协方差 英语是什么意思 - 英语翻译

• covariance

在 中文 中使用 协方差 的示例及其翻译为 英语

总而言之，协方差矩阵界定了数据的形状。
To sum up, the covariance matrix defines the shape of the data.

方差与协方差分析、多元ANOVA、重复测量ANOVA.
Analysis of variance and covariance, multivariate ANOVA, repeated measures ANOVA.

为了方便计算我们需要构建协方差矩阵。
To make progress, we need to construct the covariance matrix.

图3展示了数据的整体形状如何定义协方差矩阵：.
Figure 3 illustrates how the overall shape of the data defines the covariance matrix:.

特别地，这一步需要在计算协方差矩阵前完成。
Specifically, this should be done before computing the covariance matrix.

在另一篇文章中，我们表明协方差矩阵可以写为一系列线性操作（缩放和旋转）。
In an earlier article, we showed that the covariance matrix can be written as a sequence of linear operations(scaling and rotations).

协方差和相关性是两个数学概念；这两种方法在统计学中被广泛使用。
Both correlation and covariance are basically two concepts of mathematics which are widely used in statistics.

(你可能已经猜到协方差矩阵是一个对称矩阵，这意味着可以任意交换i和j)。
(You might be able to guess that the covariance matrix is symmetric, which means that it doesn't matter if you swap i and j).

然而，该解决方案需要计算协方差矩阵，因此它可能不适用于具有大量特征的情况。
However, the‘eigen' solver needs to compute the covariance matrix, so it might not be suitable for situations with a high number of features.

协方差矩阵不仅仅描述了这个分布的形状，也最终决定了我们想要预测的函数所具有的特性。
The covariance matrix will not only describe the shape of our distribution, but ultimately determines the characteristics of the function that we want to predict.

但是，协方差矩阵不包含数据变换相关的任何信息。
However, the covariance matrix does not contain any information related to the translation of the data.

人们可能会愤世嫉俗地说，由于协方差结构，资产配置是过去25年来最容易的游戏。
One could be cynical and say that asset allocation has been the easiest game over the past 25 years because of the covariance structure.

相关性和协方差都可以构建关系，并且还可测量两个随机变量之间的依赖关系。
Both Correlation and Covariance establish the relationship and also measure the dependency between two random variables.

概率论中也涉及到算子，如期望、方差、协方差、阶乘等。
Operators are also involved in probability theory, such as expectation, variance, covariance, factorials, etc.

现在，我们有了一个预测矩阵来表示下一时刻的状态，但是，我们仍然不知道怎么更新协方差矩阵。
We now have a prediction matrix which gives us our next state, but we still don't know how to update the covariance matrix.

TruncatedSVD非常类似于PCA,但不同之处在于它工作在样本矩阵而不是它们的协方差矩阵。
TruncatedSVD is very similar to PCA, but differs in that it works on sample matrices directly instead of their covariance matrices.

这个“白色”数据的协方差矩阵等于单位矩阵，使得方差和标准差等于1，协方差等于零：.
The covariance matrix of this‘white' data equals the identity matrix, such that the variances and standard deviations equal 1 and the covariance equals zero:.

将此参数设置为这两个极值之间的值将估计协方差矩阵的缩小版本。
Setting this parameter to a value between these two extrema will estimate a shrunk version of the covariance matrix.

因此，这个多元高斯模型将x和μ作为长度d的向量，∑将是一个d×d协方差矩阵。
Thus, this multivariate Gaussian model would have x and μ as vectors of length d, and Σ would be a d x d covariance matrix.

解决这个问题的一种方式是使用限制形式的协方差矩阵。
One way to address this problem is to use restricted forms of the covariance matrix.

由于该协方差只取决于两点间的距离，因而是平稳的。
Since the covariance only depends on distances between points, it is stationary.

这可以看作是：原始数据协方差矩阵的特征向量是（每列表示一个特征向量）：.
This can be seen as follows: The eigenvectors of the covariance matrix of the original data are(each column represents an eigenvector):.

这对EM算法提出了一个问题，因为它试图更新协方差矩阵。
This poses a problem for the EM algorithm as it tries to update the covariation matrix.

然而，协方差矩阵的秩受到训练图像的限制：如果有N个训练样本，则最多有N-1个对应非零特征值的特征向量。
However the rank of the covariance matrix is limited by the number of training examples: if there are N training examples, there will be at most N- 1 eigenvectors with non-zero eigenvalues.

协方差矩阵适应性进化策略（CMA-ES）.
Covariance matrix adaptation evolution strategy(CMA-ES).

协方差和相关性是两个数学概念；.
Covariance and Correlation are two mathematical concepts;

首先，我们假设所有类别共享相同的协方差矩阵。
To start with, we shall assume that all classes share the same covariance matrix.

首先，我们假设所有类别共享相同的协方差矩阵。
It assumes that all classes share the same covariance matrix.

其中，$\mathbf{S}$是数据的协方差矩阵。
Where$\mathbf{S}$ is the sample covariance matrix of the data.

这个完整的协方差矩阵与数据一起发布（见附录F）。
The full covariance matrix is released with the data(see Appendix F).