如何使用只读随机源从过去进行耦合
How to Couple from the Past Using a Read-Once Source of Randomness
摘要：
本文给出了一种由马尔可夫链的平稳分布产生完全随机样本的新方法。该方法与来自过去的耦合（CFTP）有关，但仅在时间上向前运行马尔可夫链，并且从不在过去的先前时间重新启动它。该方法也与运筹学文献中称为PASTA（泊松到达时间平均值）的思想有关。因为新算法可以使用只读随机流运行，我们称之为只读CFTP。只读CFTP的内存和时间要求与CFTP的通常形式的要求相当，对于各种应用程序的要求可能会显着less.Some完美的采样算法点过程是基于一个扩展的CFTP称为耦合到过去和从过去，为了完整性，我们给一个只读版本的耦合到过去和从过去，但它仍然是不切实际的。对于这些点过程的应用，我们给出了一个替代的耦合方法，只读CFTP可以有效地使用。
英文摘要：
We give a new method for generating perfectly random samples from the stationary distribution of a Markov chain. The method is related to coupling from the past (CFTP), but only runs the Markov chain forwards in time, and never restarts it at previous times in the past. The method is also related to an idea known as PASTA (Poisson arrivals see time averages) in the operations research literature. Because the new algorithm can be run using a read-once stream of randomness, we call it read-once CFTP. The memory and time requirements of read-once CFTP are on par with the requirements of the usual form of CFTP, and for a variety of applications the requirements may be noticeably less. Some perfect sampling algorithms for point processes are based on an extension of CFTP known as coupling into and from the past; for completeness, we give a read-once version of coupling into and from the past, but it remains unpractical. For these point process applications, we give an alternative coupling method with which read-once CFTP may be efficiently used.
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