Guidance for twisted particle filter: a continuous-time perspective
Abstract
The particle filter (PF), also known as sequential Monte Carlo (SMC), approximates high-dimensional probability distributions and their normalizing constants in the discrete-time setting. To reduce the variance of the Monte Carlo approximation, various twisted particle filters (TPFs) have been proposed, in which a twisting function is chosen or learned to modify the Markov transition kernel. Guided by existing control-based importance sampling algorithms in the continuous-time setting, we propose a novel algorithm called the ``Twisted-Path Particle Filter'' (TPPF), in which the twisting function is parameterized by a neural network and trained to minimize a specific KL-divergence between path measures. Numerical experiments illustrate the capability of the proposed algorithm.
Cite
@article{arxiv.2409.02399,
title = {Guidance for twisted particle filter: a continuous-time perspective},
author = {Jianfeng Lu and Yuliang Wang},
journal= {arXiv preprint arXiv:2409.02399},
year = {2026}
}