Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedprecheck pass
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Binary symmetric source: Slepian–Wolf corners and common part

Example

Let X be a uniform bit, let N∼Bernoulli⁡(p) be independent of X, and set Y=X⊕N, where 0≤p≤1/2. Use the IID source with this one-letter joint law, and put h(p):=−pln⁡p−(1−p)ln⁡(1−p),0ln⁡0:=0. The sloped segment of its Slepian–Wolf lower boundary joins (h(p),ln⁡2) to (ln⁡2,h(p)) in nats per source symbol; when p=1/2 this segment collapses to (ln⁡2,ln⁡2) and continues as the horizontal ray R2=ln⁡2 for R1≥ln⁡2. For p>0 the deterministic Gács–Körner common part is constant. This example does not compute Wyner common information.

Facts & Assumptions

Given: The binary source model and parameter range in the Example.

[F1]

Finite-alphabet IID multisource: an IID two-source is specified by its finite joint one-letter law and its product block laws.

[F2]

Finite conditional entropy: conditional entropy is the finite weighted sum of the entropies of positive-mass conditional probability vectors.

[F3]

Finite-IID k-source Slepian–Wolf rate region: the achievable region is given by all nonempty-subset conditional-entropy lower bounds.

[F4]

Deterministic Gács–Körner common part and Wyner common information: the deterministic Gács–Körner common part is the connected-component label of the joint-support bipartite graph; Wyner common information is a separate auxiliary-variable infimum.

[F5]

Finite distributions, entropy, divergence, and stochastic matrices: finite entropy is −∑qi>0qiln⁡qi, with 0ln⁡0=0, and finite KL divergence is directional.

[F6]

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

Verification

1.1F1F2F5algebra

The one-letter joint masses are P(0,0)=P(1,1)=(1−p)/2 and P(0,1)=P(1,0)=p/2. Summing rows and columns gives uniform marginals. For either y, the conditional law of X given Y=y has masses 1−p and p; the same holds for Y given X=x. Therefore [F2] gives HP(X∣Y)=HP(Y∣X)=h(p), including p=0 and p=1/2 under the zero-term convention.

2.1F5step 1.1algebra

Directly summing the four joint entropy terms gives HP(X,Y)=ln⁡2+h(p): the two diagonal terms contribute −(1−p)ln⁡((1−p)/2) and the two off-diagonal terms contribute −pln⁡(p/2), with zero terms omitted when p=0.

3.1F3F5F6F7step 1.1step 2.1algebra

Since 0≤p≤1/2, the binary entropy satisfies 0≤h(p)≤ln⁡2. The lower bound follows termwise from ln⁡t≤0 for 0<t≤1 and [F5], while [F6] applied to (p,1−p) and (1/2,1/2) gives ln⁡2−h(p)≥0. Thus the region from [F3] is R1≥h(p), R2≥h(p), and R1+R2≥ln⁡2+h(p). For h(p)≤R1≤ln⁡2, the sum constraint gives the boundary R2=ln⁡2+h(p)−R1, joining the stated endpoints; for R1≥ln⁡2, the coordinate bound R2≥h(p) is the active lower boundary.

4.1F1F4step 1.1algebra∎

If p>0, all four joint masses are positive, so the support bipartite graph in [F4] has every possible edge and is connected. Any common function K=f(X)=g(Y) almost surely must then satisfy f(0)=g(0)=f(1)=g(1), since each of the four pairs has positive probability. Hence the common part is constant and has entropy zero.

Remarks

The Wyner quantity in [F4] is defined by a different optimization; its value is not asserted here.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

25 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources