Information theory | Probabilistic inequalities

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 . Then, we have the following bound on the probability that the empirical measure of the samples falls within the set A: , where * is the joint probability distribution on , and * 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 the closure of its interior, (Wikipedia).

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Related pages

Interior (topology) | Large deviations theory | Kullback–Leibler divergence | Information projection | Closure (topology) | Mathematics | Rate function | Typical set | Probability distribution | Empirical measure | Information theory | Independent and identically distributed random variables