Dynamic temperature selection for parallel-tempering in Markov chain Monte Carlo simulations

Modern problems in astronomical Bayesian inference require efficient methods for sampling from complex, high-dimensional, often multi-modal probability distributions. Most popular methods, such as Markov chain Monte Carlo sampling, perform poorly on strongly multi-modal probability distributions, rarely jumping between modes or settling on just one mode without finding others. Parallel tempering addresses this problem by sampling simultaneously with separate Markov chains from tempered versions of the target distribution with reduced contrast levels. Gaps between modes can be traversed at higher temperatures, while individual modes can be efficiently explored at lower temperatures. In this paper, we investigate how one might choose the ladder of temperatures to achieve lower autocorrelation time for the sampler (and therefore more efficient sampling). In particular, we present a simple, easily-implemented algorithm for dynamically adapting the temperature configuration of a sampler while sampling in order to maximise its efficiency. This algorithm dynamically adjusts the temperature spacing to achieve a uniform rate of exchanges between neighbouring temperatures. We compare the algorithm to conventional geometric temperature configurations on a number of test distributions, and report efficiency gains by a factor of 1.2--2.5 over a well-chosen geometric temperature configuration and by a factor of 1.5--5 over a poorly chosen configuration. On all of these test distributions a sampler using the dynamical adaptations to achieve uniform acceptance ratios between neighbouring chains outperforms one that does not.