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.

翻译：马尔可夫链蒙特卡罗（MCMC）在采样复杂后验分布方面表现出色，但在精确证据估计方面传统上落后于嵌套采样，而证据估计对于天体物理问题中的模型比较至关重要。我们推出reddemcee——一种自适应并行回火集成采样器，旨在通过同时引入下一代自动化温度阶梯自适应技术与稳健、低偏差的证据估计器来填补这一鸿沟。reddemcee将仿射不变拉伸移动与五种可互换的阶梯自适应目标（均匀交换接受率、交换平均距离、高斯区域重叠、小高斯间隙和均衡热力学长度）相结合，通过统一的微分更新规则实现。该工具提供三种证据估计器：曲率感知热力学积分（TI+）、几何桥接踏脚石法（SS+）以及融合两种方法的新型混合算法（H+）。我们在n维高斯壳层、高斯蛋盒函数、Rosenbrock函数和HD 20794系外行星径向速度时间序列上对性能与精度进行了基准测试。在高达15维的壳层测试中，reddemcee的有效采样速度约为最佳动态嵌套采样配置的7倍。TI+、SS+和H+估计器在误差低于3%的条件下恢复估计值，并仅用六个温度即可提供真实的误差范围。在HD 20794案例研究中，reddemcee复现了文献中的模型排序，相比dynesty获得了更紧凑但仍一致的星体参数，其证据误差与多次运行离散度相符。通过将快速阶梯自适应与可靠证据估计器相结合，reddemcee实现了强大的处理能力和精确的证据估计，其表现常与动态嵌套采样相当，有时甚至更优，同时保留了使MCMC成为现代贝叶斯推断不可或缺工具的丰富后验信息。