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arXiv 1003.0792cond-mat.stat-mech
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马氏二分噪声驱动的广义增长模型的精确解析解

An exact analytical solution for generalized growth models driven by a Markovian dichotomic noise

G. Aquino, M. Bologna, H. Calisto

摘要:

逻辑增长模型在生物学、流行病学、市场模型、神经网络和社交网络中经常出现。它们在包括激光建模在内的许多其他领域都有重要的应用。在许多现实的情况下,增长率经历随机波动,我们考虑一个增长模型的随机增长率建模通过一个非对称马尔可夫二分噪声。我们找到了一个精确的概率分布的解析解,为从生物学到天体物理学和激光物理学的应用提供了一个强大的工具。

英文摘要:

Logistic growth models are recurrent in biology, epidemiology, market models, and neural and social networks. They find important applications in many other fields including laser modelling. In numerous realistic cases the growth rate undergoes stochastic fluctuations and we consider a growth model with a stochastic growth rate modelled via an asymmetric Markovian dichotomic noise. We find an exact analytical solution for the probability distribution providing a powerful tool with applications ranging from biology to astrophysics and laser physics.

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