Deep Variational Sequential Monte Carlo for High-Dimensional Observations

• URL: http://arxiv.org/abs/2501.05982v1
• Date: Fri, 10 Jan 2025 14:10:19 GMT
• Title: Deep Variational Sequential Monte Carlo for High-Dimensional Observations
• Authors: Wessel L. van Nierop, Nir Shlezinger, Ruud J. G. van Sloun,
• Abstract summary: 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. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations.
• Score: 39.455729393887786
• License:
• 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.

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