Bayesian dynamic mode decomposition

Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

27 被引用数 (Scopus)

抄録

Dynamic mode decomposition (DMD) is a datadriven method for calculating a modal representation of a nonlinear dynamical system, and it has been utilized in various fields of science and engineering. In this paper, we propose Bayesian DMD, which provides a principled way to transfer the advantages of the Bayesian formulation into DMD. To this end, we first develop a probabilistic model corresponding to DMD, and then, provide the Gibbs sampler for the posterior inference in Bayesian DMD. Moreover, as a specific example, we discuss the case of using a sparsity-promoting prior for an automatic determination of the number of dynamic modes. We investigate the empirical performance of Bayesian DMD using synthetic and real-world datasets.

本文言語英語
ホスト出版物のタイトル26th International Joint Conference on Artificial Intelligence, IJCAI 2017
編集者Carles Sierra
出版社International Joint Conferences on Artificial Intelligence
ページ2814-2821
ページ数8
ISBN(電子版)9780999241103
DOI
出版ステータス出版済み - 2017
外部発表はい
イベント26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, オーストラリア
継続期間: 8 19 20178 25 2017

出版物シリーズ

名前IJCAI International Joint Conference on Artificial Intelligence
0
ISSN(印刷版)1045-0823

その他

その他26th International Joint Conference on Artificial Intelligence, IJCAI 2017
国/地域オーストラリア
CityMelbourne
Period8/19/178/25/17

All Science Journal Classification (ASJC) codes

  • 人工知能

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