Sanov's theorem

In mathematics and information theory, Sanov's theorem gives a bound on the probability of observing an atypical sequence of samples from a given probability distribution. In the language of large deviations theory, Sanov's theorem identifies the rate function for large deviations of the empirical measure of a sequence of i.i.d. random variables.

Let A be a set of probability distributions over an alphabet X, and let q be an arbitrary distribution over X (where q may or may not be in A). Suppose we draw n i.i.d. samples from q, represented by the vector $x^n=x_1,x_2,\dots ,x_n$. Then, we have the following bound on the probability that the empirical measure ${\hat{p}}_{x^n}$ of the samples falls within the set A:

$q^n({\hat{p}}_{x^n}\in A)\le (n+1{)}^{|X|}{2}^{-n{D}_{\mathrm{K}\mathrm{L}}(p^{*}||q)}$,

where

• $q^n$ is the joint probability distribution on $X^n$, and
• $p^{*}$ is the information projection of q onto A.

In words, the probability of drawing an atypical distribution is bounded by a function of the KL divergence from the true distribution to the atypical one; in the case that we consider a set of possible atypical distributions, there is a dominant atypical distribution, given by the information projection.

Furthermore, if A is a closed set, then

$\underset{n\to \mathrm{\infty }}{\lim }\frac{1}{n}\log q^n({\hat{p}}_{x^n}\in A)=-D_{\mathrm{K}\mathrm{L}}(p^{*}||q).$

References

• Cover, Thomas M.; Thomas, Joy A. (2006). Elements of Information Theory (2 ed.). Hoboken, New Jersey: Wiley Interscience. pp. 362. ISBN 9780471241959. https://archive.org/details/elementsinformat00cove_577. 

• Sanov, I. N. (1957) "On the probability of large deviations of random variables". Mat. Sbornik 42(84), No. 1, 11–44.
• Санов, И. Н. (1957) "О вероятности больших отклонений случайных величин". МАТЕМАТИЧЕСКИЙ СБОРНИК' 42(84), No. 1, 11–44.