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  "body": "\n## Summary\n\nLarge deviations theory studies the exponential decay of the probabilities of rare events.\nIts central notion is the large deviation principle, with a [[rate function|140N2PQSTE]] $I$.\nA family of probability measures $\\mu_n$ satisfies it when $\\mu_n(A)$ behaves like $\\exp(-n \\inf_{x \\in A} I(x))$.\nBasic results are [[Cramér's theorem|RZZK51CXMN]] for sample means, [[Sanov's theorem|QWEF2WAXJM]] for empirical measures and [[Varadhan's lemma|BD1CNVV5P0]] for exponential integrals.\nThe theory is used in statistical mechanics, information theory, queueing and risk analysis.\n\n## Explanations\n\n### L1 human\n\nThe [[law of large numbers|0ZVEAYVRC3]] says that the average of many coin tosses is close to $1/2$.\nLarge deviations theory asks how unlikely it is that the average is far from $1/2$, say above $0.6$.\nThe answer is that such a probability shrinks exponentially in the number of tosses, with a precise exponent.\nFor fair coins, the probability that the average of $n$ tosses is at least $0.6$ is about $e^{-0.020 n}$, up to factors that grow more slowly than any exponential.\n",
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