# Forward Event-Chain Monte Carlo: Fast sampling by randomness control in irreversible Markov chains

## TL;DR

This paper introduces a new class of Event-Chain Monte Carlo methods that minimizes additional randomization, leading to significantly faster sampling and better scalability compared to traditional PDMC and Monte Carlo techniques.

## Contribution

The paper proposes a novel Event-Chain Monte Carlo approach that reduces the need for extra randomization, enhancing efficiency and scalability in sampling from statistical models.

## Key findings

- Achieves up to several magnitudes acceleration in sampling speed
- Demonstrates improved dimensional scaling performance
- Reduces the amount of randomization needed for correctness

## Abstract

Irreversible and rejection-free Monte Carlo methods, recently developed in Physics under the name Event-Chain and known in Statistics as Piecewise Deterministic Monte Carlo (PDMC), have proven to produce clear acceleration over standard Monte Carlo methods, thanks to the reduction of their random-walk behavior. However, while applying such schemes to standard statistical models, one generally needs to introduce an additional randomization for sake of correctness. We propose here a new class of Event-Chain Monte Carlo methods that reduces this extra-randomization to a bare minimum. We compare the efficiency of this new methodology to standard PDMC and Monte Carlo methods. Accelerations up to several magnitudes and reduced dimensional scalings are exhibited.