Bayesian dynamic mode decomposition

Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Citations (Scopus)


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.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages8
ISBN (Electronic)9780999241103
Publication statusPublished - 2017
Externally publishedYes
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: Aug 19 2017Aug 25 2017

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823


Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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