EF1 – Extracting dynamical Laws from Complex Data



Kernel Ensemble Kalman Filter and Inference

Project Heads

Péter Koltai, Nicolas Perkowski

Project Members

Ilja Klebanov

Project Duration

01.04.2021 − 31.03.2024

Located at

FU Berlin


We propose combining recent advances in the computation of conditional (posterior probability) distributions via Hilbert space embedding with the stochastic analysis of partially observed dynamical systems —exemplified by ensemble Kalman methods— to develop, analyse, and apply novel learning methods for profoundly nonlinear, multimodal problems.

External Website

Related Publications

Related Pictures

While the framed formula for the conditional expectation is not valid for general random variables, it is always true for their versions embedded into a RKHS.

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