mean vector वाक्य
उदाहरण वाक्य
मोबाइल
- But we may have some guess as to what the mean vector is.
- The update of the mean vector m maximizes a log-likelihood, such that
- The sample estimates of the mean vector and variance-covariance matrices will substitute the population quantities in this formula.
- *If you have two smallest vectors with the same magnitude, then the mean vector will be on one of the angle bisectors.
- Assuming ( the maximum likelihood estimate of ) \ Sigma is non-singular, the first order condition for the estimate of the mean vector is
- The second line suggests the interpretation as perturbation ( mutation ) of the current favorite solution vector m _ k ( the distribution mean vector ).
- Where \ mu is the mean vector of the vector " r " and \ sigma ^ 2 is the variance of final wealth.
- The mean vector, of course, will be perpendicular to the direction of the smallest vector, and in the direction of the most positive vector.
- 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.
- *"'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.
- Returning to the example above, when the covariance is zero it is trivial to determine the location of the object after it moves according to an arbitrary nonlinear function f ( x, y ) : just apply the function to the mean vector.
- Where n is the length / dimension of x and \ frac { \ partial g ( \ mu ) } { \ partial x _ i } is the partial derivative of g at the mean vector \ mu with respect to the " i "-th entry of x.
- If I and III are equal in magnitude ( don't need the same sign ), then the mean vector can be 150?or-30? but in those cases lead II will be smallest, so the only possibilities left are + 60?or-120? depending on the sign of the lead II result.
- The mean vector and covariance matrix of the Gaussian distribution completely specify the GP . GPs are usually used as a priori distribution for functions, and as such the mean vector and covariance matrix can be viewed as functions, where the covariance function is also called the " kernel " of the GP . Let a function f follow a Gaussian process with mean function m and kernel function k,
- The mean vector and covariance matrix of the Gaussian distribution completely specify the GP . GPs are usually used as a priori distribution for functions, and as such the mean vector and covariance matrix can be viewed as functions, where the covariance function is also called the " kernel " of the GP . Let a function f follow a Gaussian process with mean function m and kernel function k,
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