Diffusion Map-based Algorithm for Gain Function Approximation in the Feedback Particle Filter

DOI10.1137/19M124513XzbMath1451.65225arXiv1902.07263OpenAlexW3048753330WikidataQ110649517 ScholiaQ110649517MaRDI QIDQ5119640

Prashant G. Mehta, Amirhossein Taghvaei, Sean P. Meyn

Publication date: 31 August 2020

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.07263

zbMATH Keywords

error analysisKalman filternonlinear filtering

Mathematics Subject Classification ID

Probabilistic methods, particle methods, etc. for boundary value problems involving PDEs (65N75) Filtering in stochastic control theory (93E11) Error bounds for boundary value problems involving PDEs (65N15) Laplace operator, Helmholtz equation (reduced wave equation), Poisson equation (35J05) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (8)

On the mathematical theory of ensemble (linear-Gaussian) Kalman-Bucy filtering ⋮ An optimal control approach to particle filtering ⋮ Analysis of the ensemble Kalman-Bucy filter for correlated observation noise ⋮ Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering ⋮ Affine-invariant ensemble transform methods for logistic regression ⋮ McKean--Vlasov SDEs in Nonlinear Filtering ⋮ Analysis of the feedback particle filter with diffusion map based approximation of the gain ⋮ Controlled interacting particle algorithms for simulation-based reinforcement learning

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