There was no interaction of multiplicative model among factors by interaction analysis.
未发现因素之间有相乘模型的交互作用。
Based on generalized run-off triangle, we introduce multiplicative model and its marginal-sum estimation in chapter 2. We get that the marginal-sum estimation is the chain ladder estimation.
基于一般的流量三角形，我们在第二章中提出了乘法模型及边际和估计，并证明了在该模型下，参数的边际和估计就是链梯法估计。
Examples on volume equation of multiplicative regression model are illustrated.
例举了乘法回归模型的材积方程的应用实例。
The effect of correlation between additive and multiplicative noises on cubic model of a single mode laser is discussed.
从理论上对加性白噪音和乘性色噪音之间存在关联作用的单模激光立方模型进行了分析。
The dynamical property of a logistic growth model is investigated when the colored coupling between multiplicative colored noise and additive white noise included.
研究了乘性色噪声和加性白噪声之间的耦合为色噪声时虫口模型的动力学行为。
This paper studies the optimum detection model of the multiplicative spread-spectrum watermark embedded by fixed strength.
对以固定强度嵌入的乘性扩频水印的最优检测模型进行了研究。
A model for the removal of multiplicative noises was proposed based on the non-local diffusion method.
提出了一种基于非局部扩散的去除乘性噪声的模型。
We apply a Langevin model by imposing additive and multiplicative noises to study thermally activated diffusion over a fluctuating barrier in underdamped dynamics.
本文从一个由加性噪声和乘性噪声驱动的朗之万方程出发，研究了欠阻尼动力学系统涨落势垒作用下的扩散性质。
Descusses the logarithmic correction factors of general multiplicative regression model and percent bias of unadjusted predicted value. The pool of correction factors is proposed.
文中讨论了一般乘法回归模型的对数改正系数和未校正预测值的相对偏差，提出了对数改正系数的合并问题。
Finally, some numerical examples are given to show that the new model can remove effectively the multiplicative noise of images by comparing the new model with a known model.
最后，通过具体的数值实验同已有的去噪模型进行比较，证明该模型能有效地去除图像中的乘性噪声。
Abstract: We apply a Langevin model by imposing additive and multiplicative noises to study thermally activated diffusion over a fluctuating barrier in underdamped dynamics.
摘要：本文从一个由加性噪声和乘性噪声驱动的朗之万方程出发，研究了欠阻尼动力学系统涨落势垒作用下的扩散性质。
This NNSC model utilizes the optimized method that combines the gradient and multiplicative algorithm to learn the feature coefficients, and only the gradient algorithm to learn feature vectors.
该模型利用梯度和倍增因子相结合的优化算法实现特征系数的学习；
This NNSC model utilizes the optimized method that combines the gradient and multiplicative algorithm to learn the feature coefficients, and only the gradient algorithm to learn feature vectors.
该模型利用梯度和倍增因子相结合的优化算法实现特征系数的学习；
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