# Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains

arXiv: https://arxiv.org/abs/1701.04244 · HTML: https://mlsanity.com/paper/1701.04244/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2017.

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

- [Piecewise deterministic generative models](https://mlsanity.com/paper/2407.19448/) (2024)
- [Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes](https://mlsanity.com/paper/2202.09129/) (2022)
- [The Application of Zig-Zag Sampler in Sequential Markov Chain Monte Carlo](https://mlsanity.com/paper/2111.10210/) (2021)
- [Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densities](https://mlsanity.com/paper/2504.18210/) (2025)

## Abstract (arXiv)

Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how they can be implemented in settings where the parameters live on a restricted domain.