Bayesian Dynamic Mode Decomposition

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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