Closing the Evidence Gap: reddemcee, a Fast Adaptive Parallel Tempering Sampler
arXiv:2509.24870 · doi:10.1051/0004-6361/202556609
Abstract
Markov Chain Monte Carlo (MCMC) excels at sampling complex posteriors but traditionally lags behind nested sampling in accurate evidence estimation, which is crucial for model comparison in astrophysical problems. We introduce reddemcee, an Adaptive Parallel Tempering Ensemble Sampler, aiming to close this gap by simultaneously presenting next-generation automated temperature-ladder adaptation techniques and robust, low-bias evidence estimators. reddemcee couples an affine-invariant stretch move with five interchangeable ladder-adaptation objectives, Uniform Swap Acceptance Rate, Swap Mean Distance, Gaussian-Area Overlap, Small Gaussian Gap, and Equalised Thermodynamic Length, implemented through a common differential update rule. Three evidence estimators are provided: Curvature-aware Thermodynamic Integration (TI+), Geometric-Bridge Stepping Stones (SS+), and a novel Hybrid algorithm that blends both approaches (H+). Performance and accuracy are benchmarked on n-dimensional Gaussian Shells, Gaussian Egg-box, Rosenbrock Functions, and exoplanet radial-velocity time-series of HD 20794. Across Shells up to 15 dimensions, reddemcee presents roughly 7 times the effective sampling speed of the best dynamic nested sampling configuration. The TI+, SS+ and H+ estimators recover estimates under 3 percent error and supply realistic uncertainties with as few as six temperatures. In the HD 20794 case study, reddemcee reproduces literature model rankings and yields tighter yet consistent planetary parameters compared with dynesty, with evidence errors that track run-to-run dispersion. By unifying fast ladder adaptation with reliable evidence estimators, reddemcee delivers strong throughput and accurate evidence estimates, often matching, and occasionally surpassing, dynamic nested sampling, while preserving the rich posterior information which makes MCMC indispensable for modern Bayesian inference.
v2: Revised after referee comments; resubmitted to A&A on 24 Sep 2025
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