We consider Bayesian analysis of data from multivariate linear regression models whose errors have a distribution that is a scale mixture of normals. Such models are used to analy...
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sample...
Maxime Gautier, Alexandre Janot, Pierre-Olivier Va...
A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribu...
Abstract. Gaussian process prior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the...