# Stochastic Bouncy Particle Sampler

arXiv: https://arxiv.org/abs/1609.00770 · HTML: https://mlsanity.com/paper/1609.00770/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics](https://mlsanity.com/paper/2105.10347/) (2021)
- [Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers](https://mlsanity.com/paper/2406.19051/) (2024)
- [A stepped sampling method for video detection using LSTM](https://mlsanity.com/paper/2107.08471/) (2021)
- [Piecewise Deterministic Markov Processes for Bayesian Neural Networks](https://mlsanity.com/paper/2302.08724/) (2023)

## Abstract (arXiv)

We introduce a novel stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear. The algorithm is based on simulating first arrival times in a doubly stochastic Poisson process using the thinning method, and allows efficient sampling of Bayesian posteriors in big datasets. We prove that in the BPS no bias is introduced by noisy evaluations of the log-likelihood gradient. On the other hand, we argue that efficiency considerations favor a small, controllable bias in the construction of the thinning proposals, in exchange for faster mixing. We introduce a simple regression-based proposal intensity for the thinning method that controls this trade-off. We illustrate the algorithm in several examples in which it outperforms both unbiased, but slowly mixing stochastic versions of BPS, as well as biased stochastic gradient-based samplers.