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arXiv 0906.0153math.PR
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随机关系和马尔可夫耦合的计算方法

Computational methods for stochastic relations and Markovian couplings

Lasse Leskelä

摘要:

保序耦合是一种很好的工具,可以用来获得马尔可夫过程的时变和平稳分布的鲁棒估计,这些马尔可夫过程太复杂而无法精确分析。本文的出发点是研究随机关系,它可以看作是随机序的自然推广。这种推广的动机是观察到,对于两个马尔可夫过程的随机排序,它足以使过程的生成器保持一些,不一定是自反或传递的顺序关系的子关系。本文的主要贡献是有限空间之间的随机关系的算法表征,和截断方法比较无限状态马尔可夫过程。该方法示出了应用程序的损失网络和并行队列。

英文摘要:

Order-preserving couplings are elegant tools for obtaining robust estimates of the time-dependent and stationary distributions of Markov processes that are too complex to be analyzed exactly. The starting point of this paper is to study stochastic relations, which may be viewed as natural generalizations of stochastic orders. This generalization is motivated by the observation that for the stochastic ordering of two Markov processes, it suffices that the generators of the processes preserve some, not necessarily reflexive or transitive, subrelation of the order relation. The main contributions of the paper are an algorithmic characterization of stochastic relations between finite spaces, and a truncation approach for comparing infinite-state Markov processes. The methods are illustrated with applications to loss networks and parallel queues.

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。

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