Nonlinear filtering based on density approximation and deep BSDE prediction
Preprint, 2025
A novel approximate Bayesian filter based on backward stochastic differential equations is introduced. It uses a nonlinear Feynman–Kac representation of the filtering problem and the approximation of an unnormalized filtering density using the well-known deep BSDE method and neural networks. The method is trained offline, which means that it can be applied online with new observations. A hybrid a priori-a posteriori error bound is proved under a parabolic Hörmander condition. The theoretical convergence rate is confirmed in two numerical examples.
convergence order
backward stochastic differential equations
Filtering problem
numerical analysis
Hörmander's condition
deep learning
Fokker--Planck equation
Author
Kasper Bågmark
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Adam Andersson
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Stig Larsson
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Subject Categories (SSIF 2025)
Probability Theory and Statistics
Computational Mathematics
Mathematical Analysis
Roots
Basic sciences
Infrastructure
Chalmers e-Commons (incl. C3SE, 2020-)
DOI
10.48550/arXiv.2508.10630