Data processing inequality for f-divergences

For a measurable function $T$, every f-divergence contracts under pushforward:This follows from conditional Jensen inequality applied to the jointly convex perspective $(a,b)\mapsto bf(a/b)$.

This is the data processing inequality for f-divergences specialized to relative entropy. Passing two probability distributions through the same Markov kernel cannot increase their relative entropy. For a kernel $K$,