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In mathematical statistics, the Kullback–Leibler divergence, D KL {\displaystyle D_{\text{KL}}} (also called relative entropy), is a measure of how one probability distribution is different from a second, reference probability distribution. Applications include characterizing the relative (Shannon) entropy in information systems, randomness in continuous time-series, and information gain when comparing statistical models of inference. In contrast to variation of information, it is a distribution-wise asymmetric measure and thus does not qualify as a statistical metric of spread – it also does not satisfy the triangle inequality. In the simple case, a relative entropy of 0 indicates that the two distributions in question have identical quantities of information. In simplified terms, it is a measure of surprise, with diverse applications such as applied statistics, fluid mechanics, neuroscience and bioinformatics.
referencePosted on 10 Sep 2024, this text provides information on Miscellaneous in Academic & Science related to Academic & Science. Please note that while accuracy is prioritized, the data presented might not be entirely correct or up-to-date. This information is offered for general knowledge and informational purposes only, and should not be considered as a substitute for professional advice.
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