Stochastic Models and Techniques for Sparse Signals
Arash Amini, EPFL STI LIB

Seminar • 19 March 2012 • BM 4.233

Abstract
Stochastic models are currently the main tools for interpreting unpredictable parts in physical phenomena. To better cope with the real world data, it is recently shown that it is possible to include the sparsity/compressibility property in the conventional stochastic models by considering various innovation models. In this talk, after a brief review of the new model, some results about denoising and interpolation of such processes will be covered. The results are obtained by employing the spline theory tools in stochastic processes and using characteristic forms in estimation problems.