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where λ2/2 denotes the variance.
where Var denotes the variance of the random sequence.
where ({sigma _{n}^{2}}) denotes the variance of the components of the noise vector n.
where (sigma _{0}^{2}) denotes the variance of h i in the inactive state, and (sigma _{1}^{2} gg sigma _{0}^{2}) denotes the variance of h i in the active state.
where W k denotes the variance matrix of the objects' features allocated to cluster C k (k = 1, …, g).
Let us define SNR = 10 lg ∑ n = 1 N s 2 ( n · L mul ) N · Var[noise], where Var denotes the variance of a random variable.
Similar(19)
where R(t i ) denotes the variance-covariance matrix of the observation noise ((R(t_{i})=left
Here, β0j denotes the hospital-specific log-odds of death for an average patient, β1j denotes the hospital-specific regression slope relating illness severity to the log-odds of death, and (σ 1 2 σ 12 σ 12 σ 2 2 ) denotes the variance-covariance matrix for the hospital-specific random effects.
Let Var(D) denote the variance of D. We have text{Var}(D =text{Var}(D_{1}).
where T is the period of time between measurements, q1 and q2 denote the variance of the acceleration noise and the noise in target return intensity, respectively.
We denote the variance of A i ( d ) and A i ( c ) by σ a, i ( d ) and σ a, i ( c ), respectively.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com