Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)
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.

The pCN accept-reject kernel preserves the posterior

Statement

Assume the Axiom of Choice and the inputs of the prior-preserving Crank–Nicolson algorithm: a separable real Hilbert space H, centered Borel Gaussian reference probability μ0, full-mass Borel domain D, Borel potential ΦD:D→(−∞,+∞], positive finite evidence Z, and scale β∈[0,1]. Let μ be the normalized target on H obtained by extending ΦD by +∞ off D, and let Pβ be the algorithm's accept-reject transition. Then Pβ is reversible with respect to μ: its joint flow Λβ(C):=∫HPβ(u,Cu) μ(du),Cu:={v∈H:(u,v)∈C}, is unchanged by (u,v)↦(v,u) for every Borel C⊆H×H. Consequently, μ is invariant: ∫HPβ(u,A) μ(du)=μ(A)(A∈B(H)). This conclusion gives invariance only; it does not assert uniqueness, convergence, mixing, an effective-sample-size bound, mesh-independent cost, or correctness of an approximate forward solver.

Facts & Assumptions

Given: The algorithm inputs in the Statement.

[A1]

The Axiom of Choice and the algorithm's full-mass domain, potential extension, evidence, target density, proposal, and acceptance convention. The Axiom of Choice Prior-preserving Crank–Nicolson sampler

[F1]

The proposal rule Qβ and accept-reject rule Pβ are Borel probability kernels; for each u, Qβ(u,B)=∫H1B(ρu+βz) μ0(dz),Pβ(u,A)=∫Aα(u,v)Qβ(u,dv)+1A(u)(1−a(u)), where a(u)=∫Hα(u,v)Qβ(u,dv). The pCN accept-reject procedure defines a probability kernel

[F2]

If w(u)=e−Φˉ(u), then μ(du)=Z−1w(u)μ0(du), w(u)=0 when Φˉ(u)=+∞, and α(u,v)=0 if either potential is infinite. Prior-preserving Crank–Nicolson sampler Misfit evidence and the potential posterior law

[F3]

For U0,ξ independent with law μ0 and V0=ρU0+βξ, the joint Borel law of (U0,V0) is symmetric under coordinate swap; on the separable Hilbert product, the Borel sigma-algebra equals the product sigma-algebra. The pCN Gaussian reference pair law is symmetric Borel sigma-algebra of a product of second-countable spaces

[F4]

A prior and probability kernel define a joint flow probability measure with the rectangle formula and nonnegative iterated-integral identity. A prior and model kernel define a joint law Measure kernel and probability kernel Probability measures and probability spaces

[F5]

Equality of probability laws is preserved under measurable changes of variables, and integration against a law equals expectation of the corresponding random-element function. Law or distribution of a random element Change of variables for expectation

[F7]

A probability kernel is reversible with respect to a probability measure when its joint flow equals its coordinate transpose; this definition requires no density. General-state reversibility by symmetric flow measure

[F8]

Reversibility with respect to a probability measure implies its invariance. General-state reversibility implies invariance

[F9]

Since H is separable, norm balls centered at a countable dense subset with positive rational radii form a countable base of its induced metric topology: given x∈U with U open, choose r>0 with B(x,r)⊆U, then a dense point d with ∥d−x∥<r/3 and a positive rational s with ∥d−x∥<s<r−∥d−x∥; the triangle inequality gives x∈B(d,s)⊆U. Hence H is second-countable, and the Borel sigma-algebra of H×H equals B(H)⊗B(H). Separability: the existence of an at most countable dense subset Second countability: an at most countable basis for the topology A product of two at most countable sets is at most countable Q is countably infinite The rationals embed densely in the reals Every subset of an at most countable set is at most countable Hilbert space A norm on a real vector space, the induced metric, and the dictionary with the metric axioms Open ball, closed ball and sphere in a metric space The metric topology: a set is open when every one of its points has a ball around it inside the set; closed means open complement Arbitrary unions and finite intersections of open sets are open, open balls are open and closed balls are closed Borel sigma-algebra of a product of second-countable spaces

[F10]

Continuous maps have Borel preimages of Borel sets. A continuous map has Borel preimages of Borel sets

Proof

Given: The hypotheses and notation in the Statement.

1.1A1F1F3F4F5F6givenconstruct

AC is assumed as recorded in [A1]. By [F9], H is second-countable and the Borel sigma-algebra of H×H is the product sigma-algebra. Put ρ=1−β2 and let Λ0(du,dv)=μ0(du)Qβ(u,dv) be the joint flow of the reference probability and proposal kernel. It exists by [F4]. Take the product probability space (H×H,μ0⊗μ0) and its coordinate random elements U0,ξ; [F6] gives their independence and common law μ0. For every nonnegative product-Borel h, the iterated-integral identity in [F4] and [F1] give ∫h dΛ0=∫H∫Hh(u,ρu+βz) μ0(dz) μ0(du)=E[h(U0,ρU0+βξ)], where the proposal-row pushforward and the change-of-variables identity [F5] identify the flow integral with expectation under U0∼μ0, and the product joint law [F6] together with the measurable proposal map identifies that expectation with the displayed one. Thus Λ0 is the law in [F3], so Λ0 is symmetric.

1.2A1F2givenalgebra

Define w(u)=e−Φˉ(u) and m(u,v)=min⁡{w(u),w(v)}. For every (u,v), if both potential values are finite, then w(u)α(u,v)=e−Φˉ(u)min⁡{1,eΦˉ(u)−Φˉ(v)}=min⁡{e−Φˉ(u),e−Φˉ(v)}=m(u,v). If either value is +∞, the algorithm sets α(u,v)=0 and m(u,v)=0, so the identity still holds. In particular, m is a symmetric nonnegative Borel function.

1.3F1F4F10givenalgebra

Set r(u)=1−a(u)∈[0,1]. The diagonal map d(u)=(u,u) is continuous from H into the product topology on H×H, so by [F10] it is Borel. Thus u↦1C(u,u)=1C(d(u)) is measurable for every Borel C⊆H×H. By the diagonal term in [F1] and the joint-flow formula [F4], the rejection part of the target joint flow has mass on C Λrej(C)=∫H1C(u,u)r(u) μ(du). It is symmetric because τ(d(u))=d(u) for every u: for every Borel C, 1τ−1C(u,u)=1C(u,u).

2.1F2F4F5step 1.1step 1.2algebra

For Borel C⊆H×H, the accepted part of the target joint flow is Λacc(C):=∫H∫H1C(u,v)α(u,v)Qβ(u,dv) μ(du)=Z−1∫H×H1C(u,v)m(u,v) Λ0(du,dv). The equality follows from the target density in [F2], identity 1.2, and the iterated-integral formula [F4]. Since Λ0 is symmetric by step 1.1 and m(u,v)=m(v,u), this accepted measure is symmetric. Indeed, for τ(u,v)=(v,u), the change-of-variables identity [F5] gives ∫1C(u,v)m(u,v) dΛ0=∫1C(v,u)m(v,u) dΛ0=∫1τ−1C(u,v)m(u,v) dΛ0. Equality for every Borel C is symmetry under τ.

3.1F1F4F7step 2.1step 1.3given

By the decomposition of Pβ in [F1] and the joint-flow identity [F4], Λβ=Λacc+Λrej. The accepted and rejection pieces are symmetric by steps 2.1 and 1.3, hence Λβ is symmetric. By [F7], Pβ is reversible with respect to μ.

4.1F1F8step 3.1given∎

The hypotheses of [F8] hold: [F1] gives a probability kernel and the algorithm gives the probability measure μ. Therefore [F8] yields ∫HPβ(u,A) μ(du)=μ(A) for every Borel A. The proof uses only the symmetric-flow identity; it establishes no uniqueness or convergence claim.

Depends on

Used by

Dependency tree · two levels

155 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