Entropy of convolution of measures Let $G$ be a countable, discrete group, and let $\mu_1,\mu_2$ be probability measures on the group $G$. We define the entropy of $\mu_i$ as
$H(\mu_i)=\sum\limits_{g \in G}-\mu_i(g)\log(\mu_i(g))$ (with the convention that $0 \cdot \log0=0$).
Recall that convolution of $\mu_1$ and $\mu_2$ is defined as:
$(\mu_1 \star \mu_2)(g)=\sum\limits_{h \in G}\mu_1(gh^{-1})\mu_2(h)$.
Now, it's a fact that $H(\mu_1 \star \mu_2) \leq H(\mu_1)+H(\mu_2)$. The usual proof goes by showing that entropy decreases under factor maps. 
My question is: Is there a completely hands on proof of this fact- preferably using the concavity of $\log x$ or convexity of $-x\log x$?
 A: With no convexity... If $P$ and $Q$ are probability measures and $R=P\ast Q$, then $$H(R)=-\sum_xR(x)\log R(x)=-\sum_{x,y}P(y)Q(x-y)\log R(x).$$ For every $(x,y)$, $R(x)\geqslant P(y)Q(x-y)$, hence $$H(R)\leqslant-\sum_{x,y}P(y)Q(x-y)\log(P(y)Q(x-y))=-\sum_{x,y}P(y)Q(x)\log(P(y)Q(x)),$$
where the last identity holds because, for every $y$, the mapping $x\mapsto x-y$ is a bijection from $G$ to $G$. Thus, $$H(R)\leqslant-\sum_{y}P(y)\log(P(y))\left(\sum_xQ(x)\right)-\sum_{x}Q(x)\log(Q(x))\left(\sum_yP(y)\right).$$ Finally, $P$ and $Q$ are probability measures hence the two sums between parentheses are equal to $1$ and we are left with the inequality $$H(R)\leqslant-\sum_{y}P(y)\log(P(y))-\sum_{x}Q(x)\log(Q(x))=H(P)+H(Q).$$
A: You can use the convexity of $\phi(x) = -x\log x$ to show this inequality directly. First
$$\phi\left(\sum_i x_i\right) \leq \sum_i \phi(x_i)$$
We have
$$H(\mu_1 \star \mu_2) = \sum_{g\in G} \phi((\mu_1 \star \mu_2)(g)) \\= \sum_{g\in G} \phi\left(\sum_{h \in G}\mu_1(gh^{-1})\mu_2(h)\right)$$
$$\leq \sum_{g,h\in G} \phi\left(\mu_1(gh^{-1})\mu_2(h)\right) = S$$
Here we need to calculate the sum S explicitly
$$S = \sum_{g,h\in G} -\mu_1(gh^{-1})\mu_2(h)\log(\mu_1(gh^{-1})\mu_2(h))$$
$$= \sum_{g,h\in G} -\mu_1(gh^{-1})\mu_2(h)\log(\mu_1(gh^{-1}))+\sum_{g,h\in G}-\mu_1(gh^{-1})\mu_2(h)\log(\mu_2(h))$$
In the summation we can let $g = g'h$ and sum over $g'$ instead
$$S = \sum_{g',h\in G} -\mu_1(g')\mu_2(h)\log(\mu_1(g'))+\sum_{g',h\in G}-\mu_1(g')\mu_2(h)\log(\mu_2(h))$$
Summing over h first in the first sum and over g first in the second one we have
$$S =\sum_{h\in G}\mu_2(h)\sum_{g'\in G} -\mu_1(g')\log(\mu_1(g'))+\sum_{g'\in G}\mu_1(g')\sum_{h\in G}-\mu_2(h)\log(\mu_2(h))$$
$$ =\sum_{g'\in G} -\mu_1(g')\log(\mu_1(g'))+\sum_{h\in G}-\mu_2(h)\log(\mu_2(h)) $$
$$= H(\mu_1)+H(\mu_2)$$
and we are done.
A: Knowing that the convolution of densities correspond to the sum of independent variables, letting $Z=X+Y$ , and using the chain rule: $H(Z, Y ) = H( Z) + H(Y|Z)=H(Y)+H(Z|Y) $ we have
$$
\begin{array}{rclllll}
H(Z) &=& H(Z|Y)&+&H(Y)&-&H(Y|Z)  \\ 
&=&H(X)&+& H(Y) &-&H(Y|Z)\\ 
&\le&H(X)&+& H(Y) &&\\
\end{array}
$$
For the second row we've used $H(Z|Y)=H(X+Y|Y)=H(X|Y)=H(X)$ where the last equality follows from the independence of $X,Y$, and the previous from $H(X+a)=H(X)$
(Notice that here I'm using a different notation than in the question, in $H(X)$ $X$ is the random variable, not the probability measure; that's why $H(\mu_1 \star \mu_2)$ in your notation corresponds to $H(X+Y)=H(Z)$ here.)
BTW: This also says that the equality holds only if $H(Y|Z)=0$, that is, when the sum is one-to-one, when knowing the  sum you can know the summands.
