Kullback Leibler divergence

Since version 7.16

The Kullback-Leibler divergence (KL Divergence) is an information theoretic value which quantifies the difference between two distributions.

The divergence between a distribution Q(x) and P(x) is denoted D(P||Q) or D(P(x)||Q(x)).

Kullback-Leibler divergence

KL Divergence is not a metric as D(P||Q) != D(Q||P).

KL divergence can be used in many settings, but when used in a Bayesian setting, Q(x) is the prior and P(x) is the posterior, and it quantifies the change in distribution/model having made an observation.

Kullback-Leibler divergence calculator

In order to use the calculator, Q(x) is configured by setting Base Evidence, while P(x) is the current evidence on a network.