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

## TL;DR

This paper extends Piecewise Deterministic Monte Carlo algorithms to handle parameters constrained to restricted domains, enabling scalable Bayesian inference with sub-sampling.

## Contribution

It introduces a novel implementation of PDMC methods suitable for restricted parameter spaces, broadening their applicability.

## Key findings

- Effective handling of constrained parameters in PDMC algorithms
- Maintains scalability with sub-sampling in restricted domains
- Potential for improved Bayesian inference in constrained settings

## Abstract

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.