3 papers
eess.SP2026
An integration-free approach for particle flow filtering
Domonkos Csuzdi, Tamás Bécsi, Olivér TörÅ
Log-homotopy particle flow filters realize nonlinear Bayesian estimation by continuously migrating samples from the prior to the posterior distribution. This transport is governed…
eess.SY2026
A Variational Lagrangian Framework for Log-Homotopy Particle Flow Filters
Olivér TörÅ, 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…
cs.LG2026
Physics-informed neural particle flow for the Bayesian update step
Domonkos Csuzdi, Tamás Bécsi, Olivér TörÅ
The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filters offer an alternative to stoch…