Sampling algorithms in statistical physics: a guide for statistics and machine learning

TL;DR

This paper reviews various sampling algorithms used in statistical physics, highlighting their connections to statistics and machine learning, and presents new insights and results on advanced methods like event chain Monte Carlo.

Contribution

It provides a comprehensive overview of classical and modern sampling algorithms, emphasizing their interdisciplinary links and introducing recent approaches with practical simulation results.

Findings

Event chain Monte Carlo improves sampling efficiency.

Sampling from the Ising model using statistical tools yields new insights.

Reproducible code demonstrates practical implementation of advanced algorithms.

Abstract

We discuss several algorithms for sampling from unnormalized probability distributions in statistical physics, but using the language of statistics and machine learning. We provide a self-contained introduction to some key ideas and concepts of the field, before discussing three well-known problems: phase transitions in the Ising model, the melting transition on a two-dimensional plane and simulation of an all-atom model for liquid water. We review the classical Metropolis, Glauber and molecular dynamics sampling algorithms before discussing several more recent approaches, including cluster algorithms, novel variations of hybrid Monte Carlo and Langevin dynamics and piece-wise deterministic processes such as event chain Monte Carlo. We highlight cross-over with statistics and machine learning throughout and present some results on event chain Monte Carlo and sampling from the Ising model using tools from the statistics literature. We provide a simulation study on the Ising and XY models, with reproducible code freely available online, and following this we discuss several open areas for interaction between the disciplines that have not yet been explored and suggest avenues for doing so.