matjeffreyspar function

Matrix of Jeffreys measures (symmetrised Kullback-Leibler divergences) between Gaussian densities

Computes the matrix of Jeffreys measures between several multivariate ($p > 1$) or univariate ($p = 1$) Gaussian densities, given their parameters (mean vectors and covariance matrices if the densities are multivariate, or means and variances if univariate), using jeffreyspar.

matjeffreyspar(meanL, varL)

Arguments

• meanL: list of the means ($p = 1$) or vector means ($p > 1$) of the Gaussian densities.
• varL: list of the variances ($p = 1$) or covariance matrices ($p > 1$) of the probability densities.

Returns

Positive symmetric matrix whose order is equal to the number of densities, consisting of pairwise Jeffreys measures between the Gaussian densities.

Author(s)

Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine Demotes-Mainard

See Also

jeffreyspar.

matjeffreys for the matrix of Jeffreys divergences between probability densities which are estimated from the data.

Examples

data(roses)
    
    # Multivariate:
    X <- roses[,c("Sha","Den","Sym","rose")]
    summary(X)
    mean.X <- as.list(by(X[, 1:3], X$rose, colMeans))
    var.X <- as.list(by(X[, 1:3], X$rose, var))
    matjeffreyspar(mean.X, var.X)

    # Univariate :
    X1 <- roses[,c("Sha","rose")]
    summary(X1)
    mean.X1 <- by(X1$Sha, X1$rose, mean)
    var.X1 <- by(X1$Sha, X1$rose, var)
    matjeffreyspar(mean.X1, var.X1)