2021/08/31 by Raphael Keusch, Hans-Andrea Loeliger, Hans‐Andrea Loeliger +2
Computer Science · Engineering · Mathematics · #Control Systems and Identification #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #Statistical and numerical algorithms #Systems and Control (eess.SY) #cs.LG #cs.SY #eess.SP #eess.SY #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2109.00036
arxiv created 2021/08/31 · openalex publication_date 2021/08/31 · arxiv updated 2021/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Normals with unknown variance (NUV) can represent many useful priors and blend well with Gaussian models and message passing algorithms. NUV representations of sparsifying priors have long been known, and NUV representations of binary (and M-level) priors have been proposed very recently. In this document, we propose NUV representations of half-space constraints and box constraints, which allows to add such constraints to any linear Gaussian model with any of the previously known NUV priors without affecting the computational tractability.