Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-14
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.

Extreme points of the probability measures are Dirac masses

Statement

For a compact Hausdorff space K, the extreme points of the convex set of regular Borel probability measures on K are exactly the Dirac measures δx with xK.

Facts & Assumptions

Given: A compact Hausdorff space K and its convex set P(K) of regular Borel probability measures.

[F1]

Extreme points are exactly points whose strict two-term convex decompositions are trivial (Extreme point and face).

[F2]

Every Dirac set function is a probability measure (A Dirac set function is a probability measure).

[F3]

Restricting a measure to a measurable set produces a measure (The restriction of a measure to a measurable set is a measure).

[F4]

A regular Borel measure is inner regular on every Borel set (Regular Borel measure on an LCH space).

[F5]

A closed family with the finite-intersection property has nonempty intersection in a compact space (A space is compact exactly when every family of closed subsets with the finite intersection property has nonempty intersection).

Proof

technique · direct characterization by measurable restrictions
1.1

If K=, then P(K)= and there are no Dirac measures, so the equality is empty on both sides. Hence assume K. By [F7], compactness makes K locally compact, and it is Hausdorff by hypothesis, so the LCH regularity convention [F4] applies.

F4F7given
1.2

Let μP(K) and let A be Borel. The restriction μA(E)=μ(EA) is a measure by [F3]. It is regular: for Borel E, inner regularity of μ on EA gives μA(E)=sup{μ(D):DEA, D compact}; every such D is also a compact subset of E with μA(D)=μ(D), while every compact CE satisfies μA(C)μA(E). Thus the required supremum over compact CE equals μA(E).

F3F4given
1.3

Suppose that μ(E){0,1} for every Borel E. Let C be all closed CK with μ(C)=1. It contains K. A finite intersection of its members has measure one because the complement is a finite union of null sets, so C has the finite-intersection property. By [F5], choose xC.

F5given
1.4

Conversely, fix any xK. By [F2], δx is a probability measure. The Hausdorff hypothesis makes the compact singleton {x} closed and hence Borel. Thus δx is regular: for a Borel set containing x, that singleton realizes mass one, while a set not containing x has mass zero. If δx=tμ+(1t)ν with μ,νP(K) and 0<t<1, evaluation on K{x} gives 0=tμ(K{x})+(1t)ν(K{x}), so nonnegativity gives both masses zero. Thus μ({x})=ν({x})=1 and μ=ν=δx; [F1] makes δx extreme.

F1F2given
2.1

If 0<t:=μ(A)<1, define μ1=t1μA and μ2=(1t)1μKA. Step 1.2 makes both regular probabilities, and μ=tμ1+(1t)μ2. They are distinct because μ1(A)=1 and μ2(A)=0, so [F1] shows that μ is not extreme.

F1step 1.2
2.2

Every open neighborhood U of the point x from step 1.3 has measure one. Otherwise the zero-one hypothesis gives μ(U)=0, so the closed complement KU has measure one and belongs to C, contradicting xUC.

step 1.3
3.1

If DK{x} is compact, [F6] gives disjoint open sets U,V with xU and DV. Step 2.2 gives μ(U)=1, hence μ(V)=0 and μ(D)=0. Inner regularity [F4] on the Borel set K{x} now gives μ(K{x})=0, so μ=δx.

F4F6step 2.2
4.1

Let μ be extreme. Step 2.1 rules out every Borel set of intermediate mass, so μ is zero-one valued; steps 1.3, 2.2, and 3.1 then give μ=δx for some xK. Step 1.4 proves the reverse implication, and step 1.1 covers the empty space.

step 1.1step 1.3step 1.4step 2.1step 2.2step 3.1

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

37 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