具有退缩的随机匹配模型的完美采样
Perfect sampling of stochastic matching models with reneging
AI总结：
本文提出了一种改进的DCFTP算法，用于高效采样具有有限耐心的随机匹配模型，通过队列控制和特殊技术解决稳定性问题，并用于估计系统损失概率。
摘要：
在本文中，我们介绍了一种Kendall提出的主导耦合从过去算法（DCFTP）的微小变种，用于有界马尔可夫链。该算法基于通过一个（通常单调的）递归控制一个（通常非单调的）随机递归。我们证明该算法特别适用于具有有限耐心的随机匹配模型，这类模型通常无法用闭合形式表达其稳态分布。我们首先证明该模型的马尔可夫链可通过无限服务器队列轻松控制。然后研究了耐心时间确定性的情况，此时该控制论证可能失效。在这种情况下，我们采用一种专门技术，也可视为通过到达序列的控制。我们还比较了该算法与经典CFTP算法，并展示了如何利用我们的完美模拟结果来估计和比较各种平衡系统中的损失概率。
英文摘要：
In this paper, we introduce a slight variation of the Dominated Coupling From the Past algorithm (DCFTP) of Kendall, for bounded Markov chains. It is based on the control of a (typically non-monotonic) stochastic recursion by a (typically monotonic) one. We show that this algorithm is particularly suitable for stochastic matching models with bounded patience, a class of models for which the steady state distribution of the system is in general unknown in closed form. We first show that the Markov chain of this model can be easily controlled by an infinite-server queue. We then investigate the particular case where patience times are deterministic, and this control argument may fail. in that case we resort to an ad-hoc technique that can also be seen as a control (this time, by the arrival sequence). We then compare this algorithm to the classical CFTP one, and show how our perfect simulation results can be used to estimate, and compare, the loss probabilities of various systems in equilibrium.
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。
