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arXiv 2007.14220math.PRstat.CO
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平稳高斯过程联合漂移时间的有效计算

Effective computations of joint excursion times for stationary Gaussian processes

Georg Lindgren, Krzysztof Podgorski, Igor Rychlik

摘要:

本文推广了用推广的多元Rice公式计算高斯过程漂移时间分布的方法。该方法被用于数值实现的高维集成例程,并在早期的工作中,它被证明是更有效的计算,从而更精确的比那些基于赖斯展开。偏移时间的联合分布与水平交叉点数量的分布有关,这是一个可以通过基于交叉点数量的矩的赖斯级数展开来解决的问题。另一个攻击点是“独立区间近似”,它被深入研究以获得物理系统的持久性。它将连续交叉间隔的长度视为统计独立的。更新类型的参数导致通过其拉普拉斯变换提供近似区间分布的表达式。然而,独立性在典型情况下是无效的。即使它导致持久性指数的可接受的结果,近似误差的严格评估是不可用的。此外,我们表明,IIA方法不能提供正确定义的概率分布,因此该方法仅限于持久性研究。本文提出了一种替代方法,既更一般,更准确,相对未知。它是基于一个和两个连续的偏移长度的概率密度的精确表达式。数值例程RIND使用科学计算的最新进展计算密度,并且可以通过简单的Matlab接口轻松访问。这一结果解决了一般平稳可微高斯过程的两步漂移依赖性问题。这项工作还提供了一些分析结果,解释了实施方法的有效性。

英文摘要:

This work is to popularize the method of computing the distribution of the excursion times for a Gaussian process that involves extended and multivariate Rice's formula. The approach was used in numerical implementations of the high-dimensional integration routine and in earlier work it was shown that the computations are more effective and thus more precise than those based on Rice expansions. The joint distribution of excursion times is related to the distribution of the number of level crossings, a problem that can be attacked via the Rice series expansion, based on the moments of the number of crossings. Another point of attack is the "Independent Interval Approximation" intensively studied for the persistence of physical systems. It treats the lengths of successive crossing intervals as statistically independent. A renewal type argument leads to an expression that provides the approximate interval distribution via its Laplace transform. However, independence is not valid in typical situations. Even if it leads to acceptable results for the persistency exponent, rigorous assessment of the approximation error is not available. Moreover, we show that the IIA approach cannot deliver properly defined probability distributions and thus the method is limited to persistence studies. This paper presents an alternative approach that is both more general, more accurate, and relatively unknown. It is based on exact expressions for the probability density for one and for two successive excursion lengths. The numerical routine RIND computes the densities using recent advances in scientific computing and is easily accessible via a simple Matlab interface. The result solves the problem of two-step excursion dependence for a general stationary differentiable Gaussian process. The work offers also some analytical results that explain the effectiveness of the implemented method.

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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