rags2ridges (version 2.2.6)

KLdiv: Kullback-Leibler divergence between two multivariate normal distributions

Description

Function calculating the Kullback-Leibler divergence between two multivariate normal distributions.

Usage

KLdiv(Mtest, Mref, Stest, Sref, symmetric = FALSE)

Value

Function returns a numeric representing the (symmetric) Kullback-Leibler divergence.

Arguments

Mtest

A numeric mean vector for the approximating multivariate normal distribution.

Mref

A numeric mean vector for the true/reference multivariate normal distribution.

Stest

A covariance matrix for the approximating multivariate normal distribution.

Sref

A covariance matrix for the true/reference multivariate normal distribution.

symmetric

A logical indicating if the symmetric version of Kullback-Leibler divergence should be calculated.

Author

Wessel N. van Wieringen, Carel F.W. Peeters <[e-mail]>

Details

The Kullback-Leibler (KL) information (Kullback and Leibler, 1951; also known as relative entropy) is a measure of divergence between two probability distributions. Typically, one distribution is taken to represent the `true' distribution and functions as the reference distribution while the other is taken to be an approximation of the true distribution. The criterion then measures the loss of information in approximating the reference distribution. The KL divergence between two \(p\)-dimensional multivariate normal distributions \(\mathcal{N}^{0}_{p}(\boldsymbol{\mu}_{0}, \mathbf{\Sigma}_{0})\) and \(\mathcal{N}^{1}_{p}(\boldsymbol{\mu}_{1}, \mathbf{\Sigma}_{1})\) is given as $$ \mathrm{I}_{KL}(\mathcal{N}^{0}_{p} \| \mathcal{N}^{1}_{p}) = \frac{1}{2}\left\{\mathrm{tr}(\mathbf{\Omega}_{1}\mathbf{\Sigma}_{0}) + (\boldsymbol{\mu}_{1} - \boldsymbol{\mu}_{0})^{\mathrm{T}} \mathbf{\Omega}_{1}(\boldsymbol{\mu}_{1} - \boldsymbol{\mu}_{0}) - p - \ln|\mathbf{\Sigma}_{0}| + \ln|\mathbf{\Sigma}_{1}| \right\}, $$ where \(\mathbf{\Omega} = \mathbf{\Sigma}^{-1}\). The KL divergence is not a proper metric as \(\mathrm{I}_{KL}(\mathcal{N}^{0}_{p} \| \mathcal{N}^{1}_{p}) \neq \mathrm{I}_{KL}(\mathcal{N}^{1}_{p} \| \mathcal{N}^{0}_{p})\). When symmetric = TRUE the function calculates the symmetric KL divergence (also referred to as Jeffreys information), given as $$ \mathrm{I}_{KL}(\mathcal{N}^{0}_{p} \| \mathcal{N}^{1}_{p}) + \mathrm{I}_{KL}(\mathcal{N}^{1}_{p} \| \mathcal{N}^{0}_{p}). $$

References

Kullback, S. and Leibler, R.A. (1951). On Information and Sufficiency. Annals of Mathematical Statistics 22: 79-86.

See Also

covML, ridgeP

Examples

## Define population
set.seed(333)
p = 25
n = 1000
X = matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X)[1:25] = letters[1:25]
Cov0  <- covML(X)
mean0 <- colMeans(X)

## Obtain sample from population
samples <- X[sample(nrow(X), 10),]
Cov1  <- covML(samples)
mean1 <- colMeans(samples)

## Regularize singular Cov1
P <- ridgeP(Cov1, 10)
CovR <- solve(P)

## Obtain KL divergence
KLdiv(mean1, mean0, CovR, Cov0)