Sanov's theorem

In information theory, Sanov's theorem gives a bound on the probability of observing an atypical sequence of samples from a given probability distribution.

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 $x^{n}=x_{1},x_{2},...,x_{n}$. Then

$q^{n}(\{x^{n}:{\hat {p}}_{x^{n}}\in A\})\leq (n+1)^{|X|}2^{-nD_{\mathrm {KL} }(p^{*}||q)}$,

where

• $q^{n}(x^{n})$ is shorthand for $q(x_{1})q(x_{2})...q(x_{n})$ and $q^{n}(S)$ is shorthand for $\sum _{x^{n}\in S}q^{n}(x^{n})$,
• ${\hat {p}}_{x^{n}}$ is the empirical distribution of the sample $x^{n}$, and
• $p^{*}$ is the information projection of q onto A.

Furthermore, if A is a closed set,

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