4 papers
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…
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…
A priori and a posteriori error estimates for discontinuous Galerkin time-discrete methods via maximal regularity
Georgios Akrivis, Stig Larsson
The maximal regularity property of discontinuous Galerkin methods for linear parabolic equations is used together with variational techniques to establish a priori and a posteriori…
Error analysis for discontinuous Galerkin time-stepping methods for nonlinear parabolic equations via maximal regularity
Georgios Akrivis, Stig Larsson
We consider the discretization of a class of nonlinear parabolic equations by discontinuous Galerkin time-stepping methods and establish a priori as well as conditional a posterior…