主持人：MCMC锤子
emcee: The MCMC Hammer
摘要：
我们介绍了一个稳定的，经过良好测试的Python实现的仿射不变集成采样器的马尔可夫链蒙特卡罗（MCMC）提出的古德曼和Weare（2010）。该代码是开源的，已经在天体物理学文献中的几个已发表的项目中使用。与传统的MCMC采样方法相比，emcee背后的算法具有几个优点，并且通过自相关时间（或每个独立样本的函数调用）来衡量，它具有出色的性能。该算法的一个主要优点是，与N维参数空间中的传统算法的$\sim N^2$相比，它只需要手动调整1或2个参数。在本文档中，我们描述了算法以及我们的实现和API的细节。利用集成方法的并行性，emcee允许任何用户利用多个CPU内核，而无需额外的努力。该代码可以在http://dan.iel.fm/emcee上根据MIT许可证在线获得。
英文摘要：
We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to $\sim N^2$ for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation and API. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the MIT License.
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。
