Distance correlation is a measure of statistical dependence between two paired random vectors of arbitrary, and possibly different, dimension. It was introduced in 2005 by Gabor J. Szekely to address a deficiency of Pearson's correlation, which can be zero even for variables that are actually dependent. The population distance correlation is zero if and only if the two random vectors are independent, so unlike ordinary correlation, a distance correlation of zero does imply independence, allowing it to detect nonlinear as well as linear relationships. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/
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Source Distance Correlation (Wikipedia)
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Entity-backed identity for the object-kind enum value this mathematical object already carries, resolved to a mathematics concept by an explicit value-to-entity map (phase 3 bucket conversion, docs\design_entity_backed_browse_buckets_20260928.md). The object-kind fact itself stays on the object unchanged.
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1. Distance Correlation (Wikipedia)
In Branch: Probability and Statistics, Lead sentenceQuote, In Branch: Probability and Statistics, Lead sentence
In statistics and in probability theory, distance correlation is a measure of dependence between two paired random vectors of arbi
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