1 - 10 of 134 for CovarianceSearch Results
Covariance   (Built-in Mathematica Symbol)
Covariance[v_1, v_2] gives the covariance between the vectors v_1 and v_2. Covariance[m] gives the covariance matrix for the matrix m. Covariance[m_1, m_2] gives the ...
Descriptive Statistics   (Mathematica Tutorial)
Descriptive statistics refers to properties of distributions, such as location, dispersion, and shape. The functions described here compute descriptive statistics of lists of ...
CovarianceFunction   (Built-in Mathematica Symbol)
CovarianceFunction[data, hspec] estimates the covariance function at lags hspec from data. CovarianceFunction[proc, hspec] represents the covariance function at lags hspec ...
CovarianceEstimatorFunction   (Built-in Mathematica Symbol)
CovarianceEstimatorFunction is an option for generalized linear model fitting functions that specifies the estimator for the parameter covariance matrix.
WeakStationarity   (Built-in Mathematica Symbol)
WeakStationarity[proc] gives conditions for the process proc to be weakly stationary.
AbsoluteCorrelation   (Built-in Mathematica Symbol)
AbsoluteCorrelation[v_1, v_2] gives the absolute correlation between the vectors v_1 and v_2. AbsoluteCorrelation[m] gives the absolute correlation matrix for the matrix m. ...
WishartDistribution   (Multivariate Statistics Package Symbol)
WishartDistribution[\[CapitalSigma], m] represents a Wishart distribution with scale matrix \[CapitalSigma] and degrees of freedom parameter m.
A variety of moments or combinations of moments are used to summarize a distribution or data. Mean is used to indicate a center location, variance and standard deviation are ...
KalmanEstimator   (Built-in Mathematica Symbol)
KalmanEstimator[ssm, {w, v}] constructs the Kalman estimator for the StateSpaceModel ssm with process and measurement noise covariance matrices w and v. KalmanEstimator[ssm, ...
LQEstimatorGains   (Built-in Mathematica Symbol)
LQEstimatorGains[ssm, {w, v}] gives the optimal estimator gain matrix for the StateSpaceModel ssm, with process and measurement noise covariance matrices w and v. ...
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