Develops safe particle flow for constrained variational inference by applying control barrier functions to probability densities with theoretical guarantees.
A new approach to linear filtering and prediction prob- lems
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Two new techniques guarantee positive definiteness in the NANO filter's natural gradient covariance update, improving performance over standard Kalman filters on nonlinear systems.
citing papers explorer
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Constrained Variational Inference via Safe Particle Flow
Develops safe particle flow for constrained variational inference by applying control barrier functions to probability densities with theoretical guarantees.
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Natural Gradient Gaussian Approximation Filter with Positive Definiteness Guarantee
Two new techniques guarantee positive definiteness in the NANO filter's natural gradient covariance update, improving performance over standard Kalman filters on nonlinear systems.