Past exam of the information theory course of the University of Oxford / 2021 / 1 / b / i

For probability mass functions on the finite alphabet, take logarithms to base two and define the relative entropy byA term with $q_i=0$ is zero, including $p_i=0$; a term with $q_i>0=p_i$ is $+\infty$. Fix $p_i>0$ and write $f_i(t)=t{\log }_2(t/p_i)$. For $t>0$,Thus $f_i$ is a convex function on $[0,\infty )$ by continuous extension to zero. For $0\le \lambda \le 1$, applying this scalar convexity to each coordinate and summing provesIf $p$ has zero coordinates, the finite-valued domain consists of probability mass functions supported on $\{i:p_i>0\}$; it is convex and the same proof applies there. Outside that domain the right side is infinite whenever an offending probability mass function has a positive mixture weight, so the extended-valued convexity assertion still holds. At mixture weights zero or one the inequality is an identity, without needing the expression $0\cdot \infty$.