On Performance Measures for Infinite Swapping Monte Carlo Methods

TL;DR

This paper proposes new performance measures for rare-event sampling methods like infinite swapping, helping to evaluate how sampling efficiency varies with different ensemble parameters across various applications.

Contribution

It introduces and demonstrates performance measures applicable to a range of ensemble sampling techniques, including infinite and partial infinite swapping methods.

Findings

Performance measures effectively evaluate sampling efficiency.

Sampling performance varies with ensemble parameters.

Application examples illustrate measure utility.

Abstract

We introduce and illustrate a number of performance measures for rare-event sampling methods. These measures are designed to be of use in a variety of expanded ensemble techniques including parallel tempering as well as infinite and partial infinite swapping approaches. Using a variety of selected applications we address questions concerning the variation of sampling performance with respect to key computational ensemble parameters.