In probability theory and information theory, the Kullback–Leibler divergence (also information divergence, information gain, relative entropy, or KLIC; here abbreviated as KL divergence) is a non-symmetric measure of the difference between two probability distributions P and Q. Specifically, the Kullback–Leibler divergence of Q from P, denoted DKL(P||Q), is a measure of the information lost when Q is used to approximate P: The KL divergence measures the expected number of extra bits required to code samples from P when using a code based on Q, rather than using a code based on P. Typically P represents the “true” distribution of data, observations, or a precisely calculated theoretical distribution. The measure Q typically represents a theory, model, description, or approximation of P. Although it is often intuited as a metric or distance, the KL divergence is not a true metric – for example, it is not symmetric: the KL divergence from P to Q is generally not the same as that from Q to P. However, its infinitesimal form, specifically its Hessian, is a metric tensor: it is the Fisher information metric. … Kullback–Leibler Divergence

# If you did not already know: “Kullback–Leibler Divergence”

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