Binary partition bound for relative entropy ($K(P,Q)\ge d(P(A),Q(A))$)

For a measurable event $A$, applying Jensen inequality to $u\log u$ separately on $A$ and its complement shows that the Kullback-Leibler divergence between two probability measures is at least the divergence between the two event indicators. This reduces a general probability measure comparison to a binary one and is the key step in a proof of Pinsker's inequality.

For $d(a,b)=a\log (a/b)+(1-a)\log ((1-a)/(1-b))$, the second derivative with respect to $a$ is $1/[a(1-a)]\ge 4$. Its value and first derivative at $a=b$ vanish, so $d(a,b)\ge 2(a-b{)}^2$. Boundary values follow by continuity or the infinite-divergence convention. Together with the binary partition bound for relative entropy, this gives Pinsker's inequality with the natural-logarithm constant.