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For classical finite random variables, $H(X,Z\mid Y)=H(X\mid Y)+H(Z\mid X,Y)=H(Z\mid Y)+H(X\mid Z,Y)$. Each identity follows by inserting the corresponding joint entropy into $H(X,Z,Y)-H(Y)$. The two orders are particularly useful when one variable is a deterministic function of the others, as in Fano's inequality via an error indicator.

Use logarithms to base two, so all entropies are in bits. For a discrete probability distribution $p$ and a density operator $\rho$ with eigenvalues ${\lambda }_j$, the Shannon entropy and Von Neumann entropy are respectivelyUse $0\log 0=0$. The second definition is basis-independent by the spectral theorem; for a density operator diagonal with probabilities $p_x$, it agrees with the first.

The classical relative entropy, or Kullback-Leibler divergence, of $p$ from $q$ isIt is $+\infty$ if some $p_x>0$ has $q_x=0$. The quantum relative entropy iswhen the support of $\rho$ is contained in that of $\sigma$, and is $+\infty$ otherwise. Matrix logarithms are taken on the corresponding positive supports. In a common eigenbasis this reduces to classical relative entropy; both divergences are nonnegative and are generally asymmetric.

To prove Fano's inequality, let the finite alphabet size be $m\ge 2$ and let $E$ indicate an incorrect guess. Since $E$ is determined by $(X,Y)$, the chain rule for conditional entropy first givesExpanding in the opposite order givesConditioning reduces entropy, so $H(E\mid Y)\le H(E)=h_2(p_e)$, the binary entropy function. When $E=0$, knowledge of $Y$ determines $X=f(Y)$ and the conditional entropy is zero. When $E=1$, $X$ can lie in at most the $m-1$ alphabet values other than $f(Y)$. Entropy on $m-1$ values is at most ${\log }_2(m-1)$: its deficit from that number is the nonnegative relative entropy from the uniform distribution. Averaging the two cases therefore givesCombining the two expansions provesThis proof of Fano's inequality via an error indicator works for any alphabet-valued guessing rule, not just an optimal one. If $m=1$, there is no guessing uncertainty and the claim is simply $H(X\mid Y)=0$; that case is treated separately rather than writing $\log 0$.

Because $Z$ is determined by $X,Y$, its conditional entropy satisfies $H(Z\mid X,Y)=0$. Equate the two forms of the chain rule for conditional entropy to obtainConditioning cannot increase classical Shannon entropy: $H(Z)-H(Z\mid Y)=I(Z:Y)\ge 0$ by nonnegativity of mutual information. Thus $H(Z\mid Y)\le H(Z)=h(p_e)$. When $Z=0$, the value of $X$ is exactly $f(Y)$ and $H(X\mid Y,Z=0)=0$. When $Z=1$ and $Y=y$, the value $f(y)$ is excluded, leaving at most $|J_X|-1$ possibilities. By maximum entropy on a finite alphabet,Averaging the two conditional cases now yieldsand consequentlyThis is Fano's inequality via an error indicator. Events of zero probability contribute zero to the average and need no conditional distribution. For $|J_X|=1$, the inference is automatically correct and the entropy is zero; the displayed logarithmic form is intended for $|J_X|\ge 2$. Optimality of the guess is not needed for the inequality: it holds for every deterministic $f$.