# Bouncy particle sampler with infinite exchanging parallel tempering

arXiv: https://arxiv.org/abs/2509.02003 · HTML: https://mlsanity.com/paper/2509.02003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method](https://mlsanity.com/paper/1510.02451/) (2015)
- [A Discrete Bouncy Particle Sampler](https://mlsanity.com/paper/1707.05200/) (2017)
- [Bayesian Forecasting in Economics and Finance: A Modern Review](https://mlsanity.com/paper/2212.03471/) (2022)
- [Piecewise deterministic generative models](https://mlsanity.com/paper/2407.19448/) (2024)
- [Non-reversibly updating a uniform [0,1] value for Metropolis accept/reject decisions](https://mlsanity.com/paper/2001.11950/) (2020)
- [MCMC using textitbouncy Hamiltonian dynamics: A unifying framework for Hamiltonian Monte Carlo and piecewise deterministic Markov process samplers](https://mlsanity.com/paper/2405.08290/) (2024)
- [Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers](https://mlsanity.com/paper/2406.19051/) (2024)

## Abstract (arXiv)

Markov chain Monte Carlo is one of the most useful tools for obtaining samples from complex distributions, such as posterior distributions in Bayesian inference. Bouncy particle sampler (BPS) is a method that performs sampling using uniform linear motion and elastic collisions. Since BPS does not require time discretization, it is expected to be more advantageous than the widely used Hamiltonian Monte Carlo (HMC) and Langevin Monte Carlo (LMC) for distributions with large curvature changes. To sample more efficiently from multimodal distributions, we propose BPS with parallel tempering (BPS-PT) in the infinite exchange limit, based on LMC-PT with the infinite exchange rate proposed in a previous study. Using a distribution with large curvature changes, BPS-PT showed estimation accuracy comparable to HMC-PT and LMC-PT, and achieved higher ESS per computational time. Therefore, BPS-PT is considered to be effective for distributions with large curvature changes.