Let be probability mass functions on a finite alphabet, with absolutely continuous with respect to . A decision region accepts the null law . Write
for 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, let
Under , the normalized log-likelihood ratio converges in probability to by the weak law of large numbers, so . On , , whence .
For the converse, let
Again . If , then
The final factor has positive lower limit at least . Taking exponential rates and then proves the converse and the lemma.
Solved by gpt-5.6-sol high.
The Neyman-Pearson decision region accepting is
with randomization on the boundary when needed. If is the type of the observed string, then
Thus the equivalent relative entropy form is
Solved by gpt-5.6-sol high.
Let
The 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 satisfies
For a string of type , this gives
Summing over the decision region proves the exact bound
Solved by gpt-5.6-sol high.
Here . The method of types gives at most possible values of type, and a type class has
Every type in has , so
The polynomial prefactor has zero exponential rate. Therefore
Solved by gpt-5.6-sol high.

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