文章基本信息

标题：Sparse Bayesian ARX models with flexible noise distributions ⁎
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作者：Johan Dahlin ; Adrian Wills ; Brett Ninness 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：25-30
DOI：10.1016/j.ifacol.2018.09.085
语种：English
出版社：Elsevier
摘要：AbstractThis 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 orwrap-aroundissues 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.
关键词：KeywordsBayesian inferenceHamiltonian Monte CarloGaussian mixture models