# A divide and conquer sequential Monte Carlo approach to high dimensional filtering

arXiv: https://arxiv.org/abs/2211.14201 · HTML: https://mlsanity.com/paper/2211.14201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2022.

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

- [Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems](https://mlsanity.com/paper/2303.00301/) (2023)

## Abstract (arXiv)

We propose a divide-and-conquer approach to filtering which decomposes the state variable into low-dimensional components to which standard particle filtering tools can be successfully applied and recursively merges them to recover the full filtering distribution. It is less dependent upon factorization of transition densities and observation likelihoods than competing approaches and can be applied to a broader class of models. Performance is compared with state-of-the-art methods on a benchmark problem and it is demonstrated that the proposed method is broadly comparable in settings in which those methods are applicable, and that it can be applied in settings in which they cannot.