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 be a uniform bit, let be independent of , and set , where . Use the IID source with this one-letter joint law, and put The sloped segment of its Slepian–Wolf lower boundary joins to in nats per source symbol; when this segment collapses to and continues as the horizontal ray for . For 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.
Finite-alphabet IID multisource: an IID two-source is specified by its finite joint one-letter law and its product block laws.
Finite conditional entropy: conditional entropy is the finite weighted sum of the entropies of positive-mass conditional probability vectors.
Finite-IID k-source Slepian–Wolf rate region: the achievable region is given by all nonempty-subset conditional-entropy lower bounds.
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.
Finite distributions, entropy, divergence, and stochastic matrices: finite entropy is , with , and finite KL divergence is directional.
Finite KL divergence is nonnegative with equality exactly at equal laws: for finite probability vectors, with equality only for equal laws.
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: is strictly increasing and .
Verification
The one-letter joint masses are and . Summing rows and columns gives uniform marginals. For either , the conditional law of given has masses and ; the same holds for given . Therefore [F2] gives , including and under the zero-term convention.
Directly summing the four joint entropy terms gives : the two diagonal terms contribute and the two off-diagonal terms contribute , with zero terms omitted when .
Since , the binary entropy satisfies . The lower bound follows termwise from for and [F5], while [F6] applied to and gives . Thus the region from [F3] is , , and . For , the sum constraint gives the boundary , joining the stated endpoints; for , the coordinate bound is the active lower boundary.
If , all four joint masses are positive, so the support bipartite graph in [F4] has every possible edge and is connected. Any common function almost surely must then satisfy , 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
- Finite-IID k-source Slepian–Wolf rate region
- Finite-alphabet IID multisource
- Finite fixed-length multiterminal lossless code
- Deterministic Gács–Körner common part and Wyner common information
- Finite conditional entropy
- Information units and logarithm-base convention
- Finite KL divergence is nonnegative with equality exactly at equal laws
- Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm
- Finite distributions, entropy, divergence, and stochastic matrices
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
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Sources
- Thomas M. Cover and Joy A. Thomas, Elements of Information Theory, second edition, Chapter 15 (standard reference, not scraped)
- Yury Polyanskiy and Yihong Wu, Information Theory: From Coding to Learning, author-hosted book draft (standard reference, not scraped)