9 citations · 11 across the 5 of their papers we have counts for
5 papers
An integration-free approach for particle flow filtering
Domonkos Csuzdi, Tamás Bécsi, Olivér Törő
Log-homotopy particle flow filters realize nonlinear Bayesian estimation by continuously migrating samples from the prior to the posterior distribution. This transport is governed…
A Variational Lagrangian Framework for Log-Homotopy Particle Flow Filters
Olivér Törő, Domonkos Csuzdi, Tamás Bécsi
The log-homotopy particle flow filter resolves the Bayesian update by transporting particles along a continuous trajectory in pseudo-time. However, the governing partial differenti…
Physics-informed neural particle flow for the Bayesian update step
Domonkos Csuzdi, Tamás Bécsi, Olivér Törő
The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filters offer an alternative to stoch…
Differentiable Particle Filtering using Optimal Placement Resampling
Domonkos Csuzdi, Olivér Törő, Tamás Bécsi
Particle filters are a frequent choice for inference tasks in nonlinear and non-Gaussian state-space models. They can either be used for state inference by approximating the filter…
Analytic solution of the exact Daum-Huang flow equation for particle filters
Olivér Törő, Tamás Bécsi
State estimation for nonlinear systems, especially in high dimensions, is a generally intractable problem, despite the ever-increasing computing power. Efficient algorithms usually…