference scheme. Based on numerical experiments, we demonstrate the viability of the model and the approximate inference scheme.

READ FULL TEXT
research
09/15/2015

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

In this work, we address the problem of solving a series of underdetermi...
research
04/27/2017

Structured Sparse Modelling with Hierarchical GP

In this paper a new Bayesian model for sparse linear regression with a s...
research
11/02/2021

Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging

Several problems in neuroimaging and beyond require inference on the par...
research
07/08/2018

Spatio-Temporal Instance Learning: Action Tubes from Class Supervision

The goal of this paper is spatio-temporal localization of human actions ...
research
12/10/2011

Convergent Expectation Propagation in Linear Models with Spike-and-slab Priors

Exact inference in the linear regression model with spike and slab prior...
research
05/17/2013

Sparse Approximate Inference for Spatio-Temporal Point Process Models

Spatio-temporal point process models play a central role in the analysis...

Please sign up or login with your details

Forgot password? Click here to reset