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arXiv 2008.12387cond-mat.stat-mechphysics.data-an
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基于扩散参数协方差估计的二维格点上各向异性随机游动跳跃率的推断

Inference of hopping rates of anisotropic random walk on a 2D lattice via covariance-based estimators of diffusion parameters

Masanori Mishima

摘要:

传统上,均方位移的时间发展已被用来确定单个颗粒的轨迹的扩散系数。然而,该方法对图像获取时的噪声和运动模糊敏感。最近,Vestergaard等人提出了一种基于位移序列之间协方差的新方法。该方法给出了无偏的一维扩散的扩散系数的更鲁棒的估计,即,当平均速度为零时。在这里,我们将这种方法扩展到二维晶格上的潜在有偏随机游走。首先,我们描述的跳跃率的八个相邻的网站和随机二维位移的高阶矩的时间发展之间的关系。然后,我们推导出这些高阶矩的基于协方差的估计。数值模拟证实,这里提出的程序允许从二维轨迹数据的位置误差和运动模糊的随机跳跃率的推断。

英文摘要:

Traditionally, time-development of the mean square displacement has been employed to determine the diffusion coefficient from the trajectories of single particles. However, this approach is sensitive to the noise and the motion blur upon image acquisition. Recently, Vestergaard et al. has proposed a novel method based on the covariance between the shifted displacement series. This approach gives a more robust estimator of the diffusion coefficient of one-dimensional diffusion without bias, i.e., when mean velocity is zero. Here, we extend this approach to a potentially biased random walk on a two-dimensional lattice. First, we describe the relationship between the hopping rates to the eight adjacent sites and the time development of the higher-order moments of the stochastic two-dimensional displacements. Then, we derive the covariance-based estimators for these higher-order moments. Numerical simulations confirmed that the procedure presented here allows inference of the stochastic hopping rates from two-dimensional trajectory data with location error and motion blur.

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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