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As we all know, formula for mutual information is as follows:

$ I(X, Y) = H(Y) - H(Y|X) $

In case of Binary Symmetric Channel with flip probability equal to $f$ and both alphabets of size $2$:

$ I(X, Y) = H(Y) - H(Y|X) = log2 - H(f) = 1 - H(f) $

I'm confused about the equality $H(Y) = log2$ a little bit. Is it always the case that entropy of output is equal to $log$ of size of alphabet? If not, could you give me an example of such a channel?

Thank you

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It is not always true. However, the entropy of a discrete random variable $Y$ on a finite alphabet $\mathcal{Y}$ is always upper bounded by $\log |\mathcal{Y}|$: this is because the uniform distribution on $\mathcal{Y}$ maximizes the entropy.

So for the mutual information, because

$$I(X; Y) = H(Y) - H(Y | X)$$

the first term is maximized when $Y$ is uniform. Luckily, for the BSC we can choose $X$ to be uniform, which will also make $Y$ uniform. Since the distribution of $Y$ given $X$ is just $\mathrm{Bernoulli}(f)$, the second term doesn't depend on the distribution of $X$ at all. Therefore we can just choose the distribution of $X$ to maximize $H(Y)$.

It's not true in general that we can make $H(Y) = \log |\mathcal{Y}|$ in order to maximize the mutual information: sometimes that will also make $H(Y | X)$ large so the difference is smaller than it would be with another choice of $X$.

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