Deep Variational Sequential Monte Carlo for High-Dimensional Observations
Abstract
Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to parameterize the proposal and transition distributions with a neural network, designed to learn from high-dimensional observations. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations. Furthermore, an evidence lower bound based evaluation indicates that our method offers a more accurate representation of the posterior distribution.
Cite
@article{arxiv.2501.05982,
title = {Deep Variational Sequential Monte Carlo for High-Dimensional Observations},
author = {Wessel L. van Nierop and Nir Shlezinger and Ruud J. G. van Sloun},
journal= {arXiv preprint arXiv:2501.05982},
year = {2026}
}