Examples of Covariance

org.apache.commons.math.stat.correlation.Covariance

Computes covariances for pairs of arrays or columns of a matrix.

The constructors that take RealMatrix or double[][] arguments generate covariance matrices. The columns of the input matrices are assumed to represent variable values.

The constructor argument biasCorrected determines whether or not computed covariances are bias-corrected.

Unbiased covariances are given by the formula

cov(X, Y) = Σ[(x_i - E(X))(y_i - E(Y))] / (n - 1) where E(X) is the mean of X and E(Y) is the mean of the Y values.

Non-bias-corrected estimates use n in place of n - 1 @version $Revision: 983921 $ $Date: 2010-08-10 12:46:06 +0200 (mar. 10 août 2010) $ @since 2.0

org.apache.commons.math3.stat.correlation.Covariance

Computes covariances for pairs of arrays or columns of a matrix.

The constructors that take RealMatrix or double[][] arguments generate covariance matrices. The columns of the input matrices are assumed to represent variable values.

The constructor argument biasCorrected determines whether or not computed covariances are bias-corrected.

Unbiased covariances are given by the formula

cov(X, Y) = Σ[(x_i - E(X))(y_i - E(Y))] / (n - 1) where E(X) is the mean of X and E(Y) is the mean of the Y values.

Non-bias-corrected estimates use n in place of n - 1 @since 2.0

                errorSeeds.setEntry(i, j, rg.nextGaussian() * sigma[j]);
            }
        }
        
        // Get covariance matrix for columns
        RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();
          
        // Create a CorrelatedRandomVectorGenerator to use to generate correlated errors
        GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
        double[] errorMeans = new double[nObs];  // Counting on init to 0 here
        CorrelatedRandomVectorGenerator gen = new CorrelatedRandomVectorGenerator(errorMeans, cov,

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Examples of Covariance

Examples of org.apache.commons.math3.stat.correlation.Covariance