Paper-Conference

Constrained likelihood ratios for detecting sparse signals in highly noisy 3D data

S. Paris, R. F. R. Suleiman, D. Mary, A. Ferrari

Models with products of Dirichlet processes

P. Djuric, A. Ferrari

Nonlinear unmixing of hyperspectral data with partially linear least-squares support vector regression

J. Chen, C. Richard, A. Ferrari, P. Honeine

Classification of multivariate data using Dirichlet process mixture models

P. Djuric, A. Ferrari

Adaptive inverse control using kernel identification

A. Abelli, A. Ferrari, S. Monaco, C. Richard

Generalization of the posterior distribution of the likelihood ratio to composite vs composite hypotheses testing

I. Smith, A. Ferrari

MAP-based sparse detection strategies. Application to the hyperspectral data of the MUSE instrument.

Silvia Paris, David Mary, A. Ferrari

PDR and LRMAP detection tests applied to massive hyperspectral data

S. Paris, D. Mary, A. Ferrari

Restauration des cubes hyperspectraux du spectro-imageur MUSE

S. Bourguignon, D. Mary, H. Carfantan, E. Slezak, A. Ferrari

Sparsity-based composite detection tests. Application to astrophysical hyperspectral data

S. Paris, D. Mary, A. Ferrari, S. Bourguignon