# The Application of Zig-Zag Sampler in Sequential Markov Chain Monte Carlo

arXiv: https://arxiv.org/abs/2111.10210 · HTML: https://mlsanity.com/paper/2111.10210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Langevin diffusions and the Metropolis-adjusted Langevin algorithm](https://mlsanity.com/paper/1309.2983/) (2013)
- [The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method](https://mlsanity.com/paper/1510.02451/) (2015)
- [The Geometric Foundations of Hamiltonian Monte Carlo](https://mlsanity.com/paper/1410.5110/) (2014)
- [Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo](https://mlsanity.com/paper/1611.07873/) (2016)
- [The Boomerang Sampler](https://mlsanity.com/paper/2006.13777/) (2020)
- [Information-geometric Markov Chain Monte Carlo methods using Diffusions](https://mlsanity.com/paper/1403.7957/) (2014)
- [Piecewise-Deterministic Markov Chain Monte Carlo](https://mlsanity.com/paper/1707.05296/) (2017)
- [Particle Filtering with Invertible Particle Flow](https://mlsanity.com/paper/1607.08799/) (2016)
- [Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains](https://mlsanity.com/paper/1701.04244/) (2017)
- [A Discrete Bouncy Particle Sampler](https://mlsanity.com/paper/1707.05200/) (2017)
- [Reversible Jump PDMP Samplers for Variable Selection](https://mlsanity.com/paper/2010.11771/) (2020)
- [Invertible Particle Flow-based Sequential MCMC with extension to Gaussian Mixture noise models](https://mlsanity.com/paper/1807.02535/) (2018)

## Abstract (arXiv)

Particle filtering methods are widely applied in sequential state estimation within nonlinear non-Gaussian state space model. However, the traditional particle filtering methods suffer the weight degeneracy in the high-dimensional state space model. Currently, there are many methods to improve the performance of particle filtering in high-dimensional state space model. Among these, the more advanced method is to construct the Sequential Makov chian Monte Carlo (SMCMC) framework by implementing the Composite Metropolis-Hasting (MH) Kernel. In this paper, we proposed to discrete the Zig-Zag Sampler and apply the Zig-Zag Sampler in the refinement stage of the Composite MH Kernel within the SMCMC framework which is implemented the invertible particle flow in the joint draw stage. We evaluate the performance of proposed method through numerical experiments of the challenging complex high-dimensional filtering examples. Nemurical experiments show that in high-dimensional state estimation examples, the proposed method improves estimation accuracy and increases the acceptance ratio compared with state-of-the-art filtering methods.