3 papers
math.NA2026
High-dimensional Bayesian filtering through deep density approximation
Kasper BÃ¥gmark, Filip Rydin
In this work, we systematically benchmark two recently developed deep density methods for nonlinear filtering. We model the filtering density of a discretely observed stochastic di…
math.NA2026
Nonlinear filtering based on density approximation and deep BSDE prediction
Kasper BÃ¥gmark, Adam Andersson, Stig Larsson
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…
math.NA2026
A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
Kasper BÃ¥gmark, Adam Andersson, Stig Larsson +1
A numerical scheme for approximating the nonlinear filtering density is introduced and its convergence rate is established, theoretically under a parabolic Hörmander condition, an…