zeus：用于高效贝叶斯参数推断的Encourage切片采样的Python实现
zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference
摘要：
我们将介绍zeus，这是一个经过良好测试的Python实现，用于贝叶斯参数推断的Entrance Slice Sampling（ESS）方法。ESS是一种新颖的马尔可夫链蒙特卡罗（MCMC）算法，专门设计用于解决现代天文学和宇宙学分析所带来的计算挑战。特别是，该方法只需要对1-2个通常微不足道的超参数进行最小的手动调整;其性能对线性相关性不敏感，并且可以扩展到1000个CPU而无需任何额外的努力。此外，它的局部自适应特性允许即使在存在强非线性相关时也能有效地采样。最后，该方法实现了高性能，即使在强多峰分布在高维。与流行的MCMC采样器emcee相比，zeus在宇宙学和系外行星应用中的表现分别好9倍和29倍。
英文摘要：
We introduce zeus, a well-tested Python implementation of the Ensemble Slice Sampling (ESS) method for Bayesian parameter inference. ESS is a novel Markov chain Monte Carlo (MCMC) algorithm specifically designed to tackle the computational challenges posed by modern astronomical and cosmological analyses. In particular, the method requires only minimal hand--tuning of 1-2 hyper-parameters that are often trivial to set; its performance is insensitive to linear correlations and it can scale up to 1000s of CPUs without any extra effort. Furthermore, its locally adaptive nature allows to sample efficiently even when strong non-linear correlations are present. Lastly, the method achieves a high performance even in strongly multimodal distributions in high dimensions. Compared to emcee, a popular MCMC sampler, zeus performs 9 and 29 times better in a cosmological and an exoplanet application respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
