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 refers to properties of distributions, such as location, dispersion, and shape. The functions described here compute descriptive statistics of lists of ...

CovarianceFunction[data, hspec] estimates the covariance function at lags hspec from data. CovarianceFunction[proc, hspec] represents the covariance function at lags hspec ...

CovarianceEstimatorFunction is an option for generalized linear model fitting functions that specifies the estimator for the parameter covariance matrix.

WeakStationarity[proc] gives conditions for the process proc to be weakly stationary.

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[\[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[ssm, {w, v}] constructs the Kalman estimator for the StateSpaceModel ssm with process and measurement noise covariance matrices w and v. KalmanEstimator[ssm, ...

LQEstimatorGains[ssm, {w, v}] gives the optimal estimator gain matrix for the StateSpaceModel ssm, with process and measurement noise covariance matrices w and v. ...