Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-10
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.

Quantile coupling on the real line

Example

Assume AC. If real probability laws μnμ have CDFs Fn,F and generalized inverses Qn(u)=inf{x:Fn(x)u}, Q(u)=inf{x:F(x)u} for 0<u<1, then on Borel Lebesgue probability (0,1), Qn and Q have those laws and QnQ almost surely.

Facts & Assumptions

[F1]

Probability laws correspond to distribution functions: Assume the Axiom of Countable Choice.

  1. Let X be a real random variable, let PX be its law, and let FX(x)=P(Xx). Then FX is nondecreasing and right-continuous, satisfies limxFX(x)=0,limx+FX(x)=1, and obeys PX((a,b])=FX(b)FX(a)(a<b).
  2. Conversely, if F:RR is nondecreasing and right-continuous with limxF(x)=0,limx+F(x)=1, then there is a unique Borel probability measure μ on R such that μ((a,b])=F(b)F(a)(a<b), equivalently F(x)=μ((,x])(xR).
[F2]

A box in Rn with parameters aibi is Lebesgue measurable of measure i<n(biai), whichever of its faces are included: Let n1, assume the Axiom of Countable Choice (def-countable-choice), and let aibi be reals for i<n. Write

R:={xRn:ai<xi<bi for every i<n},R:=[a,b]={xRn:aixibi for every i<n}

(def-multidimensional-rectangle-and-volume). Then R is open and R is closed, so both are Borel and Lebesgue measurable, and every set R with RRR is Lebesgue measurable with

λn(R)  =  i<n(biai).

In particular this covers the four one-dimensional face conventions in each coordinate — the open box, the closed box [a,b], the half-open box B(a,b)=i<n(ai,bi] of def-half-open-box, and every mixture of them, in any combination of coordinates — and it gives measure 0 to all of them whenever ai=bi for some i<n. For a half-open box with infinite parameters the value is already λn(B)=vol(B) (thm-lebesgue-measure-is-a-complete-measure).

D  :=  {cI:f is discontinuous at c}

(def-classification-of-discontinuities) is at most countable (def-countable).

More precisely, the proof exhibits an injection J:DN (def-injection-surjection-bijection) built from one fixed enumeration of the rationals: at a discontinuity c interior to I the value J(c) is read off the least index of a rational lying in the gap (limxcf(x), limxc+f(x)), which is a nonempty open interval by thm-monotone-discontinuities-are-jumps. The map J is therefore determined by f and by the fixed enumeration, and no choice principle is used: least indices are canonical by thm-well-ordering-principle, and nothing anywhere in the proof is selected without being determined.

[F4]

Portmanteau theorem: For Borel probabilities μn,μ on a metric space S, the following are equivalent: (i) μnμ; (ii) integrals converge for all bounded uniformly continuous real tests; (iii) lim supnμn(F)μ(F) for every closed F; (iv) lim infnμn(G)μ(G) for every open G; (v) μn(A)μ(A) for every Borel A with μ(A)=0.

[F5]

Every at most countable subset of Rn is Lebesgue null; in particular λ1(Q)=0: Let n1 and assume the Axiom of Countable Choice (def-countable-choice). Every at most countable subset ERn (def-countable) is Lebesgue measurable with

λn(E)  =  0,

so E is a λn-null set (def-measure-null-set-and-almost-everywhere). In particular every singleton is null, and on the real line the set QR of rational reals (lem-rat-embeds-dense) satisfies λ1(QR)=0.

Verification

Given: The objects, hypotheses and definitions in the statement. Its conclusions are to be established below.

1.1

AC implies CC by restriction to any countable family. F1 gives right-continuity, monotonicity and the CDF endpoint limits. For 0<u<1, the set defining Q(u) is nonempty and bounded below by those limits. For any x, if u<=F(x) then Q(u)<=x. Conversely if Q(u)<=x, for each h>0 the infimum property supplies y<x+h with F(y)>=u; thus F(x+h)>=u and right-continuity gives F(x)>=u. Therefore {u:Q(u)x}={u:uF(x)}, and the same holds for Qn.

F1
2.1

The sublevel identity in step 1.1 proves measurability. F2 gives the length of (0,F(x)](0,1) as F(x), including values zero and one. Thus the CDF of Q on this probability interval equals F; the uniqueness clause of F1 identifies its law as μ, and likewise for every Qn.

F1F2step 1.1
2.2

Fix a continuity point u of the nondecreasing Q and ε>0. Choose v with u<v<1 and Q(v)<Q(u)+ε, using continuity at u. Choose a continuity point a of F between Q(u)-ε and Q(u), and a continuity point b of F between max(Q(u),Q(v)) and Q(u)+ε. Such choices exist because a monotone CDF has only countably many discontinuities by F3. Step 1.1 gives F(a)<u and F(b)>=v>u. F4 at the half-lines with endpoints a,b gives Fn(a)->F(a) and Fn(b)->F(b). Eventually Fn(a)<u<Fn(b), whence a<Qn(u)b by step 1.1. Thus Qn(u)Q(u)<ε eventually.

F3F4step 1.1
3.1

Q is nondecreasing, since increasing u shrinks the defining set. F3 makes its discontinuity set on (0,1) countable. F5, under CC from step 1.1, makes this set null. Step 2.2 proves convergence elsewhere, so the coupling has the stated almost-sure limit. The excluded u=0,1 need no inverse values.

F3F5step 1.1

Depends on

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Sources