Convert standard deviation and correlation to covariance
This example shows how to convert standard deviation and correlation to covariance.
ExpSigma = [0.5 2.0]; ExpCorrC = [1.0 -0.5 -0.5 1.0]; ExpCovariance = corr2cov(ExpSigma, ExpCorrC)
ExpCovariance = 2×2 0.2500 -0.5000 -0.5000 4.0000
ExpSigma— Standard deviations of each process
Standard deviations of each process, specified as a vector of length
n with the standard deviations of each process.
n is the number of random processes.
ExpCorrC— Correlation matrix
(Optional) Correlation matrix, specified as an
n correlation coefficient
matrix. A correlation coefficient is a statistic in
which the covariance is scaled to a value between minus one (perfect
negative correlation) and plus one (perfect positive correlation).
ExpCorrC is not specified, the processes are assumed
to be uncorrelated, and the identity matrix is used.
ExpCovariance— Covariance matrix
Covariance matrix, returned as an
n covariance matrix, where
n is the number of processes.
The (i,j) entry is the expectation of the i'th fluctuation from the mean times the j'th fluctuation from the mean.
ExpCov(i,j) = ExpCorrC(i,j)*ExpSigma(i)*ExpSigma(j)