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  "body": "\n## Summary\n\nA real random variable is sub-Gaussian when its tails decay at least as fast as those of a [[normal distribution|05AQE0QR0D]], that is, like $e^{-c t^2}$.\nNormal variables and bounded variables are sub-Gaussian.\nSums of independent sub-Gaussian variables are again sub-Gaussian, with explicit constants, and this gives concentration bounds of [[Hoeffding|0WPDNXGSSN]] type.\nThe notion is central in high-dimensional probability, random matrix theory and statistical learning.\n\n## Explanations\n\n### L1 human\n\nA sub-Gaussian variable is one that is very unlikely to be far from $0$: the chance of exceeding $t$ drops like $e^{-c t^2}$, as for a bell curve.\nA fair $\\pm 1$ coin is sub-Gaussian, since it never exceeds $1$ at all.\nAn [[exponential|0E99F51GVB]] variable is not, since $P(X \u003e t) = e^{-t}$ decays only like $e^{-t}$.\nThe average of many independent sub-Gaussian variables is sharply concentrated around its mean.\n",
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