Sparse Bayesian ARX models with flexible noise distributions⁎. Issue 15 (2018)

• Record Type: Journal Article
• Authors: Dahlin, Johan; Wills, Adrian; Ninness, Brett
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 15(2018)
• Page Start: 25
• Page End: 30
• Publication Date: 2018
• Subjects: Bayesian inference -- Hamiltonian Monte Carlo -- Gaussian mixture models; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.085
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper considers the problem of estimating linear dynamic system models when the observations are corrupted by random disturbances with nonstandard distributions. The paper is particularly motivated by applications where sensor imperfections involve significant contribution of outliers or wrap-around issues resulting in multi-modal distributions such as commonly encountered in robotics applications. As will be illustrated, these nonstandard measurement errors can dramatically compromise the effectiveness of standard estimation methods, while a computational Bayesian approach developed here is demonstrated to be equally effective as standard methods in standard measurement noise scenarios, but dramatically more effective in nonstandard measurement noise distribution scenarios.