But we may have some guess as to what the mean vector is.
2.
The update of the mean vector m maximizes a log-likelihood, such that
3.
The sample estimates of the mean vector and variance-covariance matrices will substitute the population quantities in this formula.
4.
*If you have two smallest vectors with the same magnitude, then the mean vector will be on one of the angle bisectors.
5.
Assuming ( the maximum likelihood estimate of ) \ Sigma is non-singular, the first order condition for the estimate of the mean vector is
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The second line suggests the interpretation as perturbation ( mutation ) of the current favorite solution vector m _ k ( the distribution mean vector ).
7.
Where \ mu is the mean vector of the vector " r " and \ sigma ^ 2 is the variance of final wealth.
8.
The mean vector, of course, will be perpendicular to the direction of the smallest vector, and in the direction of the most positive vector.
9.
The mean and covariance representation of uncertainty is mathematically convenient because any linear transformation T can be applied to a mean vector m and covariance matrix M as Tm and TMT ^ T.
10.
*"'Mean as minimizer "': A key result about Bregman divergences is that, given a random vector, the mean vector minimizes the expected Bregman divergence from the random vector.
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