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分数布朗运动大偏差的几何光学

Geometrical optics of large deviations of fractional Brownian motion.

作者信息

Meerson Baruch, Oshanin Gleb

机构信息

Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem 91904, Israel.

Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée (UMR CNRS 7600), 4 Place Jussieu, 75252 Paris Cedex 05, France.

出版信息

Phys Rev E. 2022 Jun;105(6-1):064137. doi: 10.1103/PhysRevE.105.064137.

Abstract

It has been shown recently that the optimal fluctuation method-essentially geometrical optics-provides a valuable insight into large deviations of Brownian motion. Here we extend the geometrical optics formalism to two-sided, -∞<t<∞, fractional Brownian motion (fBm) on the line, which is "pushed" to a large deviation regime by imposed constraints. We test the formalism on three examples where exact solutions are available: the two- and three-point probability distributions of the fBm and the distribution of the area under the fBm on a specified time interval. Then we apply the formalism to several previously unsolved problems by evaluating large-deviation tails of the following distributions: (i) of the first-passage time, (ii) of the maximum of, and (iii) of the area under, fractional Brownian bridge and fractional Brownian excursion, and (iv) of the first-passage area distribution of the fBm. An intrinsic part of a geometrical optics calculation is determination of the optimal path-the most likely realization of the process which dominates the probability distribution of the conditioned process. Due to the non-Markovian nature of the fBm, the optimal paths of a fBm, subject to constraints on a finite interval 0<t≤T, involve both the past -∞<t<0 and the future T<t<∞.

摘要

最近的研究表明,最优涨落方法——本质上是几何光学——为布朗运动的大偏差提供了有价值的见解。在此,我们将几何光学形式主义扩展到直线上的双边(-∞<t<∞)分数布朗运动(fBm),它通过施加约束被“推向”大偏差 regime。我们在三个有精确解的例子上检验该形式主义:fBm的两点和三点概率分布以及fBm在指定时间间隔下的面积分布。然后,我们通过评估以下分布的大偏差尾部,将该形式主义应用于几个先前未解决的问题:(i)首次通过时间的分布,(ii)分数布朗桥和分数布朗游程的最大值的分布,(iii)分数布朗桥和分数布朗游程下方的面积的分布,以及(iv)fBm的首次通过面积分布。几何光学计算中一个内在的部分是确定最优路径——过程最可能的实现,它主导了条件过程的概率分布。由于fBm的非马尔可夫性质,在有限区间0<t≤T上受约束的fBm的最优路径涉及过去(-∞<t<0)和未来(T<t<∞)。

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