- Title
- Sparse Bayesian ARX models with flexible noise distributions
- Creator
- Dahlin, Johan; Wills, Adrian; Ninness, Brett
- Relation
- 18th IFAC Symposium on System Identification SYSID 2018. 18th IFAC Symposium on System Identification SYSID 2018: Proceedings [presented in IFAC-PapersOnLine, Vol. 51, Issue 15] (Stockholm, Sweden 09-11 July, 2018) p. 25-30
- Publisher Link
- http://dx.doi.org/10.1016/j.ifacol.2018.09.085
- Publisher
- International Federation of Automatic Control (IFAC)
- Resource Type
- conference paper
- Date
- 2018
- Description
- 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.
- Subject
- Bayesian inference; Hamiltonian Monte Carlo; Gaussian mixture models
- Identifier
- http://hdl.handle.net/1959.13/1392445
- Identifier
- uon:33401
- Identifier
- ISSN:2405-8963
- Language
- eng
- Full Text
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