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.
Every probability vector is approximated by n-types
Statement
Let be a finite nonempty alphabet of size , and let be any probability vector on . For every , there is an -type such that
Facts & Assumptions
Given: The finite alphabet , its size , a probability vector on , and .
Empirical types, n-types, and type classes: an -type is exactly a probability vector whose coordinates are integer counts divided by , with counts summing to .
Proof
Fix a listing of . Set and . Thus is an integer with . If , at least fractional parts are positive, since fewer than positive terms, each below , cannot sum to . Add to the first positive fractional-part coordinates in the fixed listing; if , add nothing. The resulting integers are nonnegative and sum to . If , then and , so this gives the unique type exactly.
Put . This is an -type by [F1]. Each coordinate changes from by less than : it is rounded down when not selected and rounded up when selected. Summing the coordinate errors gives , hence the stated weak bound. The construction also applies when some .
Depends on
Used by
Dependency tree · two levels
2 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
- Yury Polyanskiy and Yihong Wu, Information Theory: From Coding to Learning, author-hosted book draft (standard reference, not scraped)
- Thomas M. Cover and Joy A. Thomas, Elements of Information Theory, second edition, Chapter 11 (standard reference, not scraped)