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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 covariance ...
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 ...
CovarianceEstimatorFunction   (Built-in Mathematica Symbol)
CovarianceEstimatorFunction is an option for generalized linear model fitting functions that specifies the estimator for the parameter covariance matrix.
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[ss, {w, v}] constructs the Kalman estimator for the StateSpaceModel object ss with process and measurement noise covariance matrices w and v. ...
LQEstimatorGains   (Built-in Mathematica Symbol)
LQEstimatorGains[ss, {w, v}] gives the optimal estimator gain matrix for the StateSpaceModel object ss with process and measurement noise covariance matrices w and ...
DiscreteLQEstimatorGains   (Built-in Mathematica Symbol)
DiscreteLQEstimatorGains[ss, {w, v}, \[Tau]] gives the optimal discrete-time estimator gain matrix with sampling period \[Tau] for the continuous-time StateSpaceModel object ...
Statistics`MultiDescriptiveStatistics`   (Mathematica Compatibility Information)
Univariate descriptive statistics have been added to the built-in Mathematica kernel. Multivariate functionality from this package is included in the newly created ...
LogitModelFit   (Built-in Mathematica Symbol)
LogitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial logistic regression model of the form 1/(1 + E -(\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...)) ...
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