Jun 26, 2024 � We show that this methodology can be used to learn gain matrices for filtering linear and nonlinear dynamical systems, as well as inflation and�...

Filtering—the task of estimating the conditional distribution of states of a dynamical system given partial, noisy, observations—is important in many areas�...

Jun 26, 2024 � We introduce a framework and methodology for learning parameterized filters and apply it to learning gains for filtering linear and nonlinear�...

Jun 26, 2024 � We show that this methodology can be used to learn gain matrices for filtering linear and nonlinear dynamical systems, as well as inflation and�...

Luk, E., Bach, E., Baptista, R., Stuart, A., 2024: Learning optimal filters using variational inference. Workshop on Machine Learning for Earth System�...

Jun 27, 2024 � Our new preprint on learning optimal parameterized filters using variational inference has been accepted as an oral presentation for the�...

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Aug 13, 2024 � We show that this methodology can be used to learn gain matrices for filtering linear and nonlinear dynamical systems, as well as inflation and�...

We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable�...

We train each method for 3500 data points, freeze the dynamics model, then infer the filtering posterior for 500 subsequent time steps. In Table 2 we report all�...