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

Regular conditional law for a finite partition

Example

Let H1,,Hn be a finite measurable partition of a probability space, let X:Ω(E,S) be measurable, and supply a fixed target probability ρ. For ωHj put K(ω,A)={P(Hj{XA})/P(Hj),P(Hj)>0,ρ(A),P(Hj)=0.

This is a regular conditional law of X given σ(H1,,Hn). For example, on Ω={1,2,3,4} take point masses (1/4,1/4,1/2,0), cells {1,2},{3},{4}, and X=(0,1,1,2). With ρ=δ0, the conditional laws on the three cells are respectively (δ0+δ1)/2, δ1, and δ0.

Facts & Assumptions

Given: The hypotheses and conventions in the example.

[F1]

RCDs are probability kernels satisfying all conditioning-event identities. Regular conditional distribution.

[F2]

A probability kernel has pointwise probability sections and measurable evaluations. Measure kernel and probability kernel.

Verification

technique · direct
1.1

For a positive-mass cell, preimages under X preserve disjoint unions, so AP(HjX1(A)) is countably additive, vanishes at the empty set and has total mass P(Hj). Division by this positive finite mass gives a probability. On zero-mass cells the supplied ρ is a probability. For each A the evaluation is constant on every cell and is therefore measurable for the finite partition sigma-algebra. Every event H in that sigma-algebra is a union of cells: the set of such unions is itself a sigma-algebra containing the cells. Thus HK(ω,A)dP=j:HjHP(Hj)KHj(A)=j:HjHP(Hj{XA})=P(H{XA}). Each zero cell contributes zero on both sides, and an empty cell can be ignored. This proves [F1]–[F2].

F1F2
2.1

In the displayed finite model the cell masses are 1/2,1/2,0. On the first cell, P(X=0,H1)=1/4 and P(X=1,H1)=1/4, so the conditional probabilities are 1/2 and 1/2. On the second cell, P(X=1,H2)=1/2 gives probability one at 1. The third uses the specified filler despite X(4)=2, since its entire cell has mass zero. For instance testing A={1} and H=Omega gives (1/2)(1/2)+(1/2)(1)+0(0)=3/4=P(X=1); testing H=H1 gives 1/4 on both sides. Hence the calculated kernels have exactly the claimed values.

step 1.1

Depends on

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Dependency tree · two levels

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Sources