Приклади вживання Covariance matrix Англійська мовою та їх переклад на Українською
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A 2×2 covariance matrix is needed;
Constructing a nonnegative estimate of a covariance matrix.
Where the covariance matrix Σ is equal to.
Constructing a nonnegative estimate of a covariance matrix.
CMA-ES stands for covariance matrix adaptation evolution strategy.
And to tell the filter that we know the exact position,we give it a zero covariance matrix.
This emphasizes that the covariance matrix is symmetric.
The covariance matrix is related to the moment of inertia tensor for multivariate distributions.
Similar ideas can be used when x{\displaystyle x}is random with uncertainty in the covariance matrix.
The covariance matrix adaptation(CMA) is a method to update the covariance matrix of this distribution.
This renders the numerical representation of the state covariance matrix P indefinite, while its true form is positive-definite.
The covariance matrix of the distribution is updated(incrementally) such that the likelihood of previously successful search steps is increased.
Many filtering and control methods represent estimates of the state of a system in the form of a mean vector andan associated error covariance matrix.
The L·D·LT decomposition of the innovation covariance matrix Sk is the basis for another type of numerically efficient and robust square root filter.
If the kernel function k{\displaystyle k} is also a covariance function as used in Gaussian processes, then the Gram matrix K{\displaystyle\mathbf{K}}can also be called a covariance matrix.
In practice, we would estimate the covariance matrix based on sampled data from X{\displaystyle X} and Y{\displaystyle Y}(i.e. from a pair of data matrices). .
Proofs that use characteristic functions can be extended to cases where each individual Xi is a random vector in ℝk,with mean vector μ= E(Xi) and covariance matrix Σ(among the components of the vector), and these random vectors are independent and identically distributed.
The'forecast error covariance matrix' is typically constructed between perturbations around a mean state(either a climatological or ensemble mean).
Specifically, a mean and covariance estimate( m, M){\displaystyle(m, M)}is conservatively maintained so that the covariance matrix M{\displaystyle M} is greater than or equal to the actual squared error associated with m{\displaystyle m}.
One path is used for the covariance matrix adaptation procedure in place of single successful search steps and facilitates a possibly much faster variance increase of favorable directions.
If the initial position and velocity are not known perfectly the covariance matrix should be initialised with a suitably large number, say B, on its diagonal.
Although the covariance matrix is often treated as being the expected squared error associated with the mean, in practice the matrix is maintained as an upper bound on the actual squared error.
The moment of inertia of a cloud of n points with a covariance matrix of Σ{\displaystyle\Sigma} is given by I= n( 1 3× 3 tr( Σ)- Σ).{\displaystyle I=n(\mathbf{1}_{3\times 3}\operatorname{tr}(\Sigma)-\Sigma).} This difference between moment of inertia in physics and in statistics is clear for points that are gathered along a line.
The sample mean andthe sample covariance matrix are unbiased estimates of the mean and the covariance matrix of the random vector\textstyle\mathbf{X}, a row vector whose jth element(j= 1,…, K) is one of the random variables.
The sample mean andthe sample covariance matrix are unbiased estimates of the mean and the covariance matrix of the random vector X{\displaystyle\textstyle\mathbf{X}}, a vector whose jth element( j= 1,…, K){\displaystyle(j=1,\ldots,K)} is one of the random variables.
And with(non-singular) covariance matrices Σ 0, Σ 1.
In most real-time applications, the covariance matrices that are used in designing the Kalman filter are different from the actual(true) noise covariances matrices. .
Practical implementation of the Kalman Filter is often difficult due to thedifficulty of getting a good estimate of the noise covariance matrices Qk and Rk.