Entropy bound with one prescribed probability ($H\le h(t)+(1-t){\log }_2(k-1)$)

If one probability in a $k$-point distribution equals $t$, normalize the remaining $k-1$ probabilities to $q$. Then $H=h(t)+(1-t)H(q)\le h(t)+(1-t){\log }_2(k-1)$. Equality for $t<1$ means that the remaining probabilities are equal. This combines the binary entropy of the distinguished event with the largest residual Shannon entropy.