论文标题
无限的切片采样
Unbounded Slice Sampling
论文作者
论文摘要
切片采样是一种有效的马尔可夫链蒙特卡洛算法，可从不正常的密度中采样，并且始终$ 1 $。但是，当采样的变量无限制时，它的“逐步淘汰”启发式作品仅在本地，因此很难统一探索可能的候选人。本文提出了一种简单的变化方法，以切成示例无界变量的样品，等效地等效于[0,1）。
Slice sampling is an efficient Markov Chain Monte Carlo algorithm to sample from an unnormalized density with acceptance ratio always $1$. However, when the variable to sample is unbounded, its "stepping-out" heuristic works only locally, making it difficult to uniformly explore possible candidates. This paper proposes a simple change-of-variable method to slice sample an unbounded variable equivalently from [0,1).
