因此，经常为建立统计模型偏离20至50有更多的时间，比平常。
Therefore frequently for the creation of the statistical model departs from 20 to 50 more time, than usual.
我们深入研究了局域世界演化网络的一些统计特性以及发生在该模型网络上疾病传播的时间行为。
We deeply investigate some statistical properties of local-world evolving networks and temporal behavior of epidemic spreading on such this evolving model.
在过去数年里，西佩尔构建出强大的统计学模型来追踪DNA如何随时间而演化。
Siepel, who has developed powerful statistical models in recent years to trace how DNA changes over time.
讨论了初始光场为压缩态、原子光场耦合系数随时间变化情形下双光子过程J C模型的量子统计性质。
The quantum statistical properties of two photon Jaynes Cummings model with a time dependent atom field coupling coefficient are discussed under the initial squeezing state of the light field.
本文借助于平稳时间序列的极值分布理论，对南京地区异常低温事件频次和强度建立统计模型。
The statistical model of frequency and intensity of anomalous microtherm events in Nanjing is established by means of the extreme value distribution theory of stationary time series.
动态模型的统计参数是随时间变化的时变参数，参数的变化反映了大坝的渗流变化趋势。
Parameters of the dynamic statistical model are the time-varying parameters, variation of parameter reflects the variable trend of seepage.
药物浓度对时间数据作房室模型和统计矩解析，并求出相应的药动学参数。
Analysis of the data was executed by compartmental model and statistical moment calculation from which pharmacokinetic parameters were obtained.
本文提出了一种结合统计数据、工作量模型和机器计算速度的应用程序执行时间预测方法。
A prediction method of application's execution time was proposed in this paper, which combine the statistic data, computing workload and machine's computing speed.
性能上，得到了模型单次运行时间、延迟、利用率和三个统计指标值。
The paper receives a single model run time, latency, utilization ratio and values of the three statistical indicators.
金融时间序列模型的变点分析是一类重要的统计问题，它引起众多学者的关注。
The change-point analysis in financial time series has been regarded as one of the core areas of research in statistics.
试验所得的血浆浓度时间数据采用非房室模型统计矩原理分析处理。
Plasma concentration time data derived from the experiment were analyzed by non compartmental methods based on statistical moment theory.
动态时间序列周期分析预测模型是从数理统计的角度对值为连续型的时间序列进行分析，发现规律，从而成功预测未来。
The Dynamic time series period analysis and prediction model analyses a serial-typed time series from the point of statistics, finding out the law. thereby succeeding in predicting the future.
这类事故其本质是统计性的，其事故次数及首次事故时间是可以用数学模型来表达的。
Such events are statistical in nature and the number of events and the time of first event may be expressed by mathematical mode.
针对短时间序列的特殊性，根据仿真模型的现代谱确认技术，用置信区间的方法作为定量的统计决策。
The technique of modern spectrum validation is powerful in ensuring the similarity of simulation results to the real experimental results of short time series.
本文提供了一种用于识别这类随机信号的数学统计模型，并介绍了一种时间平均法用来获取超声背散射信号的衰减系数。
In this paper, a mathematical model is used to characterize this random signals, and a time averaging method is used for obtaining the ultrasonic attenuation coefficient.
根据装车站大量翔实的数据，在统计站停时间、装车时间、待机时间的分布规律基础上，建立了可靠性模型，进行了可靠性分析计算。
According to a great deal of real data, the distributing regularity of work-time in mine loading station is studied, and the reliability models are set up.
基于海杂波的复合散射理论，分析了海杂波的统计分布模型、时间相关性和空间相关性。
Based on theory of compound scatter, the amplitude distribution, temporal correlation and spatial correlation were discussed here.
在平均值为零或平均值为已知的季节时间序列模型中，根据加权对称估计量提出季节单位根的检验统计量，并求出此统计量的极限分布的表达式。
In this article, we propose seasonal unit root test statistics based on the Weighted Symmetric estimator and derive representation for limiting distributions of the statistics.
在检测开始之前，首先由摄像机对无人环境持续观测一段时间，建立背景的统计模型；
First, it builds the background model by observing the scene without people in it.
结果：治疗组的五个时间点的血浆乙醇含量比模型组低，有统计学意义（P＜0.05）。
Results: The blood plasm alcohol concentration at five different timing points of the treatment group was lower than that of the model group , and had statistic significance(P<0.05).
本文采用统计学习理论，建立了基于最小二乘支持向量机的永磁操动机构动作时间预测模型。
Support vector machine (SVM) is the best general machine learning theory developed from statistical learning theory, and suit to do prediction from small samples by learning.
本文采用统计学习理论，建立了基于最小二乘支持向量机的永磁操动机构动作时间预测模型。
Support vector machine (SVM) is the best general machine learning theory developed from statistical learning theory, and suit to do prediction from small samples by learning.
应用推荐