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Skipped the problems because reading on a bus is maybe not the best environment for deep thought. So instead started the next chapter on data compression and got till Kraft inequality. I remember learning about it as a side remark in my introductory TCS class in undergrad. Didn't remember the proof being so clean.

Finished Chapter on the entropy rates of stochastic processes. Very fun and light intro, though I am kinda struggling what I am missing with the first exercise.

Pretty good. The AEP chapter was nice and short. I like the book motivated the entropy rate of a stationary stochastic process too.

Finished Chapter 1 till summary. Was good so far, though I don't have a good intuition for some equalities, such as the chain rule for mutual information. - 23:16, 18.08.2025

Got stuck in the proof of Theorem 2.10.1 (Fano's inequality). I don't have a good feeling how the probability Pr(X \neq \tilde{X}) is random, and the inequalities in 2.135 don't make sense to me yet.

Read up till Section 2.7.

Finally got a feeling for what mutual information and the KL divergence is. I especially liked the interpretation for encoding a random variable X. The KL divergence is the inefficiency, the additional needed when one uses an encoding assuming X follows q(x) but actually the true probability mass function is p(x).

The Venn-Diagram with H(X | Y), H(Y | X), I(X;Y) was helpful too.