Rare-Event Sampling: Occupation-Based Performance Measures for Parallel Tempering and Infinite Swapping Monte Carlo Methods
arXiv:1208.6190 · doi:10.1063/1.4765060
Abstract
In the present paper we identify a rigorous property of a number of tempering-based Monte Carlo sampling methods, including parallel tempering as well as partial and infinite swapping. Based on this property we develop a variety of performance measures for such rare-event sampling methods that are broadly applicable, informative, and straightforward to implement. We illustrate the use of these performance measures with a series of applications involving the equilibrium properties of simple Lennard-Jones clusters, applications for which the performance levels of partial and infinite swapping approaches are found to be higher than those of conventional parallel tempering.
18 figures
References in corpus (2)
Cited by in corpus (5)
- On Performance Measures for Infinite Swapping Monte Carlo Methods
- Generalized Parallel Tempering on Bayesian Inverse Problems
- A comparative study of selected parallel tempering methods
- Thermodynamic Integration Methods, Infinite Swapping and the Calculation of Generalized Averages
- A large deviations analysis of certain qualitative properties of parallel tempering and infinite swapping algorithms