随机性回收器：完美抽样的新技术
The Randomness Recycler: A new technique for perfect sampling
摘要：
对于许多感兴趣的概率分布，有效地获取样本是相当困难的。通常，马尔可夫链用于从这些分布中获得近似随机样本。传统的马尔可夫链方法的主要缺点是链的混合时间通常是未知的，这使得不可能确定输出样本与具有目标分布的接近程度。在这里，我们提出了一个新的协议，随机循环（RR），克服了这个困难。与经典的马尔可夫链方法不同，基于RR的算法创建从所需分布中精确抽取的样本。其他完美的抽样方法，如耦合从过去使用现有的马尔可夫链，但RR没有使用传统的马尔可夫链在所有。虽然不是普遍有用的，但RR确实适用于各种各样的问题。在某些问题的有限情况下，给出了产生样本的第一期望线性时间算法。在这里，我们将RR应用于自组织列表、伊辛模型、随机独立集、随机着色和随机聚类模型。
英文摘要：
For many probability distributions of interest, it is quite difficult to obtain samples efficiently. Often, Markov chains are employed to obtain approximately random samples from these distributions. The primary drawback to traditional Markov chain methods is that the mixing time of the chain is usually unknown, which makes it impossible to determine how close the output samples are to having the target distribution. Here we present a new protocol, the randomness recycler (RR), that overcomes this difficulty. Unlike classical Markov chain approaches, an RR-based algorithm creates samples drawn exactly from the desired distribution. Other perfect sampling methods such as coupling from the past use existing Markov chains, but RR does not use the traditional Markov chain at all. While by no means universally useful, RR does apply to a wide variety of problems. In restricted instances of certain problems, it gives the first expected linear time algorithms for generating samples. Here we apply RR to self-organizing lists, the Ising model, random independent sets, random colorings, and the random cluster model.
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。
