arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1004.5565math.PRmath.DS
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具有无穷均值的标度型马氏更新过程的扰动方法

Perturbation approach to scaled type Markov renewal processes with infinite mean

Zsolt Pajor-Gyulai, Domokos Szász

摘要:

标度型马尔可夫更新过程是经典更新过程的推广:更新时间来自单参数概率律族,参数序列是遍历马尔可夫链的轨迹.我们主要关心的是马尔可夫参数在时间t \to \infty的渐近分布。当然,极限取决于马尔可夫链的平稳分布。然而,结果是本质上不同的取决于是否更新的期望是有限的或无限的。如果期望值是一致有界的,那么我们可以给出一般的极限(超出标度型过程的范围),其中所讨论的概率律的期望值也会出现。如果均值是无限的,那么--通过假设更新时间是指数为0 \leq alpha \leq 1的规则变化概率定律的重新标度版本--在极限中出现的是指数a。

英文摘要:

Scaled type Markov renewal processes generalize classical renewal processes: renewal times come from a one parameter family of probability laws and the sequence of the parameters is the trajectory of an ergodic Markov chain. Our primary interest here is the asymptotic distribution of the Markovian parameter at time t \to \infty. The limit, of course, depends on the stationary distribution of the Markov chain. The results, however, are essentially different depending on whether the expectations of the renewals are finite or infinite. If the expectations are uniformly bounded, then we can provide the limit in general (beyond the class of scaled type processes), where the expectations of the probability laws in question appear, too. If the means are infinite, then - by assuming that the renewal times are rescaled versions of a regularly varying probability law with exponent 0 \leq alpha \leq 1 - it is the exponent a which emerges in the limits.

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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