kld: Kullback-Leibler Divergence

In AlexChristensen/NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

Kullback-Leibler Divergence

Description

Estimates the Kullback-Leibler Divergence which measures how one probability distribution diverges from the original distribution (equivalent means are assumed) Matrices must be positive definite inverse covariance matrix for accurate measurement. This is a relative metric

Usage

kld(base, test)

Arguments

base	Full or base model
test	Reduced or testing model

Value

A value greater than 0. Smaller values suggest the probability distribution of the reduced model is near the full model

Author(s)

Alexander Christensen <[e-mail]>

References

Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79-86.

Examples

A1 <- solve(cov(neoOpen))

## Not run: 
A2 <- LoGo(neoOpen)

kld_value <- kld(A1, A2)

## End(Not run)

AlexChristensen/NetworkToolbox documentation built on March 6, 2023, 5:08 p.m.