Sentence examples for variance from one from inspiring English sources

The high variance (from one style to another) of such a metric may strongly impact its value (distribution) and limit its applicability and its interpretation for different systems.

This shows how the complexity of the algorithm can be reduced by an order of magnitude by adopting an extra implicit assumption regarding the wide-sense stationarity of the noise and hence the constancy of its variance from one iteration to the next.

"Mr. Speaker, your voting record is at variance from one of the Church's most ancient moral teachings: From the Apostles to the present, the Magisterium of the Church has insisted that those in power are morally obligated to preference the needs of the poor," they said.

"Mr. Speaker, your voting record is at variance from one of the church's most ancient moral teachings," the letter says.

We also observed that variances from One-Color ratios show a similar profile of variation as that obtained from RefOligo and RefPool.

Will one of the F.B.I. agents who interviewed her on July 2nd go rogue and release his personal notes, showing conclusively that the only way she avoided indictment for lying to federal investigators was by telling a story wildly at variance from the one she had been telling the public for a year and a half?

In the absence of local data, these were inferred from other studies, or specialist opinion, so the numbers of episodes of care delivered may be at variance from the ones estimated by this paper.

Because Poisson and non-Poisson variance are independent from one another (Additional File 1, Figure S12), total variance can be calculated by adding the independent components of the variance [ 39].

(40) The mean of (alpha_{m}^{ k+1,l)}) is zero by construction, but the variance is unequal from one due to (39).

Based on the previous analysis, it is known that the expectation and variance of interference from one cell in Set-A are the same as the expressions presented in (23) and (25), respectively.

In other words, the first eigenvector w 1(U j ) contains the most information within the spectral image data, the second contains the most common response after the subtraction of variance from the first one, and so on.