A Hybrid Stochastic Framework for Signal Recovery
Pakshal Bohra

Meeting • 10 November 2020

Abstract
We construct a stochastic framework based on hybrid continuous-domain models for the derivation of algorithms that reconstruct multicomponent signals from noisy linear measurements. The hybrid models that we consider involve the superposition of elementary sparse processes which are solutions of linear stochastic differential equations driven by white Lévy noise. We derive a hybrid MAP estimator for the discretized models, and this results in a family of estimators that is consistent with some popular multi-penalty regularization schemes. We present an efficient ADMM implementation and illustrate the advantages of hybrid models with concrete examples.