变量

A line of best fit can be drawn in order to study the correlation between the variables.

一个最适合的线路，可以得出，以研究变量之间的相关性。

The samples actually observed. In contrast Bayesian methods view the parameters as random variables having some known a priori distribution.

与此不同的是，贝叶斯估计则把待估计的参数看成是符合某种先验概率分布的随机变量。

First, the concept emergence probability is assumed to be a random variable with a priori Beta distribution. Second, its priori parameters are designated by the distance-based similarity algorithm, calculated by Bayesian Estimation.

该算法首先假定概念出现概率是符合Beta分布的随机变量，然后基于语义距离的相似度算法计算先验参数，并根据统计样本计算该先验分布下基于最小风险的贝叶斯估计后验参数。

In this algorithm, the direction-of-arrival is assumed to be a discrete random variable with a priori probability density function defined on a set of candidate points. Whether or not the secondary sample is required is based on the a posteriori probability distribution of a set of candidate point"s, which can be calculated from the array received signals. And then, the resulting beamformer is a weighted sum of the beamformers pointed at the latest set of point"s, which are combined according to the value of the a posteriori probability for each pointing direction.

在该算法中，期望信号的波达方向被看作是一个定义在若干候选波达方向上的离散随机变量，由阵列接收信号计算出各候选波达方向的后验概率，并将后验概率的分布作为是否需要进行二次采样的判断标准；随后，对各波达方向上的波束成形权值线性加权，便可得到自适应波束成形器，权系数为各候选波达方向的后验概率值。

As the center or the Algebra subalgebra of a algebra plays an important role in reducing the number of state variables, the cartan subalgebra is a maximum Abelian subalgebra of an algebra, so it can be a powerful tool for the structure simplification of control systems.

由于代数的中心，或交换子代数在系统的变量降低方面具有重要作用，而Cartan子代数是李代数的极大交换子代数，因而它能成为简化系统结构的有力工具，第二部分利用向量场的积分曲线所定义的映射，构造出一个S—坐标，在该坐标下任一向量场都有一个标准的表示方法，一种无穷级数表示方法。

But they assume the abscissa, the ordinate of the MS and the distance between the MS and the severing BS are three independent variables neglecting their correlation.

其主要缺点是把移动台的横坐标、纵坐标与移动台到服务基站之间的距离作为3个相互独立的变量，忽略了3者之间的相关性。

In BeanShell using an untyped or "loosely" typed variable is also equivalent to declaring a local variable.

在Beanshll中使用一个宽松类型的变量也等价于定义一个本地变量。

Second, we research the way to code the AgC and the reliability, validity of the AgC variable.

其次，研究了AgC变量记分方式以及AgC变量的信效度。

Ceteris paribus is a Latin phrase that means all variables other than the ones being studied are assumed to be constant.

Ceteris paribus是一个拉丁短语，表明除了正在研究的变量之外，所有相关变量保持不变。

Between a dependent variable and independent variable X1,...,XN.

描述依存变量Y 和独立变量 X1，。。。，XN 之间的关系。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。