FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.
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A convergent deep splitting scheme approximates the nonlinear filtering density via Fokker-Planck prediction and exact Bayesian update, with sampling to address high dimensions.
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FLUID: Flow-based Unified Inference for Dynamics
FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.
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A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
A convergent deep splitting scheme approximates the nonlinear filtering density via Fokker-Planck prediction and exact Bayesian update, with sampling to address high dimensions.