… the Gibbs sampler for the posterior inference in Bayesian DMD. Moreover, as a specific …
the number of dynamic modes. We investigate the empirical performance of Bayesian DMD …

… On the other hand, our proposal just provides a new aspect of dynamic mode
decomposition, therefore, there is little concern that this paper will affect society negatively. …

… Dynamic mode decomposition has emerged as a leading … that extend dynamic mode
decomposition to systems with … the associated full-state dynamic modes with compressed sensing, …

… the near-optimal low-rank dynamic mode decomposition (DMD). Randomized algorithms …
which is then used to efficiently compute the dynamic modes and eigenvalues. The algorithm is …

… Dynamic mode decomposition (DMD) provides a regression … a bagging, optimized dynamic
mode decomposition (BOP-DMD)… , or Bayesian, forecasting with comprehensive UQ metrics. …

… An improved algorithm for the shallow water equations model reduction: Dynamic mode
decomposition vs. POD. International Journal for Numerical Methods in Fluids, 78(9):552–580, …

… Dynamic mode decomposition (DMD) is a powerful tool for extracting spatial and temporal
patterns from multi-dimensional time series, and it has been used successfully in a wide …

… are needed, particularly when addressing numerous modes and degrees of freedom. To …
, termed the “Bayesian spectral decomposition” method (BSD), employing the decompose-and-…

… is using an expectation-maximization algorithm with Bayesian filtering/smoothing (see, eg,
[33]). Recently, using approximate Bayesian inference with the variational autoencoder (VAE) …

… If all arcs are directed, both within and between slices, the model is called a dynamic
Bayesian network (DBN). (The term “dynamic” means we are modelling a dynamic system, and …