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We use the resulting data to produce the matrix of random co-occurrences (C^{mathrm{rand}}_{ij}), and compare it to the matrix of co-occurrences (C_{ij}).
In estimating variance, both mean vector and covariance matrix are required [29]; hence, lower bound on SINR is chosen because covariance matrix of random vector is known.
The expectation operation with respect to x is denoted as (mathbb {E}_{x}[!cdot ]); the correlation matrix of random vector x is defined as (mathbf {K}_{mathbf {x}}=mathbb {E}[!mathbf {x}mathbf {x}^{dag }]).
where Y is an N × 1 matrix of the target variable, X an N × p matrix of auxiliary data, β is a p × 1 matrix of model parameters, and ϵ an N × 1 matrix of random variables that follow some joint probability distribution; N is the population size; in a forest survey it might be the number of grid cells which tessellate the study area.
With linear ICA, X is modeled as a linear mixture of independent sources, i.e.,
The variance covariance matrix of random effects was assumed to be unstructured.
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Kohlgraf-Owens, T. W. & Dogariu, A. Transmission matrices of random media: means for spectral polarimetric measurements.
Since 2×2 sized matrices of random values always present shifting and scaling behaviour, it would be impossible to differentiate their qualities.
In those applications, not only covariance matrices of random variables are assumed to be structured (usually diagonal), but the causal structure itself is assumed to be known.
From the above random effects, the covariance matrix of the random effects [13] can be calculated as follows: (11).
Subsequently, Ξ d is a diagonal matrix composed of random elements that represent shadowing due to user mobility: Ξ d = diag, where ξ= [ξ1, ξ2... ξ K ].
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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