例句與造句

1. Commonly used estimators include sample covariance.
2. As far as sample covariance matrices are concerned, a theory was developed by Mar
   enko and Pastur.
3. The product in the final line is therefore zero; there is no sample covariance between different principal components over the dataset.
4. Similarly, the intrinsic inefficiency of the sample covariance matrix depends upon the Riemannian curvature of the space of positive-define matrices.
5. Due to their ease of calculation and other desirable characteristics, the sample mean and sample covariance are widely used in statistics and applications to numerically represent the distribution.
6. It's difficult to find sample covariance in a sentence. 用sample covariance造句挺難的
7. simply by replacing the state covariance Q in Kalman gain matrix K by the sample covariance C computed from the ensemble members ( called the " ensemble covariance " ).
8. The root mean square residual ( RMR ) and standardized root mean square residual ( SRMR ) are the square root of the discrepancy between the sample covariance matrix and the model covariance matrix.
9. It can be seen in Eq . ( 4 ) that the penalty function of Eq . ( 9 ) is minimized by approximating the signal model to the sample covariance matrix as accurate as possible.
10. Next, we apply a property of least square regression models, that the sample covariance between \ hat { Y } _ i and Y _ i-\ hat { Y } _ i is zero.
11. where " n " is the sample size, \ overline { \ mathbf x } is the vector of column means and { \ mathbf S } is a m \ times m sample covariance matrix.
12. The use of the term " n "  " 1 is called Bessel's correction, and it is also used in sample covariance and the sample standard deviation ( the square root of variance ).
13. However, when \ alpha > 0, i . e ., when ridge regression is used, the addition of \ alpha I to the sample covariance matrix ensures that all of its eigenvalues will be strictly greater than 0.
14. When \ alpha = 0, i . e ., in the case of ordinary least squares, the condition that d > n causes the sample covariance matrix X ^ T X to not have full rank and so it cannot be inverted to yield a unique solution.
15. The reason the sample covariance matrix has \ textstyle N-1 in the denominator rather than \ textstyle N is essentially that the population mean \ operatorname { E } ( X ) is not known and is replaced by the sample mean \ mathbf { \ bar { X } }.
16. Then " D " is the data transformed so every random variable has zero mean, and " T " is the data transformed so all variables have zero mean and zero correlation with all other variables  the sample covariance matrix of " T " will be the identity matrix.
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