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where is a sample in the th fold.
Let denote a particle set, where is a sample of x t with associated normalized weight.
where is a sample of independent and identically distributed (iid) zero mean additive white Gaussian noise (AWGN) with variance.
The method by Stuart et al. (2003) outputs in our notation a probability where is a sample from standard uniform distribution as an output score.
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In this label-free quantification approach, the abundance of peptide p in sample s was measured by its normalized peak area, where was a sample-dependent normalization factor and was calculated from the peak areas of a predetermined set of N = 6 endogenous, normalizing peptides in the sample.
In this labeled quantification approach, the abundance of peptide p in sample s was measured by its normalized response ratio, where was a sample-dependent normalization factor and was calculated from the response ratios of the N peptide normalizers in the sample.
where is a complex white Gaussian noise sample with single-sided power spectral density.
where is a vector consisting of AWGN samples with power.
where ω is a sample point.
Given this set, a real boosting algorithm iteratively finds T weak classifiers h t to form a strong classifier sign ( H ( x ) ) = sign ( ∑ t = 1 T h t ( x ) ) where x is a sample to be classified.
M × N matrix B is initialized to zero matrix where N is a sample size; then elements of B are randomly selected as directed edges.
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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