Sentence examples for sample vector and from inspiring English sources

Above equation means the correlation coefficients between sample vector and measured phased difference vectors with the same azimuth are same.

The aim of the present paper is to allow more general distributions of the sample vector, and to study exact distributions of generalized Chi-square and Fisher statistics being based upon the maximum likelihood estimators of the model's scaling parameters.

Our algorithm generates a random matrix, with dimensions that are smaller than M and N, where M is the length of the sampling vector and N is the length of signal that we want to reconstruct.

Thus to determine which frequencies tended to lead to correct responses for each observer, we performed a multiple linear regression on the SF sampling vectors and observer accuracy across the 600 total trials.

A spatial correlator ideal observer similar to Experiment 1 was implemented for Experiment 2. We performed Monte Carlo simulations testing the accuracy of the ideal observer discriminating action videos that were filtered with random SF sampling vectors and masked in 1/ f dynamic pink noise.

If correlation coefficient f2 is adopted as the similarity function between measured phase difference vector and sample vector, then f 2 ϕ, θ 1 i = Φ ˜ T ϕ, θ 1 Φ i Φ ˜ T ϕ, θ 1 Φ ˜ ϕ, θ 1 Φ T i Φ i (11) f 2 ϕ, θ 2 i = Φ ˜ T ϕ, θ 2 Φ i Φ ˜ T ϕ, θ 2 Φ ˜ ϕ, θ 2 Φ T i Φ i (12).

The presence of even a small amount of outliers in a data set can have a high impact on classical estimators like the sample mean vector and sample covariance matrix.

Concretely speaking, letting (hat {boldsymbol {mu }}) and (hat {Sigma }) be respectively the sample mean vector and sample variance-covariance matrix for a set of the inputs with a defect {x i | y i =−1}, we simulate ({tilde {boldsymbol {x}}_{i} | i=1,2,ldots,tilde {n}}) from the Gaussian distribution (mathrm {N}(hat {boldsymbol {mu }},hat {Sigma })) at random.

The Hotelling's T statistic is (5) T 2 = n 1 n 2 n 1 + n 2 (x − 1 − x − 2 ) t S p − 1 (x − 1 − x − 2 ), where x - i and S i denote the sample mean vector and sample covariance matrix of the ith group (i = 1,2), respectively, and S p = ((n1 − 1) S1 + (n2 − 1) S2)/(n1 + n2 − 2) denotes the pooled covariance matrix.

We call this reference sample "vector RNA" (vRNA) and its use in a reference design "vector reference design".

Let and be the sample vectors of and.