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Kullback Leibler Divergence

KLD.Rd

This function calculates the Kullback Leibler Divergence $D(P\|Q)$ value between two distributions $P$ and $Q$, represented by their two samples, X and Y. Both samples will have their density inferred. The intended use-case is to compare 2D and 3D densities, e.g.: to find interesting pairs of parameters within a bigger distribution.

Usage

KLD(X, Y, de = c("copula", "ks", "mvtnorm"))

Arguments

X

sample from distribution P

Y

sample from distribution Q

de

density estimation mechanism (character scalar)

Value

D, a scalar value, the Kullback Leibler Divergence

Details

This estimate requires a method of density estimation, by default we use the copula based methods fitCopula and dCopulaPrior (which has a fairly high accuracy, but can be quite slow).

Effects of setting de to

  • "ks": density is estimated via ks::kde()

  • "mvtnorm": density is estimated via manual kernel density estimation, using the multivariate Gaussian density mvtnorm::dmvnorm