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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 A be a finite nonempty alphabet of size k, and let q be any probability vector on A. For every n≥1, there is an n-type qn∈Pn(A) such that ∥qn−q∥1:=∑a∈A∣qn(a)−q(a)∣≤kn.

Facts & Assumptions

Given: The finite alphabet A, its size k, a probability vector q on A, and n≥1.

[F1]

Empirical types, n-types, and type classes: an n-type is exactly a probability vector whose coordinates are integer counts divided by n, with counts summing to n.

Proof

1.1givenF1construct

Fix a listing a1,…,ak of A. Set mi=⌊nq(ai)⌋ and r=n−∑imi=∑i(nq(ai)−mi). Thus r is an integer with 0≤r<k. If r>0, at least r fractional parts are positive, since fewer than r positive terms, each below 1, cannot sum to r. Add 1 to the first r positive fractional-part coordinates in the fixed listing; if r=0, add nothing. The resulting integers mi′ are nonnegative and sum to n. If k=1, then q(a1)=1 and r=0, so this gives the unique type exactly.

2.1F1step 1.1algebra∎

Put qn(ai)=mi′/n. This is an n-type by [F1]. Each coordinate changes from q(ai) by less than 1/n: it is rounded down when not selected and rounded up when selected. Summing the k coordinate errors gives ∥qn−q∥1<k/n, hence the stated weak bound. The construction also applies when some q(ai)=0.

Depends on

Used by

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Sources