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Gaussian variables with variance, respectively, and ; also, let be the variance of the innovation noise.
Let μ X and μ Y be the expectations of X and Y, (sigma _{Y}^{2}) be the variance of Y, and σ XY be their covariance.
Recall now the error model (25) for the analog calibration, given by the error matrix Δ B ={b ij r ij } and let β be the variance of the random variables r ij.
To be precise, let σ X be the variance of the overall coefficients in the subband, m 4,X be their fourth moment, and σ α be the standard deviation of noise-free coefficients in the band.
Equation 21 is the likelihood function for the measurement of noise in the measurements set, which denotes the clutter probability density in theoretical filtering [12, 13], and the optimal choice of σ should be the variance of noise, as shown in (20).
Let σ2 be the variance of n ℓ i (17), σ 2 = σ k + 1 2 M k 2, however, as before using Theorem 2.3 of [24]n ℓ i is white in the scale of interest, and can be modeled as C N ( 0, 1 ).
Similar(50)
That is, if the variance of the driving noise is, the variance of the noise at the resonator output is.
Because it is the variance of the estimator himself minus expectation, squared.
OK, the variance of the sum of two random variables is the variance of the first random variable, plus the variance of the second random variable, plus twice the covariance of the random variables.
where is the variance of.
where is the variance of the frame, is the variance of the noise.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com