A Partially Collapsed Sampler for Unsupervised Nonnegative Spike Train Restoration
Signal Processing
2021-02-12 v1
Abstract
In this paper the problem of restoration of non-negative sparse signals is addressed in the Bayesian framework. We introduce a new probabilistic hierarchical prior, based on the Generalized Hyperbolic (GH) distribution, which explicitly accounts for sparsity. This new prior allows on the one hand, to take into account the non-negativity. And on the other hand, thanks to the decomposition of GH distributions as continuous Gaussian mean-variance mixture, allows us to propose a partially collapsed Gibbs sampler (PCGS), which is shown to be more efficient in terms of convergence time than the classical Gibbs sampler.
Cite
@article{arxiv.2102.06081,
title = {A Partially Collapsed Sampler for Unsupervised Nonnegative Spike Train Restoration},
author = {Mehdi Chahine Amrouche and Hervé Carfantan and Jérôme Idier},
journal= {arXiv preprint arXiv:2102.06081},
year = {2021}
}
Comments
in Proceedings of iTWIST'20, Paper-ID: 14, Nantes, France, December, 2-4, 2020