Analysis and prediction of the long-run behavior of probabilistic sequential programs with recursion
Analysis and prediction of the long-run behavior of probabilistic sequential programs with recursion
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递归概率顺序程序的长期行为分析和预测
DOI：
10.1109/sfcs.2005.19
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
A. Kucera
中科院分区：
文献类型：
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作者：
T. Brázdil;J. Esparza;A. Kucera
我们介绍了一个家庭的长期运行的平均性能的马尔可夫链的性能和可靠性分析的目的是有用的，并表明这些属性可以有效地检查一个子类的无限状态马尔可夫链产生的概率程序与递归程序。我们还展示了如何预测这些属性通过分析有限前缀的运行，并提出了一个有效的预测算法提到的子类的马尔可夫链。
We introduce a family of long-run average properties of Markov chains that are useful for purposes of performance and reliability analysis, and show that these properties can effectively be checked for a subclass of infinite-state Markov chains generated by probabilistic programs with recursive procedures. We also show how to predict these properties by analyzing finite prefixes of runs, and present an efficient prediction algorithm for the mentioned subclass of Markov chains.