Bayesian Dynamic Mode Decomposition

Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi

IJCAI 2017 pp. 2814-2821

doi:10.24963/IJCAI.2017/392 /ijcai/2017/takeishi2017ijcai-bayesian/

Abstract

Dynamic mode decomposition (DMD) is a data-driven method for calculating a modal representation of a nonlinear dynamical system, and has been utilized in various fields of science and engineering. In this talk, we introduce reformulations of DMD, namely probabilistic DMD and Bayesian DMD, with which we can explicitly incorporate observation noises, conduct posterior inference on DMD-related quantities and consider extensions of DMD in a systematic way. Furthermore, we introduce two examples of application: Bayesian sparse DMD and mixtures of probabilistic DMD.

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Text

Takeishi et al. "Bayesian Dynamic Mode Decomposition." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/392

Markdown

[Takeishi et al. "Bayesian Dynamic Mode Decomposition." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/takeishi2017ijcai-bayesian/) doi:10.24963/IJCAI.2017/392

BibTeX

@inproceedings{takeishi2017ijcai-bayesian,
  title     = {{Bayesian Dynamic Mode Decomposition}},
  author    = {Takeishi, Naoya and Kawahara, Yoshinobu and Tabei, Yasuo and Yairi, Takehisa},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {2814-2821},
  doi       = {10.24963/IJCAI.2017/392},
  url       = {https://mlanthology.org/ijcai/2017/takeishi2017ijcai-bayesian/}
}