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In the present paper we identify a rigorous property of a number oftempering-based Monte Carlo sampling methods, including parallel tempering aswell as partial and infinite swapping. Based on this property we develop avariety of performance measures for such rare-event sampling methods that arebroadly applicable, informative, and straightforward to implement. Weillustrate the use of these performance measures with a series of applicationsinvolving the equilibrium properties of simple Lennard-Jones clusters,applications for which the performance levels of partial and infinite swappingapproaches are found to be higher than those of conventional paralleltempering.

Rare-Event Sampling: Occupation-Based Performance Measures for Parallel Tempering and Infinite Swapping Monte Carlo Methods