arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.02293cond-mat.stat-mechmath-phmath.MPmath.PR
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${1D}$格上随机游动和Lévy飞行的记录数统计

Statistics of the Number of Records for Random Walks and Lévy Flights on a ${1D}$ Lattice

Philippe Mounaix, Satya N. Majumdar, Gregory Schehr

摘要:

研究了$1D$格上对称的$n$ -步离散跳过程记录数$R_n$的统计性质.在一个给定的步骤,步行者可以跳跃任意格子单位从一个给定的对称概率分布。这个过程包括,作为一个特殊情况,标准的最近邻格随机游走。我们明确推导出的生成函数的分布$P(R_n)$的记录数,适用于任意离散跳跃分布。作为一个副产品,我们提供了一个相对简单的证明广义Sparre Andersen定理的生存概率的随机游走在一条线上,离散或连续的跳跃分布。对于离散跳跃过程,我们推导出了$P(R_n)$和平均记录数$E(R_n)$的渐近大$n$行为。我们证明了,与具有对称和连续跳跃分布的随机游动不同,在这种情况下,记录统计量是强普适的(即,独立于所有$n$的跳跃分布),晶格游走的记录统计取决于任何固定$n$的跳跃分布。然而,在大的$n$限制,我们表明,分布的缩放记录数$R_n/E(R_n)$接近一个普遍的,半高斯形式的任何离散跳跃过程。对跳跃分布的依赖性仅通过比例因子$E(R_n)$进入,对于任意跳跃分布,我们也在大的$n$极限中计算该比例因子。我们提出了明确的几个例子的结果,并提供我们的分析预测的数值检查。

英文摘要:

We study the statistics of the number of records $R_n$ for a symmetric, $n$-step, discrete jump process on a $1D$ lattice. At a given step, the walker can jump by arbitrary lattice units drawn from a given symmetric probability distribution. This process includes, as a special case, the standard nearest neighbor lattice random walk. We derive explicitly the generating function of the distribution $P(R_n)$ of the number of records, valid for arbitrary discrete jump distributions. As a byproduct, we provide a relatively simple proof of the generalized Sparre Andersen theorem for the survival probability of a random walk on a line, with discrete or continuous jump distributions. For the discrete jump process, we then derive the asymptotic large $n$ behavior of $P(R_n)$ as well as of the average number of records $E(R_n)$. We show that unlike the case of random walks with symmetric and continuous jump distributions where the record statistics is strongly universal (i.e., independent of the jump distribution for all $n$), the record statistics for lattice walks depends on the jump distribution for any fixed $n$. However, in the large $n$ limit, we show that the distribution of the scaled record number $R_n/E(R_n)$ approaches a universal, half-Gaussian form for any discrete jump process. The dependence on the jump distribution enters only through the scale factor $E(R_n)$, which we also compute in the large $n$ limit for arbitrary jump distributions. We present explicit results for a few examples and provide numerical checks of our analytical predictions.

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