# Irreversible Samplers from Jump and Continuous Markov Processes

arXiv: https://arxiv.org/abs/1608.05973 · HTML: https://mlsanity.com/paper/1608.05973/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2016.

## Built on by (most cited first, 5 of 5)

- [Exact Langevin Dynamics with Stochastic Gradients](https://mlsanity.com/paper/2102.01691/) (2021)
- [Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence](https://mlsanity.com/paper/2106.15980/) (2021)
- [On Convergence of Federated Averaging Langevin Dynamics](https://mlsanity.com/paper/2112.05120/) (2021)
- [Accelerated Markov Chain Monte Carlo Algorithms on Discrete States](https://mlsanity.com/paper/2505.12599/) (2025)
- [Preconditioned Discrete-HAMS: A Second-order Irreversible Discrete Sampler](https://mlsanity.com/paper/2507.21982/) (2025)

## Abstract (arXiv)

In this paper, we propose irreversible versions of the Metropolis Hastings (MH) and Metropolis adjusted Langevin algorithm (MALA) with a main focus on the latter. For the former, we show how one can simply switch between different proposal and acceptance distributions upon rejection to obtain an irreversible jump sampler (I-Jump). The resulting algorithm has a simple implementation akin to MH, but with the demonstrated benefits of irreversibility. We then show how the previously proposed MALA method can also be extended to exploit irreversible stochastic dynamics as proposal distributions in the I-Jump sampler. Our experiments explore how irreversibility can increase the efficiency of the samplers in different situations.