5 citations · 6 across the 7 of their papers we have counts for
3 papers · 1 filter
Classical vs. Bayesian methods for linear system identification: point estimators and confidence sets
D. Romeres, G. Prando, G. Pillonetto +1
This paper compares classical parametric methods with recently developed Bayesian methods for system identification. A Full Bayes solution is considered together with one of the st…
Identification of stable models via nonparametric prediction error methods
Diego Romeres, Gianluigi Pillonetto, Alessandro Chiuso
A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimati…
Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso
Aleksandr Y. Aravkin, James V. Burke, Alessandro Chiuso +1
The popular Lasso approach for sparse estimation can be derived via marginalization of a joint density associated with a particular stochastic model. A different marginalization of…