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arXiv 2102.07111cond-mat.stat-mech
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随机重置系统的极值统计

Extremal statistics for stochastic resetting systems

Prashant Singh, Arnab Pal

摘要:

虽然平均值和典型波动通常在理解非平衡系统的行为方面发挥重要作用,但这并不总是正确的。在对系统进行彻底分析时,罕见事件和大波动也是关键。在这方面,相对于平均值的极端波动统计数据发挥着重要作用,从统计和数学物理到气候、金融和生态等领域都讨论过这一点。随机重置系统的极值统计在物理、化学、排队论、搜索过程和计算机科学中有着广泛的应用,近年来引起了人们的广泛兴趣。我们对极值的有限和大时间统计进行了详细的分析(最大值和最大值,即,达到最大值的时间)。特别是,我们推导出一个确切的更新公式,涉及的联合分布的最大值和最大的重置过程的统计措施的基础过程。基准我们的结果为最大的复位轨迹,属于Gumbel类的大样本量,我们表明,无论在一个大的观察时间的基本过程中,最大密度达到一个均匀的分布。这是重置机制的更新属性的表现。结果与广泛的马尔可夫和非马尔可夫随机过程复位下,即简单扩散,扩散漂移,Ornstein-Uhlenbeck过程和随机加速过程在一维增强。严格的结果,提出了前两个设置,而后两个支持启发式和数值分析。

英文摘要:

While averages and typical fluctuations often play a major role to understand the behavior of a non-equilibrium system, this nonetheless is not always true. Rare events and large fluctuations are also pivotal when a thorough analysis of the system is being done. In this context, the statistics of extreme fluctuations in contrast to the average plays an important role, as has been discussed in fields ranging from statistical and mathematical physics to climate, finance and ecology. Herein, we study Extreme Value Statistics (EVS) of stochastic resetting systems which have recently gained lot of interests due to its ubiquitous and enriching applications in physics, chemistry, queuing theory, search processes and computer science. We present a detailed analysis for the finite and large time statistics of extremals (maximum and arg-maximum i.e., the time when the maximum is reached) of the spatial displacement in such system. In particular, we derive an exact renewal formula that relates the joint distribution of maximum and arg-maximum of the reset process to the statistical measures of the underlying process. Benchmarking our results for the maximum of a reset-trajectory that pertain to the Gumbel class for large sample size, we show that the arg-maximum density attains to a uniform distribution regardless of the underlying process at a large observation time. This emerges as a manifestation of the renewal property of the resetting mechanism. The results are augmented with a wide spectrum of Markov and non-Markov stochastic processes under resetting namely simple diffusion, diffusion with drift, Ornstein-Uhlenbeck process and random acceleration process in one dimension. Rigorous results are presented for the first two set-ups while the latter two are supported with heuristic and numerical analysis.

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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