# Stochastic filtering with moment representation

arXiv: https://arxiv.org/abs/2303.13895 · HTML: https://mlsanity.com/paper/2303.13895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2023.

## Built on by (most cited first, 1 of 1)

- [Diffusion differentiable resampling](https://mlsanity.com/paper/2512.10401/) (2025)

## Abstract (arXiv)

Stochastic filtering refers to estimating the probability distribution of the latent stochastic process conditioned on the observed measurements in time. In this paper, we introduce a new class of convergent filters that represent the filtering distributions by their moments. The key enablement is a quadrature method that uses orthonormal polynomials spanned by the moments. We prove that this moment-based filter is asymptotically exact in the order of moments, and show that the filter is also computationally efficient and is in line with the state of the art.