# Inf-entropy rate and min-entropy

I am reading the paper "Generating random bits from an arbitrary source: fundamental limits" by Vembu and Verdu. This paper is written in the language of information theory, however, I need to translate one quantity in this paper in terms of computer science.

inf-entropy rate, $\underline{H}(X)$, of an arbitrary source is defined (Definition 3 in the paper) as the largest real number $\alpha$ (including $\infty$) that satisfies $$\lim_{n\to\infty}P\{x^n:~\frac{1}{n}\log\frac{1}{P_{X^n}(x^n)}\leq \alpha-\epsilon\}=0,$$ for all $\epsilon>0$.

Comparing this quantity with the definition of min-entropy, we can say that for i.i.d sources $\underline{H}(X)=H_{\infty}(X)$.

On the other hand, it is obvious that for all stationary and ergodic sources, the Shannon-McMillan theorem leads us to conclude that $\underline{H}(X)=\lim_{n\to\infty}\frac{1}{n}H(X^n)$ as mentioned in expression (2) in the paper.

Since i.i.d sources are a special class of stationary and ergodic sources for which $\lim_{n\to\infty}\frac{1}{n}H(X^n)=H(X)$, from above, we conclude that for any i.i.d sources, $H_{\infty}(X)=H(X)$ which is clearly not true in general. [This is only true when the source is assumed to be uniform too]. So there must be a mistake somewhere in the above argument. Could you please let me know where is the mistake?