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.
Finite-alphabet IID source
Definition
Let be a nonempty finite alphabet identified with , and let be a probability vector in the sense of Finite distributions, entropy, divergence, and stochastic matrices. A finite-alphabet IID source consists of random elements , , on one probability space, which are independent and identically distributed in the sense of Identical distribution and IID families and have common law . For every , the finite vector then has probability for each , with as in Self-information and surprisal variance for a finite law.
Depends on
Used by
- Deterministic fixed-length binary block code and block error Definition
- Finite-alphabet IID multisource Definition
- Information density and typical sets for a finite source Definition
- Bernoulli information density and exact typical-set counts Example
- Finite-alphabet IID information AEP with a variance bound Lemma
- Joint typicality probability tends to one for a finite IID multisource Lemma
- Fixed-length lossless source coding at the entropy rate Theorem
Dependency tree · two levels
7 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
- Thomas M. Cover and Joy A. Thomas, Elements of Information Theory, 2nd ed., Chapter 3 (standard reference, not scraped)
- MIT OpenCourseWare 6.441 Information Theory, Spring 2010, Lecture 3 (standard reference, not scraped)