Transition behavior of the waiting time distribution in a stochastic model with the internal state.

IF 2.3 4区 数学 Q2 BIOLOGY
Zhe Xue, Yuan Zhang, Zhennan Zhou, Min Tang
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引用次数: 0

Abstract

It has been noticed that when the waiting time distribution exhibits a transition from an intermediate time power-law decay to a long-time exponential decay in the continuous time random walk model, a transition from anomalous diffusion to normal diffusion can be observed at the population level. However, the mechanism behind the transition of waiting time distribution is rarely studied. In this paper, we provide one possible mechanism to explain the origin of such a transition. A stochastic model terminated by a state-dependent Poisson clock is studied by a formal asymptotic analysis for the time evolutionary equation of its probability density function (PDF). The waiting time behavior under a more relaxed setting can be rigorously characterized by probability tools. Both approaches show the transition phenomenon of the waiting time T, which is complemented by particle simulations to shed light on the transition time scale. Our results indicate that small drift relative to noise in the state equation and a stiff response in the Poisson rate are crucial to the transitional phenomena.

具有内部状态的随机模型中等待时间分布的过渡行为。
在连续时间随机漫步模型中,当等待时间分布呈现从中间时间幂律衰减到长时间指数衰减的转变时,在总体水平上可以观察到从异常扩散到正常扩散的转变。然而,等待时间分布转变背后的机制却鲜有研究。在本文中,我们提供了一种可能的机制来解释这种转变的起源。本文研究了一个由状态相关泊松时钟终止的随机模型,并对其概率密度函数(PDF)的时间演化方程进行了形式渐近分析。在更宽松的条件下,等待时间行为可以用概率工具严格表征。两种方法都显示了等待时间T的跃迁现象,并辅以粒子模拟来阐明跃迁时间尺度。我们的研究结果表明,状态方程中相对于噪声的小漂移和泊松率的硬响应是过渡现象的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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