Mean

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MEAN. This word is sometimes used for mesne. (q.v.)

A Law Dictionary, Adapted to the Constitution and Laws of the United States. By John Bouvier. Published 1856.
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The mean vector [[mu].sub.k] is initialized by the median vector, since mean and median are same for normal distribution and the median (Me) is highly robust against outliers with 50% breakdown points to estimate central value of the distribution.
Where [M.sup.i.sub.k] is the median vector of the k-th class from the block training set [B.sup.i] and [M.sup.i] is the total mean vector of all samples from [B.sup.i].
Similarly, BT target data can also be simulated by manual setting of component weights in U, following equation (13) and adding the mean vector [bar.X].
Also, for the T-variate spherical (elliptical) distribution, the probability density function is P[S.sup.T] with respect to the mean vector, although when the density function is PST, the distribution is not always spherical (elliptical).
The most useful contribution of PCA to the Pareto dataset is that the entire Pareto dataset can be approximated by the mean vector of the dataset and a linear combination of a specified number of eigenvectors:
The within-class dispersion matrix expresses the dispersion of each sample around the mean vector, and the between-class dispersion matrix expresses the distance distribution between two sample sets:
This method first derives the sample mean vector mM and the covariance matrix R as follows:
where each Gaussian component [[phi].sup.k] is parameterized by the mean vector [[mu].sup.k] of the same length as z and a (d+1) x (d+1) positive definite covariance matrix [[summation].sup.k].
where each Gaussian component [[phi].sup.k] is parameterized by the mean vector [[mu].sup.k] of the same length as z and a (d + l)x(d + l) positive definite covariance matrix [[summation].sup.k].
A multivariate process is characterized by a mean vector [mu] and covariance matrix [summation] which describes the quality characteristics and their interrelations.
The Z value is calculated using the formula Z = [nr.sup.2], where n is the number of observations and r is the mean vector length regarding data distribution.