We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...
—Time-domain algorithms for blind separation of audio sources can be classified as being based either on a partial or complete decomposition of an observation space. The decompo...
Lack of robustness against noise uncertainty is a bottleneck of current spectrum sensing strategies to detect the primary signals. Due to noise uncertainty, the performance of tra...
This work proposes to generalize the method of cokriging when data are spatially sampled curves. A spatial functional linear model is constructed including spatial dependencies be...
David Nerini, Pascal Monestiez, Claude Manté...
Abstract. We propose an algorithmic framework for computing global solutions of variational models with convex regularity terms that permit quite arbitrary data terms. While the mi...
Thomas Pock, Daniel Cremers, Horst Bischof, Antoni...