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具有随机初始条件的临界生死过程诱导的模型。

Models induced from critical birth-death process with random initial conditions.

作者信息

Tchorbadjieff A, Mayster P

机构信息

Institute of Mathematics and Informatics, Bulgarian Academy of Science, Sofia, Bulgaria.

出版信息

J Appl Stat. 2020 Feb 25;47(13-15):2862-2878. doi: 10.1080/02664763.2020.1732309. eCollection 2020.

Abstract

In this work, we study a linear birth-death process starting from random initial conditions. First, we consider these initial conditions as a random number of particles following different standard probabilistic distributions - Negative-Binomial and its closest Geometric, Poisson or Pólya-Aeppli distributions. It is proved analytically and numerically that in these cases the random number of particles alive at any positive time follows the same probability law like the initial condition, but with different parameters depending on time. The random initial conditions cannot change the critical parameter of branching mechanism, but they impact the extinction probability. Finally, the numerical model is extended to an application for studying branching processes with more complex initial conditions. This is demonstrated with a linear birth-death process initialised with Pólya urn sampling scheme. The obtained preliminary results for particle distribution show close relation to Pólya-Aeppli distribution.

摘要

在这项工作中,我们研究了从随机初始条件开始的线性生死过程。首先,我们将这些初始条件视为遵循不同标准概率分布的随机粒子数——负二项分布及其最接近的几何分布、泊松分布或波利亚 - 埃普利分布。通过解析和数值证明,在这些情况下,任何正时间存活的随机粒子数遵循与初始条件相同的概率定律,但参数随时间变化。随机初始条件不会改变分支机制的临界参数,但会影响灭绝概率。最后,将数值模型扩展到用于研究具有更复杂初始条件的分支过程的应用中。这通过用波利亚瓮抽样方案初始化的线性生死过程进行了演示。所获得的粒子分布初步结果显示与波利亚 - 埃普利分布密切相关。

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