The increase of the worldwide installed photovoltaic (PV) capacity and the intermittent
nature of the solar resource highlights the importance of power forecasting for the grid …

Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …

One of the core problems of modern statistics is to approximate difficult-to-compute
probability densities. This problem is especially important in Bayesian statistics, which …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. They were first dedicated to linear variable selection but numerous …

The paper describes the philosophy, design, functionality, and usage of the Python software
toolbox Chaospy for performing uncertainty quantification via polynomial chaos expansions …

This paper presents a new approach to single-image superresolution, based upon sparse
signal representation. Research on image statistics suggests that image patches can be well …

We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …

It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what
appear to be highly incomplete sets of linear measurements and (2) that this can be done by …

We describe a set of complementary EEG data collection and processing tools recently
developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to …

A variety of practical methods have recently been introduced for finding maximally sparse
representations from overcomplete dictionaries, a central computational task in compressive …