Improved Yau-Yau framework with quasi-Monte Carlo low-discrepancy sampling and multi-scale kernels achieves real-time nonlinear filtering up to 1000 dimensions with local truncation error O(Δt² + D*(n)) and global error O(Δt + D*(n)/Δt).
Flour XIX, Lecture Notes in Math., vol
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An Improved Yau-Yau Algorithm for High Dimensional Nonlinear Filtering Problems
Improved Yau-Yau framework with quasi-Monte Carlo low-discrepancy sampling and multi-scale kernels achieves real-time nonlinear filtering up to 1000 dimensions with local truncation error O(Δt² + D*(n)) and global error O(Δt + D*(n)/Δt).