# Variance Reduction of Resampling for Sequential Monte Carlo

### Xiongming Dai, Gerald Baumgartner

#### 2024

#### Abstract

A resampling scheme provides a way to switch low-weight particles for sequential Monte Carlo with higherweight particles representing the objective distribution. The less the variance of the weight distribution is, the more concentrated the effective particles are, and the quicker and more accurate it is to approximate the hidden Markov model, especially for the nonlinear case. Normally the distribution of these particles is skewed, we propose repetitive ergodicity in the deterministic domain with the median for resampling and have achieved the lowest variances compared to the other resampling methods. As the size of the deterministic domain M ≪ N (the size of population), given a feasible size of particles under mild assumptions, our algorithm is faster than the state of the art, which is verified by theoretical deduction and experiments of a hidden Markov model in both the linear and non-linear cases.

Download#### Paper Citation

#### in Harvard Style

Dai X. and Baumgartner G. (2024). **Variance Reduction of Resampling for Sequential Monte Carlo**. In *Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART*; ISBN 978-989-758-680-4, SciTePress, pages 34-41. DOI: 10.5220/0012252100003636

#### in Bibtex Style

@conference{icaart24,

author={Xiongming Dai and Gerald Baumgartner},

title={Variance Reduction of Resampling for Sequential Monte Carlo},

booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},

year={2024},

pages={34-41},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0012252100003636},

isbn={978-989-758-680-4},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART

TI - Variance Reduction of Resampling for Sequential Monte Carlo

SN - 978-989-758-680-4

AU - Dai X.

AU - Baumgartner G.

PY - 2024

SP - 34

EP - 41

DO - 10.5220/0012252100003636

PB - SciTePress