# 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)).

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

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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.