论文标题
集合切片采样：相关和多模式分布的平行，黑框和无梯度推断
Ensemble Slice Sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions
论文作者
论文摘要
切片采样已成为一种强大的马尔可夫链蒙特卡洛算法，该算法适应目标分布的特征，并以最小的手动调整。但是，Slice采样的性能对用户指定的初始长度尺度高参数高度敏感，并且该方法通常以缩放率较差或相关的分布而挣扎。本文介绍了集合切片采样（ESS），这是一种新的一类算法，通过适应性调整初始长度尺度并利用平行步行者的集合来绕过此类困难，以便有效地处理参数之间的强相关性。这些仿射不变算法是微不足道的，不需要手工调整，并且可以在并行的计算环境中轻松实现。经验测试表明，与传统的MCMC方法相比，集合切片采样可以提高效率超过一个数量级。在强烈多模式分布的情况下，即使在高维度中，集合切片采样也可以有效地采样。我们认为，该方法的平行，黑盒和无梯度的性质使其非常适合在科学领域（例如物理，天体物理学和宇宙学）使用，这些科学领域以各种计算昂贵且非不同的模型为主导。
Slice Sampling has emerged as a powerful Markov Chain Monte Carlo algorithm that adapts to the characteristics of the target distribution with minimal hand-tuning. However, Slice Sampling's performance is highly sensitive to the user-specified initial length scale hyperparameter and the method generally struggles with poorly scaled or strongly correlated distributions. This paper introduces Ensemble Slice Sampling (ESS), a new class of algorithms that bypasses such difficulties by adaptively tuning the initial length scale and utilising an ensemble of parallel walkers in order to efficiently handle strong correlations between parameters. These affine-invariant algorithms are trivial to construct, require no hand-tuning, and can easily be implemented in parallel computing environments. Empirical tests show that Ensemble Slice Sampling can improve efficiency by more than an order of magnitude compared to conventional MCMC methods on a broad range of highly correlated target distributions. In cases of strongly multimodal target distributions, Ensemble Slice Sampling can sample efficiently even in high dimensions. We argue that the parallel, black-box and gradient-free nature of the method renders it ideal for use in scientific fields such as physics, astrophysics and cosmology which are dominated by a wide variety of computationally expensive and non-differentiable models.
