Let be probability mass functions on a finite alphabet, with absolutely continuous with respect to . A decision region accepts the null law . Writefor its type-I and type-II errors. Stein's lemma states that for every fixed ,where logarithms and relative entropy use base two.
For achievability, letUnder , the normalized log-likelihood ratio converges in probability to by the weak law of large numbers, so . On , , whence .
For the converse, letAgain . If , thenThe final factor has positive lower limit at least . Taking exponential rates and then proves the converse and the lemma.
The Neyman-Pearson decision region accepting iswith randomization on the boundary when needed. If is the type of the observed string, thenThus the equivalent relative entropy form is
LetThe minimum exists because the probability simplex is compact. Let be the information projection of onto the closed convex set . Its Pythagorean inequality says that every satisfiesFor a string of type , this givesSumming over the decision region proves the exact bound
Here . The method of types gives at most possible values of type, and a type class hasEvery type in has , soThe polynomial prefactor has zero exponential rate. Therefore
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