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Finite-Alphabet Entropy and Divergence — Examples

1 · Prerequisites

2 · Summary

These worked finite cases verify the binary entropy maximum and endpoint values, the zero information of independent variables, the entropy of a deterministic output, and the direction and support behavior of finite KL divergence. The examples state their probability vectors and calculate each value using the same zero and support conventions as the A page.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: Literature-sourcedVerification: AI-generatedjudge pass (gpt-6-luna)Open item page →

Binary entropy and its extremizers

Example

For a Bernoulli law q=(p,1−p), the entropy in nats is h(p):=H(q)=−pln⁡p−(1−p)ln⁡(1−p),0ln⁡0:=0. It is zero exactly at p=0,1 and has unique maximum ln⁡2 at p=1/2.

Facts & Assumptions

Given: p∈[0,1] and the Bernoulli probability vector q=(p,1−p).

[F1]

Finite distributions, entropy, divergence, and stochastic matrices: H(q)=−∑qi>0qiln⁡qi, with zero terms set to 0.

[F2]

Finite KL divergence is nonnegative with equality exactly at equal laws: for finite probability vectors, D(r∥s)≥0, with equality iff r=s.

Verification

1.1givenF1F3algebra

Substituting q=(p,1−p) into [F1] gives the displayed formula. If p=0 or p=1, the nonzero coordinate is 1 and [F3] gives ln⁡1=0, so h(p)=0.

1.2F1F2givenalgebra

Let u=(1/2,1/2). The finite KL formula gives D(q∥u)=pln⁡(2p)+(1−p)ln⁡(2(1−p))=ln⁡2−h(p), also at p=0,1 because zero terms are omitted. By [F2], this value is nonnegative and vanishes exactly when q=u, i.e. p=1/2. Thus h(p)≤ln⁡2 with a unique maximizer.

2.1F3step 1.1givenalgebra∎

If 0<p<1, then ln⁡p<0 and ln⁡(1−p)<0 by [F3], so both terms in h(p) are positive. Along with step 1.1 this proves that the only zeros are the endpoints.

CounterexampleConstruction: Literature-sourcedVerification: AI-generatedjudge pass (gpt-6-luna)Open item page →

Finite KL divergence is directional and may be infinite

Statement refuted

For every pair of finite probability vectors p,q on the same alphabet, D(p∥q)=D(q∥p).

Facts & Assumptions

Given: The finite KL convention and 0<ε<1.

[F1]

Finite distributions, entropy, divergence, and stochastic matrices: D(p∥q)=∑i:pi>0piln⁡(pi/qi), with +∞ if pi>0=qi for some coordinate.

[F2]

Finite KL divergence is nonnegative with equality exactly at equal laws: finite KL is nonnegative and is zero exactly for equal probability vectors.

Counterexample

Given: Take the alphabet {0,1}, p=δ0, q=(ε,1−ε), and 0<ε<1.

1.1givenF1F2algebra

The support of p is contained in that of q because q0=ε>0. By [F1], D(p∥q)=1⋅ln⁡(1/ε)=−ln⁡ε, a finite real number. Since p≠q, [F2] makes this value strictly positive, consistent with the formula.

2.1givenF1step 1.1cases

The second coordinate has q1=1−ε>0=p1. Thus [F1] gives D(q∥p)=+∞. The two directions are unequal, which refutes the statement.

3.1F1construct∎

For the separate disjoint point masses p=δ0 and q=δ1, coordinate 0 has p0>0=q0 and coordinate 1 has q1>0=p1; applying [F1] in each direction gives D(p∥q)=D(q∥p)=+∞. This records the disjoint-support boundary.

ExampleConstruction: Literature-sourcedVerification: AI-generatedjudge pass (gpt-6-luna)Open item page →

Independent and deterministic finite information

Example

For finite X,Y, independence gives I(X;Y)=0. If Y=f(X) for a function f on the finite alphabet of X, then I(X;Y)=H(Y).

Facts & Assumptions

Given: Finite X,Y with joint pmf pXY; in the second case Y=f(X).

[F1]

Finite mutual information: I(X;Y) is the finite KL divergence between pXY and pX⊗pY.

[F2]

Finite entropy and mutual-information chain rules: I(X;Y)=H(Y)−H(Y∣X).

[F3]

Finite conditional entropy: conditional entropy is the average of the entropies of the conditional pmfs.

[F4]

Finite distributions, entropy, divergence, and stochastic matrices: H(r)=−∑ri>0riln⁡ri, with 0ln⁡0=0.

Verification

1.1givenF1algebra

If X,Y are independent, then pXY(x,y)=pX(x)pY(y) at every coordinate. Each positive-support ratio in [F1] is 1, so every summand is zero and I(X;Y)=0.

2.1givenF2F3F4algebra∎

If Y=f(X), then for each x with pX(x)>0, pY∣X=x has mass 1 at f(x) and 0 elsewhere, so [F4] gives entropy −1ln⁡1=0. Averaging these conditional entropies by [F3] gives H(Y∣X)=0, and [F2] yields I(X;Y)=H(Y).

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