例句與造句

1. which represent the linear "'polynomial least squares "'" system " and describe its processing.
2. The latter is commonly used in target tracking in the form of Kalman filtering, which is effectively a recursive implementation of polynomial least squares.
3. The variance in a prediction of the dependent variable ( unemployment ) as a function of the independent variable ( GDP growth ) is given in polynomial least squares.
4. In effect, both applications produce average curves as generalizations of the common average of a set of numbers, which is equivalent to zero degree polynomial least squares.
5. The advantage of using orthogonal projection is that e _ \ min can be determined for use in the "'polynomial least squares "'processed statistical variance of the estimate.
6. It's difficult to find polynomial least squares in a sentence. 用polynomial least squares造句挺難的
7. Two common applications of polynomial least squares methods are approximating a low-degree polynomial that approximates a complicated function and estimating an assumed underlying polynomial from corrupted ( also known as " noisy " ) observations.
8. (4 ) "'Polynomial least squares "'processing produces deterministic moments ( analogous to mechanical moments ), which may be considered as moments of sample statistics, but not of statistical moments.
9. However, if "'polynomial least squares "'is used when the variance \ sigma _ \ varepsilon ^ 2 is not measurable ( such as in econometrics or statistics ), it can be estimated with observations in e _ \ min from orthogonal projection as follows:
10. Given samples z _ n where the subscript " n " is the sample index, the problem is to apply "'polynomial least squares "'to estimate " y ( t ) ", and to determine its variance along with its expected value.
11. Both linear regression and non-linear regression are addressed in polynomial least squares, which also describes the variance in a prediction of the dependent variable ( y axis ) as a function of the independent variable ( x axis ) and the deviations ( errors, noise, disturbances ) from the estimated ( fitted ) curve.
12. As a result, the samples z _ n ( noisy signal ) are considered to be the input to the linear "'polynomial least squares "'" system " which transforms the samples into the empirically determined statistical estimate \ hat y ( \ tau ), the expected value E [ \ hat y ], and the variance \ sigma _ \ hat y ^ 2.
13. The Greek letter "'\ tau "'is the independent variable " t " when estimating the dependent variable " y ( t ) " after data fitting has been performed . ( The letter "'\ tau "'is used to avoid confusion with " t " before and sampling during "'polynomial least squares "'processing . ) The overbar ( ?) defines the deterministic centroid of u _ n as processed by "'polynomial least squares "' i . e ., it defines the deterministic first order moment, which may be considered a sample average, but does not here approximate a first order statistical moment:
14. The Greek letter "'\ tau "'is the independent variable " t " when estimating the dependent variable " y ( t ) " after data fitting has been performed . ( The letter "'\ tau "'is used to avoid confusion with " t " before and sampling during "'polynomial least squares "'processing . ) The overbar ( ?) defines the deterministic centroid of u _ n as processed by "'polynomial least squares "' i . e ., it defines the deterministic first order moment, which may be considered a sample average, but does not here approximate a first order statistical moment: