An optimal transport reformulation of Bayes' rule lets neural networks approximate posterior samples in nonlinear filtering, with error controlled by the optimization gap.
title Estimation with applications to tracking and navigation: theory algorithms and software , publisher John Wiley & Sons
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How to implement the Bayes' formula in the age of ML?
An optimal transport reformulation of Bayes' rule lets neural networks approximate posterior samples in nonlinear filtering, with error controlled by the optimization gap.