# A fresh take on 'Barker dynamics' for MCMC

arXiv: https://arxiv.org/abs/2012.09731 · HTML: https://mlsanity.com/paper/2012.09731/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Optimal Scaling for Locally Balanced Proposals in Discrete Spaces](https://mlsanity.com/paper/2209.08183/) (2022)
- [Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms](https://mlsanity.com/paper/2201.01123/) (2022)
- [Improving multiple-try Metropolis with local balancing](https://mlsanity.com/paper/2211.11613/) (2022)
- [Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy](https://mlsanity.com/paper/2304.14762/) (2023)
- [Robust Approximate Sampling via Stochastic Gradient Barker Dynamics](https://mlsanity.com/paper/2405.08999/) (2024)
- [On skew-symmetric distributions and their use in Monte Carlo sampling algorithms: coordinate-free, Gibbs-style and manifold versions of the Barker proposal](https://mlsanity.com/paper/2610.01448/) (2026)

## Abstract (arXiv)

We study a recently introduced gradient-based Markov chain Monte Carlo method based on 'Barker dynamics'. We provide a full derivation of the method from first principles, placing it within a wider class of continuous-time Markov jump processes. We then evaluate the Barker approach numerically on a challenging ill-conditioned logistic regression example with imbalanced data, showing in particular that the algorithm is remarkably robust to irregularity (in this case a high degree of skew) in the target distribution.