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arXiv 1102.2922math.PRcs.NAmath.NAq-bio.MN
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种群过程随机模型数值方法的弱误差分析

Weak error analysis of numerical methods for stochastic models of population processes

University of Wisconsin, Madison(威斯康星大学麦迪逊分校)

David F. Anderson, Masanori Koyama

AI总结:

针对种群过程(包括生物化学和细胞生物学)的连续时间马尔可夫链模型,提出一个通用框架来分析不同数值近似方法引起的弱误差(偏差),考虑系统自然缩放和步长,并通过实例展示计算复杂度的降低。

摘要:

最简单且最常见的种群过程随机模型(包括生物化学和细胞生物学中的模型)是连续时间马尔可夫链。这类模型的模拟通常相对直接,因为存在易于实现的方法来生成精确样本路径。然而,当使用系综平均来近似期望值时,计算复杂度可能变得过高,因为每条路径的计算次数与过程的跳跃次数成线性关系。当这类方法在计算上变得棘手时,引入偏差的近似方法可能变得有利。在本文中,我们提供了一个通用框架,用于理解当前设置中不同数值近似技术引起的弱误差或偏差。该分析考虑了给定系统内的自然缩放以及数值方法的步长。提供了示例来展示主要分析结果以及近似方法所实现的计算复杂度降低。

英文摘要:

The simplest, and most common, stochastic model for population processes, including those from biochemistry and cell biology, are continuous time Markov chains. Simulation of such models is often relatively straightforward as there are easily implementable methods for the generation of exact sample paths. However, when using ensemble averages to approximate expected values, the computational complexity can become prohibitive as the number of computations per path scales linearly with the number of jumps of the process. When such methods become computationally intractable, approximate methods, which introduce a bias, can become advantageous. In this paper, we provide a general framework for understanding the weak error, or bias, induced by different numerical approximation techniques in the current setting. The analysis takes into account both the natural scalings within a given system and the step-size of the numerical method. Examples are provided to demonstrate the main analytical results as well as the reduction in computational complexity achieved by the approximate methods.

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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