1 - 10 of 88 for CovarianceSearch Results

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

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

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[ss, {w, v}] constructs the Kalman estimator for the StateSpaceModel object ss with process and measurement noise **covariance** matrices w and v. ...

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[ss, {w, v}, \[Tau]] gives the optimal discrete-time estimator gain matrix with sampling period \[Tau] for the continuous-time StateSpaceModel object ...

Univariate descriptive statistics have been added to the built-in Mathematica kernel. Multivariate functionality from this package is included in the newly created ...

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 + \ ...)) ...